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librarian-bots
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model_cards_with_metadata_test

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TheBloke/Llama-2-7B-GPTQ
TheBloke
"2023-09-27T12:44:46Z"
23,778
78
transformers
[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-2", "en", "arxiv:2307.09288", "base_model:meta-llama/Llama-2-7b-hf", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]
text-generation
"2023-07-18T17:06:01Z"
--- language: - en license: llama2 tags: - facebook - meta - pytorch - llama - llama-2 model_name: Llama 2 7B base_model: meta-llama/Llama-2-7b-hf inference: false model_creator: Meta model_type: llama pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Llama 2 7B - GPTQ - Model creator: [Meta](https://huggingface.co/meta-llama) - Original model: [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b-hf) <!-- description start --> ## Description This repo contains GPTQ model files for [Meta's Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b-hf). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Llama-2-7B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-7B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama-2-7B-GGUF) * [Meta's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/meta-llama/Llama-2-7b-hf) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: None ``` {prompt} ``` <!-- prompt-template end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc | | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- | | [main](https://huggingface.co/TheBloke/Llama-2-7B-GPTQ/tree/main) | 4 | 128 | No | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 3.90 GB | Yes | 4-bit, without Act Order and group size 128g. | | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Llama-2-7B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.28 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Llama-2-7B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.02 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. | | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Llama-2-7B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 3.90 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download from branches - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Llama-2-7B-GPTQ:main` - With Git, you can clone a branch with: ``` git clone --single-branch --branch main https://huggingface.co/TheBloke/Llama-2-7B-GPTQ ``` - In Python Transformers code, the branch is the `revision` parameter; see below. <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/Llama-2-7B-GPTQ`. - To download from a specific branch, enter for example `TheBloke/Llama-2-7B-GPTQ:main` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `Llama-2-7B-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers>=4.32.0 optimum>=1.12.0 pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ pip3 install . ``` ### For CodeLlama models only: you must use Transformers 4.33.0 or later. If 4.33.0 is not yet released when you read this, you will need to install Transformers from source: ```shell pip3 uninstall -y transformers pip3 install git+https://github.com/huggingface/transformers.git ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/Llama-2-7B-GPTQ" # To use a different branch, change revision # For example: revision="main" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=True, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI). [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Meta's Llama 2 7B # **Llama 2** Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details *Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.* Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. **Model Developers** Meta **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. ||Training Data|Params|Content Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| |Llama 2|*A new mix of publicly available online data*|7B|4k|&#10007;|2.0T|3.0 x 10<sup>-4</sup>| |Llama 2|*A new mix of publicly available online data*|13B|4k|&#10007;|2.0T|3.0 x 10<sup>-4</sup>| |Llama 2|*A new mix of publicly available online data*|70B|4k|&#10004;|2.0T|1.5 x 10<sup>-4</sup>| *Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. **Model Dates** Llama 2 was trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). **Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)| |---|---|---|---| |Llama 2 7B|184320|400|31.22| |Llama 2 13B|368640|400|62.44| |Llama 2 70B|1720320|400|291.42| |Total|3311616||539.00| **CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval| |---|---|---|---|---|---|---|---|---|---| |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9| |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9| |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7| |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6| |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3| |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1| |Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**| **Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. |||TruthfulQA|Toxigen| |---|---|---|---| |Llama 1|7B|27.42|23.00| |Llama 1|13B|41.74|23.08| |Llama 1|33B|44.19|22.57| |Llama 1|65B|48.71|21.77| |Llama 2|7B|33.29|**21.25**| |Llama 2|13B|41.86|26.10| |Llama 2|70B|**50.18**|24.60| **Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). |||TruthfulQA|Toxigen| |---|---|---|---| |Llama-2-Chat|7B|57.04|**0.00**| |Llama-2-Chat|13B|62.18|**0.00**| |Llama-2-Chat|70B|**64.14**|0.01| **Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index |Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf| |---|---|---|---|---| |7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)| |13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)| |70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF
bartowski
"2024-07-02T18:05:03Z"
23,778
17
null
[ "gguf", "text-generation", "en", "dataset:openbmb/UltraFeedback", "license:apache-2.0", "region:us" ]
text-generation
"2024-06-30T17:29:24Z"
--- license: apache-2.0 datasets: - openbmb/UltraFeedback language: - en pipeline_tag: text-generation quantized_by: bartowski --- ## Llamacpp imatrix Quantizations of Gemma-2-9B-It-SPPO-Iter3 Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3278">b3278</a> for quantization. Original model: https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3 All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) Experimental quants are made with `--output-tensor-type f16 --token-embedding-type f16` per [ZeroWw](https://huggingface.co/ZeroWw)'s suggestion, please provide any feedback on quality differences you spot. ## Prompt format ``` <start_of_turn>user {prompt}<end_of_turn> <start_of_turn>model ``` Note that this model does not support a System prompt. ## Download a file (not the whole branch) from below: | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [Gemma-2-9B-It-SPPO-Iter3-Q8_0_L.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-Q8_1.gguf) | Q8_0_L | 10.68GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Extremely high quality, generally unneeded but max available quant. | | [Gemma-2-9B-It-SPPO-Iter3-Q8_0.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-Q8_0.gguf) | Q8_0 | 9.82GB | Extremely high quality, generally unneeded but max available quant. | | [Gemma-2-9B-It-SPPO-Iter3-Q6_K_L.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-Q6_K_L.gguf) | Q6_K_L | 8.67GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Very high quality, near perfect, *recommended*. | | [Gemma-2-9B-It-SPPO-Iter3-Q6_K.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-Q6_K.gguf) | Q6_K | 7.58GB | Very high quality, near perfect, *recommended*. | | [Gemma-2-9B-It-SPPO-Iter3-Q5_K_L.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-Q5_K_L.gguf) | Q5_K_L | 7.72GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. High quality, *recommended*. | | [Gemma-2-9B-It-SPPO-Iter3-Q5_K_M.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-Q5_K_M.gguf) | Q5_K_M | 6.64GB | High quality, *recommended*. | | [Gemma-2-9B-It-SPPO-Iter3-Q5_K_S.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-Q5_K_S.gguf) | Q5_K_S | 6.48GB | High quality, *recommended*. | | [Gemma-2-9B-It-SPPO-Iter3-Q4_K_L.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-Q4_K_L.gguf) | Q4_K_L | 6.84GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Good quality, uses about 4.83 bits per weight, *recommended*. | | [Gemma-2-9B-It-SPPO-Iter3-Q4_K_M.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-Q4_K_M.gguf) | Q4_K_M | 5.76GB | Good quality, uses about 4.83 bits per weight, *recommended*. | | [Gemma-2-9B-It-SPPO-Iter3-Q4_K_S.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-Q4_K_S.gguf) | Q4_K_S | 5.47GB | Slightly lower quality with more space savings, *recommended*. | | [Gemma-2-9B-It-SPPO-Iter3-IQ4_XS.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-IQ4_XS.gguf) | IQ4_XS | 5.18GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. | | [Gemma-2-9B-It-SPPO-Iter3-Q3_K_XL.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-Q3_K_XL.gguf) | Q3_K_XL | 6.21GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Lower quality but usable, good for low RAM availability. | | [Gemma-2-9B-It-SPPO-Iter3-Q3_K_L.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-Q3_K_L.gguf) | Q3_K_L | 5.13GB | Lower quality but usable, good for low RAM availability. | | [Gemma-2-9B-It-SPPO-Iter3-Q3_K_M.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-Q3_K_M.gguf) | Q3_K_M | 4.76GB | Even lower quality. | | [Gemma-2-9B-It-SPPO-Iter3-IQ3_M.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-IQ3_M.gguf) | IQ3_M | 4.49GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. | | [Gemma-2-9B-It-SPPO-Iter3-Q3_K_S.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-Q3_K_S.gguf) | Q3_K_S | 4.33GB | Low quality, not recommended. | | [Gemma-2-9B-It-SPPO-Iter3-IQ3_XS.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-IQ3_XS.gguf) | IQ3_XS | 4.14GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. | | [Gemma-2-9B-It-SPPO-Iter3-IQ3_XXS.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-IQ3_XXS.gguf) | IQ3_XXS | 3.79GB | Lower quality, new method with decent performance, comparable to Q3 quants. | | [Gemma-2-9B-It-SPPO-Iter3-Q2_K.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-Q2_K.gguf) | Q2_K | 3.80GB | Very low quality but surprisingly usable. | | [Gemma-2-9B-It-SPPO-Iter3-IQ2_M.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-IQ2_M.gguf) | IQ2_M | 3.43GB | Very low quality, uses SOTA techniques to also be surprisingly usable. | | [Gemma-2-9B-It-SPPO-Iter3-IQ2_S.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-IQ2_S.gguf) | IQ2_S | 3.21GB | Very low quality, uses SOTA techniques to be usable. | | [Gemma-2-9B-It-SPPO-Iter3-IQ2_XS.gguf](https://huggingface.co/bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3-IQ2_XS.gguf) | IQ2_XS | 3.06GB | Very low quality, uses SOTA techniques to be usable. | ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF --include "Gemma-2-9B-It-SPPO-Iter3-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/Gemma-2-9B-It-SPPO-Iter3-GGUF --include "Gemma-2-9B-It-SPPO-Iter3-Q8_0.gguf/*" --local-dir Gemma-2-9B-It-SPPO-Iter3-Q8_0 ``` You can either specify a new local-dir (Gemma-2-9B-It-SPPO-Iter3-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
TurkuNLP/bert-base-finnish-cased-v1
TurkuNLP
"2024-02-20T11:56:47Z"
23,762
5
transformers
[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "fi", "arxiv:1912.07076", "arxiv:1908.04212", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
"2022-03-02T23:29:05Z"
--- language: fi --- ## Quickstart **Release 1.0** (November 25, 2019) We generally recommend the use of the cased model. Paper presenting Finnish BERT: [arXiv:1912.07076](https://arxiv.org/abs/1912.07076) ## What's this? A version of Google's [BERT](https://github.com/google-research/bert) deep transfer learning model for Finnish. The model can be fine-tuned to achieve state-of-the-art results for various Finnish natural language processing tasks. FinBERT features a custom 50,000 wordpiece vocabulary that has much better coverage of Finnish words than e.g. the previously released [multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md) models from Google: | Vocabulary | Example | |------------|---------| | FinBERT | Suomessa vaihtuu kesän aikana sekä pääministeri että valtiovarain ##ministeri . | | Multilingual BERT | Suomessa vai ##htuu kes ##än aikana sekä p ##ää ##minister ##i että valt ##io ##vara ##in ##minister ##i . | FinBERT has been pre-trained for 1 million steps on over 3 billion tokens (24B characters) of Finnish text drawn from news, online discussion, and internet crawls. By contrast, Multilingual BERT was trained on Wikipedia texts, where the Finnish Wikipedia text is approximately 3% of the amount used to train FinBERT. These features allow FinBERT to outperform not only Multilingual BERT but also all previously proposed models when fine-tuned for Finnish natural language processing tasks. ## Results ### Document classification ![learning curves for Yle and Ylilauta document classification](https://raw.githubusercontent.com/TurkuNLP/FinBERT/master/img/yle-ylilauta-curves.png) FinBERT outperforms multilingual BERT (M-BERT) on document classification over a range of training set sizes on the Yle news (left) and Ylilauta online discussion (right) corpora. (Baseline classification performance with [FastText](https://fasttext.cc/) included for reference.) [[code](https://github.com/spyysalo/finbert-text-classification)][[Yle data](https://github.com/spyysalo/yle-corpus)] [[Ylilauta data](https://github.com/spyysalo/ylilauta-corpus)] ### Named Entity Recognition Evaluation on FiNER corpus ([Ruokolainen et al 2019](https://arxiv.org/abs/1908.04212)) | Model | Accuracy | |--------------------|----------| | **FinBERT** | **92.40%** | | Multilingual BERT | 90.29% | | [FiNER-tagger](https://github.com/Traubert/FiNer-rules) (rule-based) | 86.82% | (FiNER tagger results from [Ruokolainen et al. 2019](https://arxiv.org/pdf/1908.04212.pdf)) [[code](https://github.com/jouniluoma/keras-bert-ner)][[data](https://github.com/mpsilfve/finer-data)] ### Part of speech tagging Evaluation on three Finnish corpora annotated with [Universal Dependencies](https://universaldependencies.org/) part-of-speech tags: the Turku Dependency Treebank (TDT), FinnTreeBank (FTB), and Parallel UD treebank (PUD) | Model | TDT | FTB | PUD | |-------------------|-------------|-------------|-------------| | **FinBERT** | **98.23%** | **98.39%** | **98.08%** | | Multilingual BERT | 96.97% | 95.87% | 97.58% | [[code](https://github.com/spyysalo/bert-pos)][[data](http://hdl.handle.net/11234/1-2837)] ## Previous releases ### Release 0.2 **October 24, 2019** Beta version of the BERT base uncased model trained from scratch on a corpus of Finnish news, online discussions, and crawled data. Download the model here: [bert-base-finnish-uncased.zip](http://dl.turkunlp.org/finbert/bert-base-finnish-uncased.zip) ### Release 0.1 **September 30, 2019** We release a beta version of the BERT base cased model trained from scratch on a corpus of Finnish news, online discussions, and crawled data. Download the model here: [bert-base-finnish-cased.zip](http://dl.turkunlp.org/finbert/bert-base-finnish-cased.zip)
timm/vit_large_patch14_reg4_dinov2.lvd142m
timm
"2024-02-09T17:59:52Z"
23,757
2
timm
[ "timm", "pytorch", "safetensors", "image-feature-extraction", "arxiv:2309.16588", "arxiv:2304.07193", "arxiv:2010.11929", "license:apache-2.0", "region:us" ]
image-feature-extraction
"2023-10-30T04:52:17Z"
--- license: apache-2.0 library_name: timm tags: - image-feature-extraction - timm --- # Model card for vit_large_patch14_reg4_dinov2.lvd142m A Vision Transformer (ViT) image feature model with registers. Pretrained on LVD-142M with self-supervised DINOv2 method. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 304.4 - GMACs: 416.1 - Activations (M): 305.3 - Image size: 518 x 518 - **Papers:** - Vision Transformers Need Registers: https://arxiv.org/abs/2309.16588 - DINOv2: Learning Robust Visual Features without Supervision: https://arxiv.org/abs/2304.07193 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - **Original:** https://github.com/facebookresearch/dinov2 - **Pretrain Dataset:** LVD-142M ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('vit_large_patch14_reg4_dinov2.lvd142m', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'vit_large_patch14_reg4_dinov2.lvd142m', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 1374, 1024) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @article{darcet2023vision, title={Vision Transformers Need Registers}, author={Darcet, Timoth{'e}e and Oquab, Maxime and Mairal, Julien and Bojanowski, Piotr}, journal={arXiv preprint arXiv:2309.16588}, year={2023} } ``` ```bibtex @misc{oquab2023dinov2, title={DINOv2: Learning Robust Visual Features without Supervision}, author={Oquab, Maxime and Darcet, Timothée and Moutakanni, Theo and Vo, Huy V. and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and Howes, Russell and Huang, Po-Yao and Xu, Hu and Sharma, Vasu and Li, Shang-Wen and Galuba, Wojciech and Rabbat, Mike and Assran, Mido and Ballas, Nicolas and Synnaeve, Gabriel and Misra, Ishan and Jegou, Herve and Mairal, Julien and Labatut, Patrick and Joulin, Armand and Bojanowski, Piotr}, journal={arXiv:2304.07193}, year={2023} } ``` ```bibtex @article{dosovitskiy2020vit, title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale}, author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil}, journal={ICLR}, year={2021} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```
hkunlp/instructor-xl
hkunlp
"2023-01-21T06:33:27Z"
23,754
533
sentence-transformers
[ "sentence-transformers", "pytorch", "t5", "text-embedding", "embeddings", "information-retrieval", "beir", "text-classification", "language-model", "text-clustering", "text-semantic-similarity", "text-evaluation", "prompt-retrieval", "text-reranking", "feature-extraction", "sentence-similarity", "transformers", "English", "Sentence Similarity", "natural_questions", "ms_marco", "fever", "hotpot_qa", "mteb", "en", "arxiv:2212.09741", "license:apache-2.0", "model-index", "region:us" ]
sentence-similarity
"2022-12-20T06:07:18Z"
--- pipeline_tag: sentence-similarity tags: - text-embedding - embeddings - information-retrieval - beir - text-classification - language-model - text-clustering - text-semantic-similarity - text-evaluation - prompt-retrieval - text-reranking - sentence-transformers - feature-extraction - sentence-similarity - transformers - t5 - English - Sentence Similarity - natural_questions - ms_marco - fever - hotpot_qa - mteb language: en inference: false license: apache-2.0 model-index: - name: final_xl_results results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 85.08955223880596 - type: ap value: 52.66066378722476 - type: f1 value: 79.63340218960269 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 86.542 - type: ap value: 81.92695193008987 - type: f1 value: 86.51466132573681 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 42.964 - type: f1 value: 41.43146249774862 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 29.872 - type: map_at_10 value: 46.342 - type: map_at_100 value: 47.152 - type: map_at_1000 value: 47.154 - type: map_at_3 value: 41.216 - type: map_at_5 value: 44.035999999999994 - type: mrr_at_1 value: 30.939 - type: mrr_at_10 value: 46.756 - type: mrr_at_100 value: 47.573 - type: mrr_at_1000 value: 47.575 - type: mrr_at_3 value: 41.548 - type: mrr_at_5 value: 44.425 - type: ndcg_at_1 value: 29.872 - type: ndcg_at_10 value: 55.65 - type: ndcg_at_100 value: 58.88099999999999 - type: ndcg_at_1000 value: 58.951 - type: ndcg_at_3 value: 45.0 - type: ndcg_at_5 value: 50.09 - type: precision_at_1 value: 29.872 - type: precision_at_10 value: 8.549 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 18.658 - type: precision_at_5 value: 13.669999999999998 - type: recall_at_1 value: 29.872 - type: recall_at_10 value: 85.491 - type: recall_at_100 value: 99.075 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 55.974000000000004 - type: recall_at_5 value: 68.35 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 42.452729850641276 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 32.21141846480423 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 65.34710928952622 - type: mrr value: 77.61124301983028 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_spearman value: 84.15312230525639 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 82.66233766233766 - type: f1 value: 82.04175284777669 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 37.36697339826455 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 30.551241447593092 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 36.797000000000004 - type: map_at_10 value: 48.46 - type: map_at_100 value: 49.968 - type: map_at_1000 value: 50.080000000000005 - type: map_at_3 value: 44.71 - type: map_at_5 value: 46.592 - type: mrr_at_1 value: 45.494 - type: mrr_at_10 value: 54.747 - type: mrr_at_100 value: 55.43599999999999 - type: mrr_at_1000 value: 55.464999999999996 - type: mrr_at_3 value: 52.361000000000004 - type: mrr_at_5 value: 53.727000000000004 - type: ndcg_at_1 value: 45.494 - type: ndcg_at_10 value: 54.989 - type: ndcg_at_100 value: 60.096000000000004 - type: ndcg_at_1000 value: 61.58 - type: ndcg_at_3 value: 49.977 - type: ndcg_at_5 value: 51.964999999999996 - type: precision_at_1 value: 45.494 - type: precision_at_10 value: 10.558 - type: precision_at_100 value: 1.6049999999999998 - type: precision_at_1000 value: 0.203 - type: precision_at_3 value: 23.796 - type: precision_at_5 value: 16.881 - type: recall_at_1 value: 36.797000000000004 - type: recall_at_10 value: 66.83 - type: recall_at_100 value: 88.34100000000001 - type: recall_at_1000 value: 97.202 - type: recall_at_3 value: 51.961999999999996 - type: recall_at_5 value: 57.940000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.597 - type: map_at_10 value: 43.424 - type: map_at_100 value: 44.78 - type: map_at_1000 value: 44.913 - type: map_at_3 value: 40.315 - type: map_at_5 value: 41.987 - type: mrr_at_1 value: 40.382 - type: mrr_at_10 value: 49.219 - type: mrr_at_100 value: 49.895 - type: mrr_at_1000 value: 49.936 - type: mrr_at_3 value: 46.996 - type: mrr_at_5 value: 48.231 - type: ndcg_at_1 value: 40.382 - type: ndcg_at_10 value: 49.318 - type: ndcg_at_100 value: 53.839999999999996 - type: ndcg_at_1000 value: 55.82899999999999 - type: ndcg_at_3 value: 44.914 - type: ndcg_at_5 value: 46.798 - type: precision_at_1 value: 40.382 - type: precision_at_10 value: 9.274000000000001 - type: precision_at_100 value: 1.497 - type: precision_at_1000 value: 0.198 - type: precision_at_3 value: 21.592 - type: precision_at_5 value: 15.159 - type: recall_at_1 value: 32.597 - type: recall_at_10 value: 59.882000000000005 - type: recall_at_100 value: 78.446 - type: recall_at_1000 value: 90.88000000000001 - type: recall_at_3 value: 46.9 - type: recall_at_5 value: 52.222 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 43.8 - type: map_at_10 value: 57.293000000000006 - type: map_at_100 value: 58.321 - type: map_at_1000 value: 58.361 - type: map_at_3 value: 53.839999999999996 - type: map_at_5 value: 55.838 - type: mrr_at_1 value: 49.592000000000006 - type: mrr_at_10 value: 60.643 - type: mrr_at_100 value: 61.23499999999999 - type: mrr_at_1000 value: 61.251999999999995 - type: mrr_at_3 value: 58.265 - type: mrr_at_5 value: 59.717 - type: ndcg_at_1 value: 49.592000000000006 - type: ndcg_at_10 value: 63.364 - type: ndcg_at_100 value: 67.167 - type: ndcg_at_1000 value: 67.867 - type: ndcg_at_3 value: 57.912 - type: ndcg_at_5 value: 60.697 - type: precision_at_1 value: 49.592000000000006 - type: precision_at_10 value: 10.088 - type: precision_at_100 value: 1.2930000000000001 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 25.789 - type: precision_at_5 value: 17.541999999999998 - type: recall_at_1 value: 43.8 - type: recall_at_10 value: 77.635 - type: recall_at_100 value: 93.748 - type: recall_at_1000 value: 98.468 - type: recall_at_3 value: 63.223 - type: recall_at_5 value: 70.122 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.721 - type: map_at_10 value: 35.626999999999995 - type: map_at_100 value: 36.719 - type: map_at_1000 value: 36.8 - type: map_at_3 value: 32.781 - type: map_at_5 value: 34.333999999999996 - type: mrr_at_1 value: 29.604999999999997 - type: mrr_at_10 value: 37.564 - type: mrr_at_100 value: 38.505 - type: mrr_at_1000 value: 38.565 - type: mrr_at_3 value: 34.727000000000004 - type: mrr_at_5 value: 36.207 - type: ndcg_at_1 value: 29.604999999999997 - type: ndcg_at_10 value: 40.575 - type: ndcg_at_100 value: 45.613 - type: ndcg_at_1000 value: 47.676 - type: ndcg_at_3 value: 34.811 - type: ndcg_at_5 value: 37.491 - type: precision_at_1 value: 29.604999999999997 - type: precision_at_10 value: 6.1690000000000005 - type: precision_at_100 value: 0.906 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 14.237 - type: precision_at_5 value: 10.056 - type: recall_at_1 value: 27.721 - type: recall_at_10 value: 54.041 - type: recall_at_100 value: 76.62299999999999 - type: recall_at_1000 value: 92.134 - type: recall_at_3 value: 38.582 - type: recall_at_5 value: 44.989000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.553 - type: map_at_10 value: 25.384 - type: map_at_100 value: 26.655 - type: map_at_1000 value: 26.778000000000002 - type: map_at_3 value: 22.733 - type: map_at_5 value: 24.119 - type: mrr_at_1 value: 20.149 - type: mrr_at_10 value: 29.705 - type: mrr_at_100 value: 30.672 - type: mrr_at_1000 value: 30.737 - type: mrr_at_3 value: 27.032 - type: mrr_at_5 value: 28.369 - type: ndcg_at_1 value: 20.149 - type: ndcg_at_10 value: 30.843999999999998 - type: ndcg_at_100 value: 36.716 - type: ndcg_at_1000 value: 39.495000000000005 - type: ndcg_at_3 value: 25.918999999999997 - type: ndcg_at_5 value: 27.992 - type: precision_at_1 value: 20.149 - type: precision_at_10 value: 5.858 - type: precision_at_100 value: 1.009 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 12.645000000000001 - type: precision_at_5 value: 9.179 - type: recall_at_1 value: 16.553 - type: recall_at_10 value: 43.136 - type: recall_at_100 value: 68.562 - type: recall_at_1000 value: 88.208 - type: recall_at_3 value: 29.493000000000002 - type: recall_at_5 value: 34.751 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.000999999999998 - type: map_at_10 value: 39.004 - type: map_at_100 value: 40.461999999999996 - type: map_at_1000 value: 40.566 - type: map_at_3 value: 35.805 - type: map_at_5 value: 37.672 - type: mrr_at_1 value: 33.782000000000004 - type: mrr_at_10 value: 44.702 - type: mrr_at_100 value: 45.528 - type: mrr_at_1000 value: 45.576 - type: mrr_at_3 value: 42.14 - type: mrr_at_5 value: 43.651 - type: ndcg_at_1 value: 33.782000000000004 - type: ndcg_at_10 value: 45.275999999999996 - type: ndcg_at_100 value: 50.888 - type: ndcg_at_1000 value: 52.879 - type: ndcg_at_3 value: 40.191 - type: ndcg_at_5 value: 42.731 - type: precision_at_1 value: 33.782000000000004 - type: precision_at_10 value: 8.200000000000001 - type: precision_at_100 value: 1.287 - type: precision_at_1000 value: 0.16199999999999998 - type: precision_at_3 value: 19.185 - type: precision_at_5 value: 13.667000000000002 - type: recall_at_1 value: 28.000999999999998 - type: recall_at_10 value: 58.131 - type: recall_at_100 value: 80.869 - type: recall_at_1000 value: 93.931 - type: recall_at_3 value: 44.161 - type: recall_at_5 value: 50.592000000000006 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.047 - type: map_at_10 value: 38.596000000000004 - type: map_at_100 value: 40.116 - type: map_at_1000 value: 40.232 - type: map_at_3 value: 35.205 - type: map_at_5 value: 37.076 - type: mrr_at_1 value: 34.932 - type: mrr_at_10 value: 44.496 - type: mrr_at_100 value: 45.47 - type: mrr_at_1000 value: 45.519999999999996 - type: mrr_at_3 value: 41.743 - type: mrr_at_5 value: 43.352000000000004 - type: ndcg_at_1 value: 34.932 - type: ndcg_at_10 value: 44.901 - type: ndcg_at_100 value: 50.788999999999994 - type: ndcg_at_1000 value: 52.867 - type: ndcg_at_3 value: 39.449 - type: ndcg_at_5 value: 41.929 - type: precision_at_1 value: 34.932 - type: precision_at_10 value: 8.311 - type: precision_at_100 value: 1.3050000000000002 - type: precision_at_1000 value: 0.166 - type: precision_at_3 value: 18.836 - type: precision_at_5 value: 13.447000000000001 - type: recall_at_1 value: 28.047 - type: recall_at_10 value: 57.717 - type: recall_at_100 value: 82.182 - type: recall_at_1000 value: 95.82000000000001 - type: recall_at_3 value: 42.448 - type: recall_at_5 value: 49.071 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.861250000000005 - type: map_at_10 value: 37.529583333333335 - type: map_at_100 value: 38.7915 - type: map_at_1000 value: 38.90558333333335 - type: map_at_3 value: 34.57333333333333 - type: map_at_5 value: 36.187166666666656 - type: mrr_at_1 value: 32.88291666666666 - type: mrr_at_10 value: 41.79750000000001 - type: mrr_at_100 value: 42.63183333333333 - type: mrr_at_1000 value: 42.68483333333333 - type: mrr_at_3 value: 39.313750000000006 - type: mrr_at_5 value: 40.70483333333333 - type: ndcg_at_1 value: 32.88291666666666 - type: ndcg_at_10 value: 43.09408333333333 - type: ndcg_at_100 value: 48.22158333333333 - type: ndcg_at_1000 value: 50.358000000000004 - type: ndcg_at_3 value: 38.129583333333336 - type: ndcg_at_5 value: 40.39266666666666 - type: precision_at_1 value: 32.88291666666666 - type: precision_at_10 value: 7.5584999999999996 - type: precision_at_100 value: 1.1903333333333332 - type: precision_at_1000 value: 0.15658333333333332 - type: precision_at_3 value: 17.495916666666666 - type: precision_at_5 value: 12.373833333333332 - type: recall_at_1 value: 27.861250000000005 - type: recall_at_10 value: 55.215916666666665 - type: recall_at_100 value: 77.392 - type: recall_at_1000 value: 92.04908333333334 - type: recall_at_3 value: 41.37475 - type: recall_at_5 value: 47.22908333333333 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.064999999999998 - type: map_at_10 value: 31.635999999999996 - type: map_at_100 value: 32.596000000000004 - type: map_at_1000 value: 32.695 - type: map_at_3 value: 29.612 - type: map_at_5 value: 30.768 - type: mrr_at_1 value: 28.528 - type: mrr_at_10 value: 34.717 - type: mrr_at_100 value: 35.558 - type: mrr_at_1000 value: 35.626000000000005 - type: mrr_at_3 value: 32.745000000000005 - type: mrr_at_5 value: 33.819 - type: ndcg_at_1 value: 28.528 - type: ndcg_at_10 value: 35.647 - type: ndcg_at_100 value: 40.207 - type: ndcg_at_1000 value: 42.695 - type: ndcg_at_3 value: 31.878 - type: ndcg_at_5 value: 33.634 - type: precision_at_1 value: 28.528 - type: precision_at_10 value: 5.46 - type: precision_at_100 value: 0.84 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 13.547999999999998 - type: precision_at_5 value: 9.325 - type: recall_at_1 value: 25.064999999999998 - type: recall_at_10 value: 45.096000000000004 - type: recall_at_100 value: 65.658 - type: recall_at_1000 value: 84.128 - type: recall_at_3 value: 34.337 - type: recall_at_5 value: 38.849000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.276 - type: map_at_10 value: 24.535 - type: map_at_100 value: 25.655 - type: map_at_1000 value: 25.782 - type: map_at_3 value: 22.228 - type: map_at_5 value: 23.612 - type: mrr_at_1 value: 21.266 - type: mrr_at_10 value: 28.474 - type: mrr_at_100 value: 29.398000000000003 - type: mrr_at_1000 value: 29.482000000000003 - type: mrr_at_3 value: 26.245 - type: mrr_at_5 value: 27.624 - type: ndcg_at_1 value: 21.266 - type: ndcg_at_10 value: 29.087000000000003 - type: ndcg_at_100 value: 34.374 - type: ndcg_at_1000 value: 37.433 - type: ndcg_at_3 value: 25.040000000000003 - type: ndcg_at_5 value: 27.116 - type: precision_at_1 value: 21.266 - type: precision_at_10 value: 5.258 - type: precision_at_100 value: 0.9299999999999999 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 11.849 - type: precision_at_5 value: 8.699 - type: recall_at_1 value: 17.276 - type: recall_at_10 value: 38.928000000000004 - type: recall_at_100 value: 62.529 - type: recall_at_1000 value: 84.44800000000001 - type: recall_at_3 value: 27.554000000000002 - type: recall_at_5 value: 32.915 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.297 - type: map_at_10 value: 36.957 - type: map_at_100 value: 38.252 - type: map_at_1000 value: 38.356 - type: map_at_3 value: 34.121 - type: map_at_5 value: 35.782000000000004 - type: mrr_at_1 value: 32.275999999999996 - type: mrr_at_10 value: 41.198 - type: mrr_at_100 value: 42.131 - type: mrr_at_1000 value: 42.186 - type: mrr_at_3 value: 38.557 - type: mrr_at_5 value: 40.12 - type: ndcg_at_1 value: 32.275999999999996 - type: ndcg_at_10 value: 42.516 - type: ndcg_at_100 value: 48.15 - type: ndcg_at_1000 value: 50.344 - type: ndcg_at_3 value: 37.423 - type: ndcg_at_5 value: 39.919 - type: precision_at_1 value: 32.275999999999996 - type: precision_at_10 value: 7.155 - type: precision_at_100 value: 1.123 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 17.163999999999998 - type: precision_at_5 value: 12.127 - type: recall_at_1 value: 27.297 - type: recall_at_10 value: 55.238 - type: recall_at_100 value: 79.2 - type: recall_at_1000 value: 94.258 - type: recall_at_3 value: 41.327000000000005 - type: recall_at_5 value: 47.588 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.142000000000003 - type: map_at_10 value: 38.769 - type: map_at_100 value: 40.292 - type: map_at_1000 value: 40.510000000000005 - type: map_at_3 value: 35.39 - type: map_at_5 value: 37.009 - type: mrr_at_1 value: 34.19 - type: mrr_at_10 value: 43.418 - type: mrr_at_100 value: 44.132 - type: mrr_at_1000 value: 44.175 - type: mrr_at_3 value: 40.547 - type: mrr_at_5 value: 42.088 - type: ndcg_at_1 value: 34.19 - type: ndcg_at_10 value: 45.14 - type: ndcg_at_100 value: 50.364 - type: ndcg_at_1000 value: 52.481 - type: ndcg_at_3 value: 39.466 - type: ndcg_at_5 value: 41.772 - type: precision_at_1 value: 34.19 - type: precision_at_10 value: 8.715 - type: precision_at_100 value: 1.6150000000000002 - type: precision_at_1000 value: 0.247 - type: precision_at_3 value: 18.248 - type: precision_at_5 value: 13.161999999999999 - type: recall_at_1 value: 29.142000000000003 - type: recall_at_10 value: 57.577999999999996 - type: recall_at_100 value: 81.428 - type: recall_at_1000 value: 94.017 - type: recall_at_3 value: 41.402 - type: recall_at_5 value: 47.695 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.039 - type: map_at_10 value: 30.669999999999998 - type: map_at_100 value: 31.682 - type: map_at_1000 value: 31.794 - type: map_at_3 value: 28.139999999999997 - type: map_at_5 value: 29.457 - type: mrr_at_1 value: 24.399 - type: mrr_at_10 value: 32.687 - type: mrr_at_100 value: 33.622 - type: mrr_at_1000 value: 33.698 - type: mrr_at_3 value: 30.407 - type: mrr_at_5 value: 31.552999999999997 - type: ndcg_at_1 value: 24.399 - type: ndcg_at_10 value: 35.472 - type: ndcg_at_100 value: 40.455000000000005 - type: ndcg_at_1000 value: 43.15 - type: ndcg_at_3 value: 30.575000000000003 - type: ndcg_at_5 value: 32.668 - type: precision_at_1 value: 24.399 - type: precision_at_10 value: 5.656 - type: precision_at_100 value: 0.874 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 13.062000000000001 - type: precision_at_5 value: 9.242 - type: recall_at_1 value: 22.039 - type: recall_at_10 value: 48.379 - type: recall_at_100 value: 71.11800000000001 - type: recall_at_1000 value: 91.095 - type: recall_at_3 value: 35.108 - type: recall_at_5 value: 40.015 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 10.144 - type: map_at_10 value: 18.238 - type: map_at_100 value: 20.143 - type: map_at_1000 value: 20.346 - type: map_at_3 value: 14.809 - type: map_at_5 value: 16.567999999999998 - type: mrr_at_1 value: 22.671 - type: mrr_at_10 value: 34.906 - type: mrr_at_100 value: 35.858000000000004 - type: mrr_at_1000 value: 35.898 - type: mrr_at_3 value: 31.238 - type: mrr_at_5 value: 33.342 - type: ndcg_at_1 value: 22.671 - type: ndcg_at_10 value: 26.540000000000003 - type: ndcg_at_100 value: 34.138000000000005 - type: ndcg_at_1000 value: 37.72 - type: ndcg_at_3 value: 20.766000000000002 - type: ndcg_at_5 value: 22.927 - type: precision_at_1 value: 22.671 - type: precision_at_10 value: 8.619 - type: precision_at_100 value: 1.678 - type: precision_at_1000 value: 0.23500000000000001 - type: precision_at_3 value: 15.592 - type: precision_at_5 value: 12.43 - type: recall_at_1 value: 10.144 - type: recall_at_10 value: 33.46 - type: recall_at_100 value: 59.758 - type: recall_at_1000 value: 79.704 - type: recall_at_3 value: 19.604 - type: recall_at_5 value: 25.367 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.654 - type: map_at_10 value: 18.506 - type: map_at_100 value: 26.412999999999997 - type: map_at_1000 value: 28.13 - type: map_at_3 value: 13.379 - type: map_at_5 value: 15.529000000000002 - type: mrr_at_1 value: 66.0 - type: mrr_at_10 value: 74.13 - type: mrr_at_100 value: 74.48700000000001 - type: mrr_at_1000 value: 74.49799999999999 - type: mrr_at_3 value: 72.75 - type: mrr_at_5 value: 73.762 - type: ndcg_at_1 value: 54.50000000000001 - type: ndcg_at_10 value: 40.236 - type: ndcg_at_100 value: 44.690999999999995 - type: ndcg_at_1000 value: 52.195 - type: ndcg_at_3 value: 45.632 - type: ndcg_at_5 value: 42.952 - type: precision_at_1 value: 66.0 - type: precision_at_10 value: 31.724999999999998 - type: precision_at_100 value: 10.299999999999999 - type: precision_at_1000 value: 2.194 - type: precision_at_3 value: 48.75 - type: precision_at_5 value: 41.6 - type: recall_at_1 value: 8.654 - type: recall_at_10 value: 23.74 - type: recall_at_100 value: 50.346999999999994 - type: recall_at_1000 value: 74.376 - type: recall_at_3 value: 14.636 - type: recall_at_5 value: 18.009 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 53.245 - type: f1 value: 48.74520523753552 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 51.729 - type: map_at_10 value: 63.904 - type: map_at_100 value: 64.363 - type: map_at_1000 value: 64.38199999999999 - type: map_at_3 value: 61.393 - type: map_at_5 value: 63.02100000000001 - type: mrr_at_1 value: 55.686 - type: mrr_at_10 value: 67.804 - type: mrr_at_100 value: 68.15299999999999 - type: mrr_at_1000 value: 68.161 - type: mrr_at_3 value: 65.494 - type: mrr_at_5 value: 67.01599999999999 - type: ndcg_at_1 value: 55.686 - type: ndcg_at_10 value: 70.025 - type: ndcg_at_100 value: 72.011 - type: ndcg_at_1000 value: 72.443 - type: ndcg_at_3 value: 65.32900000000001 - type: ndcg_at_5 value: 68.05600000000001 - type: precision_at_1 value: 55.686 - type: precision_at_10 value: 9.358 - type: precision_at_100 value: 1.05 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 26.318 - type: precision_at_5 value: 17.321 - type: recall_at_1 value: 51.729 - type: recall_at_10 value: 85.04 - type: recall_at_100 value: 93.777 - type: recall_at_1000 value: 96.824 - type: recall_at_3 value: 72.521 - type: recall_at_5 value: 79.148 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 23.765 - type: map_at_10 value: 39.114 - type: map_at_100 value: 40.987 - type: map_at_1000 value: 41.155 - type: map_at_3 value: 34.028000000000006 - type: map_at_5 value: 36.925000000000004 - type: mrr_at_1 value: 46.451 - type: mrr_at_10 value: 54.711 - type: mrr_at_100 value: 55.509 - type: mrr_at_1000 value: 55.535000000000004 - type: mrr_at_3 value: 52.649 - type: mrr_at_5 value: 53.729000000000006 - type: ndcg_at_1 value: 46.451 - type: ndcg_at_10 value: 46.955999999999996 - type: ndcg_at_100 value: 53.686 - type: ndcg_at_1000 value: 56.230000000000004 - type: ndcg_at_3 value: 43.374 - type: ndcg_at_5 value: 44.372 - type: precision_at_1 value: 46.451 - type: precision_at_10 value: 13.256 - type: precision_at_100 value: 2.019 - type: precision_at_1000 value: 0.247 - type: precision_at_3 value: 29.115000000000002 - type: precision_at_5 value: 21.389 - type: recall_at_1 value: 23.765 - type: recall_at_10 value: 53.452999999999996 - type: recall_at_100 value: 78.828 - type: recall_at_1000 value: 93.938 - type: recall_at_3 value: 39.023 - type: recall_at_5 value: 45.18 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 31.918000000000003 - type: map_at_10 value: 46.741 - type: map_at_100 value: 47.762 - type: map_at_1000 value: 47.849000000000004 - type: map_at_3 value: 43.578 - type: map_at_5 value: 45.395 - type: mrr_at_1 value: 63.834999999999994 - type: mrr_at_10 value: 71.312 - type: mrr_at_100 value: 71.695 - type: mrr_at_1000 value: 71.714 - type: mrr_at_3 value: 69.82000000000001 - type: mrr_at_5 value: 70.726 - type: ndcg_at_1 value: 63.834999999999994 - type: ndcg_at_10 value: 55.879999999999995 - type: ndcg_at_100 value: 59.723000000000006 - type: ndcg_at_1000 value: 61.49400000000001 - type: ndcg_at_3 value: 50.964 - type: ndcg_at_5 value: 53.47 - type: precision_at_1 value: 63.834999999999994 - type: precision_at_10 value: 11.845 - type: precision_at_100 value: 1.4869999999999999 - type: precision_at_1000 value: 0.172 - type: precision_at_3 value: 32.158 - type: precision_at_5 value: 21.278 - type: recall_at_1 value: 31.918000000000003 - type: recall_at_10 value: 59.223000000000006 - type: recall_at_100 value: 74.328 - type: recall_at_1000 value: 86.05000000000001 - type: recall_at_3 value: 48.238 - type: recall_at_5 value: 53.193999999999996 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 79.7896 - type: ap value: 73.65166029460288 - type: f1 value: 79.71794693711813 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 22.239 - type: map_at_10 value: 34.542 - type: map_at_100 value: 35.717999999999996 - type: map_at_1000 value: 35.764 - type: map_at_3 value: 30.432 - type: map_at_5 value: 32.81 - type: mrr_at_1 value: 22.908 - type: mrr_at_10 value: 35.127 - type: mrr_at_100 value: 36.238 - type: mrr_at_1000 value: 36.278 - type: mrr_at_3 value: 31.076999999999998 - type: mrr_at_5 value: 33.419 - type: ndcg_at_1 value: 22.908 - type: ndcg_at_10 value: 41.607 - type: ndcg_at_100 value: 47.28 - type: ndcg_at_1000 value: 48.414 - type: ndcg_at_3 value: 33.253 - type: ndcg_at_5 value: 37.486000000000004 - type: precision_at_1 value: 22.908 - type: precision_at_10 value: 6.645 - type: precision_at_100 value: 0.9490000000000001 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 14.130999999999998 - type: precision_at_5 value: 10.616 - type: recall_at_1 value: 22.239 - type: recall_at_10 value: 63.42 - type: recall_at_100 value: 89.696 - type: recall_at_1000 value: 98.351 - type: recall_at_3 value: 40.77 - type: recall_at_5 value: 50.93 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 95.06839945280439 - type: f1 value: 94.74276398224072 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 72.25718194254446 - type: f1 value: 53.91164489161391 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.47948890383323 - type: f1 value: 69.98520247230257 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.46603900470748 - type: f1 value: 76.44111526065399 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.19106070798198 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 30.78772205248094 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.811231631488507 - type: mrr value: 32.98200485378021 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.9 - type: map_at_10 value: 13.703000000000001 - type: map_at_100 value: 17.251 - type: map_at_1000 value: 18.795 - type: map_at_3 value: 10.366999999999999 - type: map_at_5 value: 11.675 - type: mrr_at_1 value: 47.059 - type: mrr_at_10 value: 55.816 - type: mrr_at_100 value: 56.434 - type: mrr_at_1000 value: 56.467 - type: mrr_at_3 value: 53.973000000000006 - type: mrr_at_5 value: 55.257999999999996 - type: ndcg_at_1 value: 44.737 - type: ndcg_at_10 value: 35.997 - type: ndcg_at_100 value: 33.487 - type: ndcg_at_1000 value: 41.897 - type: ndcg_at_3 value: 41.18 - type: ndcg_at_5 value: 38.721 - type: precision_at_1 value: 46.129999999999995 - type: precision_at_10 value: 26.533 - type: precision_at_100 value: 8.706 - type: precision_at_1000 value: 2.16 - type: precision_at_3 value: 38.493 - type: precision_at_5 value: 33.189 - type: recall_at_1 value: 6.9 - type: recall_at_10 value: 17.488999999999997 - type: recall_at_100 value: 34.583000000000006 - type: recall_at_1000 value: 64.942 - type: recall_at_3 value: 11.494 - type: recall_at_5 value: 13.496 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 33.028999999999996 - type: map_at_10 value: 49.307 - type: map_at_100 value: 50.205 - type: map_at_1000 value: 50.23 - type: map_at_3 value: 44.782 - type: map_at_5 value: 47.599999999999994 - type: mrr_at_1 value: 37.108999999999995 - type: mrr_at_10 value: 51.742999999999995 - type: mrr_at_100 value: 52.405 - type: mrr_at_1000 value: 52.422000000000004 - type: mrr_at_3 value: 48.087999999999994 - type: mrr_at_5 value: 50.414 - type: ndcg_at_1 value: 37.08 - type: ndcg_at_10 value: 57.236 - type: ndcg_at_100 value: 60.931999999999995 - type: ndcg_at_1000 value: 61.522 - type: ndcg_at_3 value: 48.93 - type: ndcg_at_5 value: 53.561 - type: precision_at_1 value: 37.08 - type: precision_at_10 value: 9.386 - type: precision_at_100 value: 1.1480000000000001 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 22.258 - type: precision_at_5 value: 16.025 - type: recall_at_1 value: 33.028999999999996 - type: recall_at_10 value: 78.805 - type: recall_at_100 value: 94.643 - type: recall_at_1000 value: 99.039 - type: recall_at_3 value: 57.602 - type: recall_at_5 value: 68.253 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 71.122 - type: map_at_10 value: 85.237 - type: map_at_100 value: 85.872 - type: map_at_1000 value: 85.885 - type: map_at_3 value: 82.27499999999999 - type: map_at_5 value: 84.13199999999999 - type: mrr_at_1 value: 81.73 - type: mrr_at_10 value: 87.834 - type: mrr_at_100 value: 87.92 - type: mrr_at_1000 value: 87.921 - type: mrr_at_3 value: 86.878 - type: mrr_at_5 value: 87.512 - type: ndcg_at_1 value: 81.73 - type: ndcg_at_10 value: 88.85499999999999 - type: ndcg_at_100 value: 89.992 - type: ndcg_at_1000 value: 90.07 - type: ndcg_at_3 value: 85.997 - type: ndcg_at_5 value: 87.55199999999999 - type: precision_at_1 value: 81.73 - type: precision_at_10 value: 13.491 - type: precision_at_100 value: 1.536 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.623 - type: precision_at_5 value: 24.742 - type: recall_at_1 value: 71.122 - type: recall_at_10 value: 95.935 - type: recall_at_100 value: 99.657 - type: recall_at_1000 value: 99.996 - type: recall_at_3 value: 87.80799999999999 - type: recall_at_5 value: 92.161 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 63.490029238193756 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 65.13153408508836 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.202999999999999 - type: map_at_10 value: 10.174 - type: map_at_100 value: 12.138 - type: map_at_1000 value: 12.418 - type: map_at_3 value: 7.379 - type: map_at_5 value: 8.727 - type: mrr_at_1 value: 20.7 - type: mrr_at_10 value: 30.389 - type: mrr_at_100 value: 31.566 - type: mrr_at_1000 value: 31.637999999999998 - type: mrr_at_3 value: 27.133000000000003 - type: mrr_at_5 value: 29.078 - type: ndcg_at_1 value: 20.7 - type: ndcg_at_10 value: 17.355999999999998 - type: ndcg_at_100 value: 25.151 - type: ndcg_at_1000 value: 30.37 - type: ndcg_at_3 value: 16.528000000000002 - type: ndcg_at_5 value: 14.396999999999998 - type: precision_at_1 value: 20.7 - type: precision_at_10 value: 8.98 - type: precision_at_100 value: 2.015 - type: precision_at_1000 value: 0.327 - type: precision_at_3 value: 15.367 - type: precision_at_5 value: 12.559999999999999 - type: recall_at_1 value: 4.202999999999999 - type: recall_at_10 value: 18.197 - type: recall_at_100 value: 40.903 - type: recall_at_1000 value: 66.427 - type: recall_at_3 value: 9.362 - type: recall_at_5 value: 12.747 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_spearman value: 81.69890989765257 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_spearman value: 75.31953790551489 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_spearman value: 87.44050861280759 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_spearman value: 81.86922869270393 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_spearman value: 88.9399170304284 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_spearman value: 85.38015314088582 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_spearman value: 90.53653527788835 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_spearman value: 68.64526474250209 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_spearman value: 86.56156983963042 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 79.48610254648003 - type: mrr value: 94.02481505422682 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 48.983 - type: map_at_10 value: 59.077999999999996 - type: map_at_100 value: 59.536 - type: map_at_1000 value: 59.575 - type: map_at_3 value: 55.691 - type: map_at_5 value: 57.410000000000004 - type: mrr_at_1 value: 51.666999999999994 - type: mrr_at_10 value: 60.427 - type: mrr_at_100 value: 60.763 - type: mrr_at_1000 value: 60.79900000000001 - type: mrr_at_3 value: 57.556 - type: mrr_at_5 value: 59.089000000000006 - type: ndcg_at_1 value: 51.666999999999994 - type: ndcg_at_10 value: 64.559 - type: ndcg_at_100 value: 66.58 - type: ndcg_at_1000 value: 67.64 - type: ndcg_at_3 value: 58.287 - type: ndcg_at_5 value: 61.001000000000005 - type: precision_at_1 value: 51.666999999999994 - type: precision_at_10 value: 9.067 - type: precision_at_100 value: 1.0170000000000001 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 23.0 - type: precision_at_5 value: 15.6 - type: recall_at_1 value: 48.983 - type: recall_at_10 value: 80.289 - type: recall_at_100 value: 89.43299999999999 - type: recall_at_1000 value: 97.667 - type: recall_at_3 value: 62.978 - type: recall_at_5 value: 69.872 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.79009900990098 - type: cos_sim_ap value: 94.94115052608419 - type: cos_sim_f1 value: 89.1260162601626 - type: cos_sim_precision value: 90.599173553719 - type: cos_sim_recall value: 87.7 - type: dot_accuracy value: 99.79009900990098 - type: dot_ap value: 94.94115052608419 - type: dot_f1 value: 89.1260162601626 - type: dot_precision value: 90.599173553719 - type: dot_recall value: 87.7 - type: euclidean_accuracy value: 99.79009900990098 - type: euclidean_ap value: 94.94115052608419 - type: euclidean_f1 value: 89.1260162601626 - type: euclidean_precision value: 90.599173553719 - type: euclidean_recall value: 87.7 - type: manhattan_accuracy value: 99.7940594059406 - type: manhattan_ap value: 94.95271414642431 - type: manhattan_f1 value: 89.24508790072387 - type: manhattan_precision value: 92.3982869379015 - type: manhattan_recall value: 86.3 - type: max_accuracy value: 99.7940594059406 - type: max_ap value: 94.95271414642431 - type: max_f1 value: 89.24508790072387 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 68.43866571935851 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 35.16579026551532 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 52.518952473513934 - type: mrr value: 53.292457134368895 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.12529588316604 - type: cos_sim_spearman value: 32.31662126895294 - type: dot_pearson value: 31.125303796647056 - type: dot_spearman value: 32.31662126895294 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.219 - type: map_at_10 value: 1.7469999999999999 - type: map_at_100 value: 10.177999999999999 - type: map_at_1000 value: 26.108999999999998 - type: map_at_3 value: 0.64 - type: map_at_5 value: 0.968 - type: mrr_at_1 value: 82.0 - type: mrr_at_10 value: 89.067 - type: mrr_at_100 value: 89.067 - type: mrr_at_1000 value: 89.067 - type: mrr_at_3 value: 88.333 - type: mrr_at_5 value: 88.73299999999999 - type: ndcg_at_1 value: 78.0 - type: ndcg_at_10 value: 71.398 - type: ndcg_at_100 value: 55.574999999999996 - type: ndcg_at_1000 value: 51.771 - type: ndcg_at_3 value: 77.765 - type: ndcg_at_5 value: 73.614 - type: precision_at_1 value: 82.0 - type: precision_at_10 value: 75.4 - type: precision_at_100 value: 58.040000000000006 - type: precision_at_1000 value: 23.516000000000002 - type: precision_at_3 value: 84.0 - type: precision_at_5 value: 78.4 - type: recall_at_1 value: 0.219 - type: recall_at_10 value: 1.958 - type: recall_at_100 value: 13.797999999999998 - type: recall_at_1000 value: 49.881 - type: recall_at_3 value: 0.672 - type: recall_at_5 value: 1.0370000000000001 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.8610000000000002 - type: map_at_10 value: 8.705 - type: map_at_100 value: 15.164 - type: map_at_1000 value: 16.78 - type: map_at_3 value: 4.346 - type: map_at_5 value: 6.151 - type: mrr_at_1 value: 22.448999999999998 - type: mrr_at_10 value: 41.556 - type: mrr_at_100 value: 42.484 - type: mrr_at_1000 value: 42.494 - type: mrr_at_3 value: 37.755 - type: mrr_at_5 value: 40.102 - type: ndcg_at_1 value: 21.429000000000002 - type: ndcg_at_10 value: 23.439 - type: ndcg_at_100 value: 36.948 - type: ndcg_at_1000 value: 48.408 - type: ndcg_at_3 value: 22.261 - type: ndcg_at_5 value: 23.085 - type: precision_at_1 value: 22.448999999999998 - type: precision_at_10 value: 21.633 - type: precision_at_100 value: 8.02 - type: precision_at_1000 value: 1.5939999999999999 - type: precision_at_3 value: 23.810000000000002 - type: precision_at_5 value: 24.490000000000002 - type: recall_at_1 value: 1.8610000000000002 - type: recall_at_10 value: 15.876000000000001 - type: recall_at_100 value: 50.300999999999995 - type: recall_at_1000 value: 86.098 - type: recall_at_3 value: 5.892 - type: recall_at_5 value: 9.443 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.3264 - type: ap value: 13.249577616243794 - type: f1 value: 53.621518367695685 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 61.57611771363894 - type: f1 value: 61.79797478568639 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 53.38315344479284 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 87.55438993860642 - type: cos_sim_ap value: 77.98702600017738 - type: cos_sim_f1 value: 71.94971653931476 - type: cos_sim_precision value: 67.50693802035153 - type: cos_sim_recall value: 77.01846965699208 - type: dot_accuracy value: 87.55438993860642 - type: dot_ap value: 77.98702925907986 - type: dot_f1 value: 71.94971653931476 - type: dot_precision value: 67.50693802035153 - type: dot_recall value: 77.01846965699208 - type: euclidean_accuracy value: 87.55438993860642 - type: euclidean_ap value: 77.98702951957925 - type: euclidean_f1 value: 71.94971653931476 - type: euclidean_precision value: 67.50693802035153 - type: euclidean_recall value: 77.01846965699208 - type: manhattan_accuracy value: 87.54246885617214 - type: manhattan_ap value: 77.95531413902947 - type: manhattan_f1 value: 71.93605683836589 - type: manhattan_precision value: 69.28152492668622 - type: manhattan_recall value: 74.80211081794195 - type: max_accuracy value: 87.55438993860642 - type: max_ap value: 77.98702951957925 - type: max_f1 value: 71.94971653931476 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.47296930182016 - type: cos_sim_ap value: 86.92853616302108 - type: cos_sim_f1 value: 79.35138351681047 - type: cos_sim_precision value: 76.74820143884892 - type: cos_sim_recall value: 82.13735756082538 - type: dot_accuracy value: 89.47296930182016 - type: dot_ap value: 86.92854339601595 - type: dot_f1 value: 79.35138351681047 - type: dot_precision value: 76.74820143884892 - type: dot_recall value: 82.13735756082538 - type: euclidean_accuracy value: 89.47296930182016 - type: euclidean_ap value: 86.92854191061649 - type: euclidean_f1 value: 79.35138351681047 - type: euclidean_precision value: 76.74820143884892 - type: euclidean_recall value: 82.13735756082538 - type: manhattan_accuracy value: 89.47685023479644 - type: manhattan_ap value: 86.90063722679578 - type: manhattan_f1 value: 79.30753865502702 - type: manhattan_precision value: 76.32066068631639 - type: manhattan_recall value: 82.53772713273791 - type: max_accuracy value: 89.47685023479644 - type: max_ap value: 86.92854339601595 - type: max_f1 value: 79.35138351681047 --- # hkunlp/instructor-xl We introduce **Instructor**👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨‍ achieves sota on 70 diverse embedding tasks! The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)! **************************** **Updates** **************************** * 01/21: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-xl) trained with hard negatives, which gives better performance. * 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-xl) and [project page](https://instructor-embedding.github.io/)! Check them out! ## Quick start <hr /> ## Installation ```bash pip install InstructorEmbedding ``` ## Compute your customized embeddings Then you can use the model like this to calculate domain-specific and task-aware embeddings: ```python from InstructorEmbedding import INSTRUCTOR model = INSTRUCTOR('hkunlp/instructor-xl') sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments" instruction = "Represent the Science title:" embeddings = model.encode([[instruction,sentence]]) print(embeddings) ``` ## Use cases <hr /> ## Calculate embeddings for your customized texts If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions: &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Represent the `domain` `text_type` for `task_objective`: * `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc. * `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc. * `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc. ## Calculate Sentence similarities You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**. ```python from sklearn.metrics.pairwise import cosine_similarity sentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'], ['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']] sentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'], ['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']] embeddings_a = model.encode(sentences_a) embeddings_b = model.encode(sentences_b) similarities = cosine_similarity(embeddings_a,embeddings_b) print(similarities) ``` ## Information Retrieval You can also use **customized embeddings** for information retrieval. ```python import numpy as np from sklearn.metrics.pairwise import cosine_similarity query = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']] corpus = [['Represent the Wikipedia document for retrieval: ','Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that the term "mixed economies" more precisely describes most contemporary economies, due to their containing both private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For example, higher demand for certain goods and services lead to higher prices and lower demand for certain goods lead to lower prices.'], ['Represent the Wikipedia document for retrieval: ',"The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loans—and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession"], ['Represent the Wikipedia document for retrieval: ','Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.']] query_embeddings = model.encode(query) corpus_embeddings = model.encode(corpus) similarities = cosine_similarity(query_embeddings,corpus_embeddings) retrieved_doc_id = np.argmax(similarities) print(retrieved_doc_id) ``` ## Clustering Use **customized embeddings** for clustering texts in groups. ```python import sklearn.cluster sentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'], ['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'], ['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'], ['Represent the Medicine sentence for clustering: ',"QCD corrections to Associated t-tbar-H production at the Tevatron"], ['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher, Vector States of Charmonium']] embeddings = model.encode(sentences) clustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2) clustering_model.fit(embeddings) cluster_assignment = clustering_model.labels_ print(cluster_assignment) ```
Lykon/dreamshaper-7
Lykon
"2023-12-07T10:13:17Z"
23,743
51
diffusers
[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "art", "artistic", "anime", "dreamshaper", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
"2023-08-26T16:49:11Z"
--- language: - en license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - art - artistic - diffusers - anime - dreamshaper duplicated_from: lykon/dreamshaper-7 --- # Dreamshaper 7 `lykon/dreamshaper-7` is a Stable Diffusion model that has been fine-tuned on [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5). Please consider supporting me: - on [Patreon](https://www.patreon.com/Lykon275) - or [buy me a coffee](https://snipfeed.co/lykon) ## Diffusers For more general information on how to run text-to-image models with 🧨 Diffusers, see [the docs](https://huggingface.co/docs/diffusers/using-diffusers/conditional_image_generation). 1. Installation ``` pip install diffusers transformers accelerate ``` 2. Run ```py from diffusers import AutoPipelineForText2Image, DEISMultistepScheduler import torch pipe = AutoPipelineForText2Image.from_pretrained('lykon/dreamshaper-7', torch_dtype=torch.float16, variant="fp16") pipe.scheduler = DEISMultistepScheduler.from_config(pipe.scheduler.config) pipe = pipe.to("cuda") prompt = "portrait photo of muscular bearded guy in a worn mech suit, light bokeh, intricate, steel metal, elegant, sharp focus, soft lighting, vibrant colors" generator = torch.manual_seed(33) image = pipe(prompt, generator=generator, num_inference_steps=25).images[0] image.save("./image.png") ``` ![](./image.png) ## Notes - **Version 8** focuses on improving what V7 started. Might be harder to do photorealism compared to realism focused models, as it might be hard to do anime compared to anime focused models, but it can do both pretty well if you're skilled enough. Check the examples! - **Version 7** improves lora support, NSFW and realism. If you're interested in "absolute" realism, try AbsoluteReality. - **Version 6** adds more lora support and more style in general. It should also be better at generating directly at 1024 height (but be careful with it). 6.x are all improvements. - **Version 5** is the best at photorealism and has noise offset. - **Version 4** is much better with anime (can do them with no LoRA) and booru tags. It might be harder to control if you're used to caption style, so you might still want to use version 3.31. V4 is also better with eyes at lower resolutions. Overall is like a "fix" of V3 and shouldn't be too much different.
TheBloke/deepseek-coder-33B-instruct-GGUF
TheBloke
"2023-11-05T16:52:39Z"
23,734
153
transformers
[ "transformers", "gguf", "deepseek", "base_model:deepseek-ai/deepseek-coder-33b-instruct", "license:other", "region:us" ]
null
"2023-11-04T22:04:16Z"
--- base_model: deepseek-ai/deepseek-coder-33b-instruct inference: false license: other license_link: LICENSE license_name: deepseek model_creator: DeepSeek model_name: Deepseek Coder 33B Instruct model_type: deepseek prompt_template: 'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer. ### Instruction: {prompt} ### Response: ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Deepseek Coder 33B Instruct - GGUF - Model creator: [DeepSeek](https://huggingface.co/deepseek-ai) - Original model: [Deepseek Coder 33B Instruct](https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct) <!-- description start --> ## Description This repo contains GGUF format model files for [DeepSeek's Deepseek Coder 33B Instruct](https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/deepseek-coder-33B-instruct-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/deepseek-coder-33B-instruct-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/deepseek-coder-33B-instruct-GGUF) * [DeepSeek's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: DeepSeek ``` You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer. ### Instruction: {prompt} ### Response: ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [deepseek-coder-33b-instruct.Q2_K.gguf](https://huggingface.co/TheBloke/deepseek-coder-33B-instruct-GGUF/blob/main/deepseek-coder-33b-instruct.Q2_K.gguf) | Q2_K | 2 | 14.03 GB| 16.53 GB | smallest, significant quality loss - not recommended for most purposes | | [deepseek-coder-33b-instruct.Q3_K_S.gguf](https://huggingface.co/TheBloke/deepseek-coder-33B-instruct-GGUF/blob/main/deepseek-coder-33b-instruct.Q3_K_S.gguf) | Q3_K_S | 3 | 14.42 GB| 16.92 GB | very small, high quality loss | | [deepseek-coder-33b-instruct.Q3_K_M.gguf](https://huggingface.co/TheBloke/deepseek-coder-33B-instruct-GGUF/blob/main/deepseek-coder-33b-instruct.Q3_K_M.gguf) | Q3_K_M | 3 | 16.07 GB| 18.57 GB | very small, high quality loss | | [deepseek-coder-33b-instruct.Q3_K_L.gguf](https://huggingface.co/TheBloke/deepseek-coder-33B-instruct-GGUF/blob/main/deepseek-coder-33b-instruct.Q3_K_L.gguf) | Q3_K_L | 3 | 17.56 GB| 20.06 GB | small, substantial quality loss | | [deepseek-coder-33b-instruct.Q4_0.gguf](https://huggingface.co/TheBloke/deepseek-coder-33B-instruct-GGUF/blob/main/deepseek-coder-33b-instruct.Q4_0.gguf) | Q4_0 | 4 | 18.82 GB| 21.32 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [deepseek-coder-33b-instruct.Q4_K_S.gguf](https://huggingface.co/TheBloke/deepseek-coder-33B-instruct-GGUF/blob/main/deepseek-coder-33b-instruct.Q4_K_S.gguf) | Q4_K_S | 4 | 18.89 GB| 21.39 GB | small, greater quality loss | | [deepseek-coder-33b-instruct.Q4_K_M.gguf](https://huggingface.co/TheBloke/deepseek-coder-33B-instruct-GGUF/blob/main/deepseek-coder-33b-instruct.Q4_K_M.gguf) | Q4_K_M | 4 | 19.94 GB| 22.44 GB | medium, balanced quality - recommended | | [deepseek-coder-33b-instruct.Q5_0.gguf](https://huggingface.co/TheBloke/deepseek-coder-33B-instruct-GGUF/blob/main/deepseek-coder-33b-instruct.Q5_0.gguf) | Q5_0 | 5 | 22.96 GB| 25.46 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [deepseek-coder-33b-instruct.Q5_K_S.gguf](https://huggingface.co/TheBloke/deepseek-coder-33B-instruct-GGUF/blob/main/deepseek-coder-33b-instruct.Q5_K_S.gguf) | Q5_K_S | 5 | 22.96 GB| 25.46 GB | large, low quality loss - recommended | | [deepseek-coder-33b-instruct.Q5_K_M.gguf](https://huggingface.co/TheBloke/deepseek-coder-33B-instruct-GGUF/blob/main/deepseek-coder-33b-instruct.Q5_K_M.gguf) | Q5_K_M | 5 | 23.54 GB| 26.04 GB | large, very low quality loss - recommended | | [deepseek-coder-33b-instruct.Q6_K.gguf](https://huggingface.co/TheBloke/deepseek-coder-33B-instruct-GGUF/blob/main/deepseek-coder-33b-instruct.Q6_K.gguf) | Q6_K | 6 | 27.36 GB| 29.86 GB | very large, extremely low quality loss | | [deepseek-coder-33b-instruct.Q8_0.gguf](https://huggingface.co/TheBloke/deepseek-coder-33B-instruct-GGUF/blob/main/deepseek-coder-33b-instruct.Q8_0.gguf) | Q8_0 | 8 | 35.43 GB| 37.93 GB | very large, extremely low quality loss - not recommended | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/deepseek-coder-33B-instruct-GGUF and below it, a specific filename to download, such as: deepseek-coder-33b-instruct.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/deepseek-coder-33B-instruct-GGUF deepseek-coder-33b-instruct.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/deepseek-coder-33B-instruct-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/deepseek-coder-33B-instruct-GGUF deepseek-coder-33b-instruct.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m deepseek-coder-33b-instruct.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer.\n### Instruction:\n{prompt}\n### Response:" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model in Python code, using ctransformers #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install ctransformers # Or with CUDA GPU acceleration pip install ctransformers[cuda] # Or with AMD ROCm GPU acceleration (Linux only) CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems only CT_METAL=1 pip install ctransformers --no-binary ctransformers ``` #### Simple ctransformers example code ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/deepseek-coder-33B-instruct-GGUF", model_file="deepseek-coder-33b-instruct.Q4_K_M.gguf", model_type="deepseek", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: DeepSeek's Deepseek Coder 33B Instruct <p align="center"> <img width="1000px" alt="DeepSeek Coder" src="https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/logo.png?raw=true"> </p> <p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/guoday/assert/blob/main/QR.png?raw=true">[Wechat(微信)]</a> </p> <hr> ### 1. Introduction of Deepseek Coder Deepseek Coder is composed of a series of code language models, each trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on project-level code corpus by employing a window size of 16K and a extra fill-in-the-blank task, to support project-level code completion and infilling. For coding capabilities, Deepseek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks. - **Massive Training Data**: Trained from scratch on 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages. - **Highly Flexible & Scalable**: Offered in model sizes of 1.3B, 5.7B, 6.7B, and 33B, enabling users to choose the setup most suitable for their requirements. - **Superior Model Performance**: State-of-the-art performance among publicly available code models on HumanEval, MultiPL-E, MBPP, DS-1000, and APPS benchmarks. - **Advanced Code Completion Capabilities**: A window size of 16K and a fill-in-the-blank task, supporting project-level code completion and infilling tasks. ### 2. Model Summary deepseek-coder-33b-instruct is a 33B parameter model initialized from deepseek-coder-33b-base and fine-tuned on 2B tokens of instruction data. - **Home Page:** [DeepSeek](https://deepseek.com/) - **Repository:** [deepseek-ai/deepseek-coder](https://github.com/deepseek-ai/deepseek-coder) - **Chat With DeepSeek Coder:** [DeepSeek-Coder](https://coder.deepseek.com/) ### 3. How to Use Here give some examples of how to use our model. #### Chat Model Inference ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-33b-instruct", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-33b-instruct", trust_remote_code=True).cuda() messages=[ { 'role': 'user', 'content': "write a quick sort algorithm in python."} ] inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device) # 32021 is the id of <|EOT|> token outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=32021) print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)) ``` ### 4. License This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use. See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) for more details. ### 5. Contact If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]). <!-- original-model-card end -->
nboost/pt-tinybert-msmarco
nboost
"2021-05-20T01:28:00Z"
23,723
0
transformers
[ "transformers", "pytorch", "jax", "bert", "endpoints_compatible", "region:us" ]
null
"2022-03-02T23:29:05Z"
Entry not found
anton-l/wav2vec2-large-xlsr-53-romanian
anton-l
"2021-07-05T20:20:21Z"
23,697
2
transformers
[ "transformers", "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "xlsr-fine-tuning-week", "ro", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
"2022-03-02T23:29:05Z"
--- language: ro datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Romanian XLSR Wav2Vec2 Large 53 by Anton Lozhkov results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice ro type: common_voice args: ro metrics: - name: Test WER type: wer value: 24.84 --- # Wav2Vec2-Large-XLSR-53-Romanian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Romanian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "ro", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-romanian") model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-romanian") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Romanian test data of Common Voice. ```python import torch import torchaudio import urllib.request import tarfile import pandas as pd from tqdm.auto import tqdm from datasets import load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor # Download the raw data instead of using HF datasets to save disk space data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/ro.tar.gz" filestream = urllib.request.urlopen(data_url) data_file = tarfile.open(fileobj=filestream, mode="r|gz") data_file.extractall() wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-romanian") model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-romanian") model.to("cuda") cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/ro/test.tsv", sep='\t') clips_path = "cv-corpus-6.1-2020-12-11/ro/clips/" def clean_sentence(sent): sent = sent.lower() # replace non-alpha characters with space sent = "".join(ch if ch.isalpha() else " " for ch in sent) # remove repeated spaces sent = " ".join(sent.split()) return sent targets = [] preds = [] for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]): row["sentence"] = clean_sentence(row["sentence"]) speech_array, sampling_rate = torchaudio.load(clips_path + row["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) row["speech"] = resampler(speech_array).squeeze().numpy() inputs = processor(row["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) targets.append(row["sentence"]) preds.append(processor.batch_decode(pred_ids)[0]) print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets))) ``` **Test Result**: 24.84 % ## Training The Common Voice `train` and `validation` datasets were used for training.
mradermacher/Yi-9B-Coder-GGUF
mradermacher
"2024-06-27T16:32:31Z"
23,691
0
transformers
[ "transformers", "gguf", "code", "llama", "en", "base_model:TechxGenus/Yi-9B-Coder", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
"2024-06-27T16:00:56Z"
--- base_model: TechxGenus/Yi-9B-Coder language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - code - llama --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/TechxGenus/Yi-9B-Coder <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Yi-9B-Coder-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Yi-9B-Coder-GGUF/resolve/main/Yi-9B-Coder.Q2_K.gguf) | Q2_K | 3.5 | | | [GGUF](https://huggingface.co/mradermacher/Yi-9B-Coder-GGUF/resolve/main/Yi-9B-Coder.IQ3_XS.gguf) | IQ3_XS | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/Yi-9B-Coder-GGUF/resolve/main/Yi-9B-Coder.Q3_K_S.gguf) | Q3_K_S | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/Yi-9B-Coder-GGUF/resolve/main/Yi-9B-Coder.IQ3_S.gguf) | IQ3_S | 4.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Yi-9B-Coder-GGUF/resolve/main/Yi-9B-Coder.IQ3_M.gguf) | IQ3_M | 4.2 | | | [GGUF](https://huggingface.co/mradermacher/Yi-9B-Coder-GGUF/resolve/main/Yi-9B-Coder.Q3_K_M.gguf) | Q3_K_M | 4.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Yi-9B-Coder-GGUF/resolve/main/Yi-9B-Coder.Q3_K_L.gguf) | Q3_K_L | 4.8 | | | [GGUF](https://huggingface.co/mradermacher/Yi-9B-Coder-GGUF/resolve/main/Yi-9B-Coder.IQ4_XS.gguf) | IQ4_XS | 4.9 | | | [GGUF](https://huggingface.co/mradermacher/Yi-9B-Coder-GGUF/resolve/main/Yi-9B-Coder.Q4_K_S.gguf) | Q4_K_S | 5.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Yi-9B-Coder-GGUF/resolve/main/Yi-9B-Coder.Q4_K_M.gguf) | Q4_K_M | 5.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Yi-9B-Coder-GGUF/resolve/main/Yi-9B-Coder.Q5_K_S.gguf) | Q5_K_S | 6.2 | | | [GGUF](https://huggingface.co/mradermacher/Yi-9B-Coder-GGUF/resolve/main/Yi-9B-Coder.Q5_K_M.gguf) | Q5_K_M | 6.4 | | | [GGUF](https://huggingface.co/mradermacher/Yi-9B-Coder-GGUF/resolve/main/Yi-9B-Coder.Q6_K.gguf) | Q6_K | 7.3 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Yi-9B-Coder-GGUF/resolve/main/Yi-9B-Coder.Q8_0.gguf) | Q8_0 | 9.5 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Yi-9B-Coder-GGUF/resolve/main/Yi-9B-Coder.f16.gguf) | f16 | 17.8 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
hf-tiny-model-private/tiny-random-BloomForCausalLM
hf-tiny-model-private
"2023-03-29T18:37:17Z"
23,683
0
transformers
[ "transformers", "pytorch", "bloom", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2023-03-29T18:37:12Z"
Entry not found
stabilityai/stablelm-2-zephyr-1_6b
stabilityai
"2024-06-03T15:16:39Z"
23,683
173
transformers
[ "transformers", "safetensors", "gguf", "stablelm", "text-generation", "causal-lm", "conversational", "en", "dataset:HuggingFaceH4/ultrachat_200k", "dataset:allenai/ultrafeedback_binarized_cleaned", "dataset:meta-math/MetaMathQA", "dataset:WizardLM/WizardLM_evol_instruct_V2_196k", "dataset:openchat/openchat_sharegpt4_dataset", "dataset:LDJnr/Capybara", "dataset:Intel/orca_dpo_pairs", "dataset:hkust-nlp/deita-10k-v0", "arxiv:2305.18290", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
"2024-01-19T15:50:04Z"
--- datasets: - HuggingFaceH4/ultrachat_200k - allenai/ultrafeedback_binarized_cleaned - meta-math/MetaMathQA - WizardLM/WizardLM_evol_instruct_V2_196k - openchat/openchat_sharegpt4_dataset - LDJnr/Capybara - Intel/orca_dpo_pairs - hkust-nlp/deita-10k-v0 language: - en tags: - causal-lm extra_gated_fields: Name: text Email: text Country: text Organization or Affiliation: text I ALLOW Stability AI to email me about new model releases: checkbox license: other --- # `StableLM 2 Zephyr 1.6B` ## Model Description `Stable LM 2 Zephyr 1.6B` is a 1.6 billion parameter instruction tuned language model inspired by [HugginFaceH4's Zephyr 7B](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) training pipeline. The model is trained on a mix of publicly available datasets and synthetic datasets, utilizing [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). ## Usage `StableLM 2 Zephyr 1.6B` uses the following instruction format: ``` <|user|> Which famous math number begins with 1.6 ...?<|endoftext|> <|assistant|> The number you are referring to is 1.618033988749895. This is the famous value known as the golden ratio<|endoftext|> ``` This format is also available through the tokenizer's `apply_chat_template` method: ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('stabilityai/stablelm-2-zephyr-1_6b') model = AutoModelForCausalLM.from_pretrained( 'stabilityai/stablelm-2-zephyr-1_6b', device_map="auto" ) prompt = [{'role': 'user', 'content': 'Which famous math number begins with 1.6 ...?'}] inputs = tokenizer.apply_chat_template( prompt, add_generation_prompt=True, return_tensors='pt' ) tokens = model.generate( inputs.to(model.device), max_new_tokens=1024, temperature=0.5, do_sample=True ) print(tokenizer.decode(tokens[0], skip_special_tokens=False)) ``` ## Model Details * **Developed by**: [Stability AI](https://stability.ai/) * **Model type**: `StableLM 2 Zephyr 1.6B` model is an auto-regressive language model based on the transformer decoder architecture. * **Language(s)**: English * **Paper**: [Stable LM 2 1.6B Technical Report](https://drive.google.com/file/d/1JYJHszhS8EFChTbNAf8xmqhKjogWRrQF/view?usp=sharing) * **Library**: [Alignment Handbook](https://github.com/huggingface/alignment-handbook.git) * **Finetuned from model**: [https://huggingface.co/stabilityai/stablelm-2-1_6b](https://huggingface.co/stabilityai/stablelm-2-1_6b) * **License**: [StabilityAI Non-Commercial Research Community License](https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b/blob/main/LICENSE). If you want to use this model for your commercial products or purposes, please contact us [here](https://stability.ai/contact) to learn more. * **Contact**: For questions and comments about the model, please email `[email protected]` ### Training Dataset The dataset is comprised of a mixture of open datasets large-scale datasets available on the [HuggingFace Hub](https://huggingface.co/datasets): 1. SFT Datasets - HuggingFaceH4/ultrachat_200k - meta-math/MetaMathQA - WizardLM/WizardLM_evol_instruct_V2_196k - Open-Orca/SlimOrca - openchat/openchat_sharegpt4_dataset - LDJnr/Capybara - hkust-nlp/deita-10k-v0 2. Preference Datasets: - allenai/ultrafeedback_binarized_cleaned - Intel/orca_dpo_pairs ## Performance ### MT-Bench <img src="https://cdn-uploads.huggingface.co/production/uploads/61b2bf4f5b1f7cad1799cfbb/QH00HVM3lg-5f17U_py4K.png" alt="mt_bench_plot" width="600"/> | Model | Size | MT-Bench | |-------------------------|------|----------| | Mistral-7B-Instruct-v0.2| 7B | 7.61 | | Llama2-Chat | 70B | 6.86 | | stablelm-zephyr-3b | 3B | 6.64 | | MPT-30B-Chat | 30B | 6.39 | | **stablelm-2-zephyr-1.6b** | 1.6B | 5.42 | | Falcon-40B-Instruct | 40B | 5.17 | | Qwen-1.8B-Chat | 1.8B | 4.95 | | dolphin-2.6-phi-2 | 2.7B | 4.93 | | phi-2 | 2.7B | 4.29 | | TinyLlama-1.1B-Chat-v1.0| 1.1B | 3.46 | ### OpenLLM Leaderboard | Model | Size | Average | ARC Challenge (acc_norm) | HellaSwag (acc_norm) | MMLU (acc_norm) | TruthfulQA (mc2) | Winogrande (acc) | Gsm8k (acc) | |----------------------------------------|------|---------|-------------------------|----------------------|-----------------|------------------|------------------|-------------| | microsoft/phi-2 | 2.7B | 61.32% | 61.09% | 75.11% | 58.11% | 44.47% | 74.35% | 54.81% | | **stabilityai/stablelm-2-zephyr-1_6b** | 1.6B | 49.89% | 43.69% | 69.34% | 41.85% | 45.21% | 64.09% | 35.18% | | microsoft/phi-1_5 | 1.3B | 47.69% | 52.90% | 63.79% | 43.89% | 40.89% | 72.22% | 12.43% | | stabilityai/stablelm-2-1_6b | 1.6B | 45.54% | 43.43% | 70.49% | 38.93% | 36.65% | 65.90% | 17.82% | | mosaicml/mpt-7b | 7B | 44.28% | 47.70% | 77.57% | 30.80% | 33.40% | 72.14% | 4.02% | | KnutJaegersberg/Qwen-1_8B-Llamaified* | 1.8B | 44.75% | 37.71% | 58.87% | 46.37% | 39.41% | 61.72% | 24.41% | | openlm-research/open_llama_3b_v2 | 3B | 40.28% | 40.27% | 71.60% | 27.12% | 34.78% | 67.01% | 0.91% | | iiuae/falcon-rw-1b | 1B | 37.07% | 35.07% | 63.56% | 25.28% | 35.96% | 62.04% | 0.53% | | TinyLlama/TinyLlama-1.1B-3T | 1.1B | 36.40% | 33.79% | 60.31% | 26.04% | 37.32% | 59.51% | 1.44% | ### Training Infrastructure * **Hardware**: `StableLM 2 Zephyr 1.6B` was trained on the Stability AI cluster across 8 nodes with 8 A100 80GBs GPUs for each nodes. * **Code Base**: We use our internal script for SFT steps and used [HuggingFace Alignment Handbook script](https://github.com/huggingface/alignment-handbook) for DPO training. ## Use and Limitations ### Intended Use The model is intended to be used in chat-like applications. Developers must evaluate the model for safety performance in their specific use case. Read more about [safety and limitations](#limitations-and-bias) below. ### Limitations and Bias ​ This model is not trained against adversarial inputs. We strongly recommend pairing this model with an input and output classifier to prevent harmful responses. Through our internal red teaming, we discovered that while the model will not output harmful information if not prompted to do so, it will hallucinate many facts. It is also willing to output potentially harmful outputs or misinformation when the user requests it. Using this model will require guardrails around your inputs and outputs to ensure that any outputs returned are not misinformation or harmful. Additionally, as each use case is unique, we recommend running your own suite of tests to ensure proper performance of this model. Finally, do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others. ## How to Cite ```bibtex @misc{StableLM-2-1.6B, url={[https://huggingface.co/stabilityai/stablelm-2-1.6b](https://huggingface.co/stabilityai/stablelm-2-1.6b)}, title={Stable LM 2 1.6B}, author={Stability AI Language Team} } ```
hf-tiny-model-private/tiny-random-GPTNeoForCausalLM
hf-tiny-model-private
"2023-03-29T18:57:18Z"
23,675
0
transformers
[ "transformers", "pytorch", "gpt_neo", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
"2023-03-29T18:57:13Z"
Entry not found
hf-tiny-model-private/tiny-random-GPTJForCausalLM
hf-tiny-model-private
"2023-03-29T18:58:06Z"
23,664
1
transformers
[ "transformers", "pytorch", "tf", "gptj", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
"2023-03-29T18:58:02Z"
Entry not found
mradermacher/copy_of_wildjailbreak_13-i1-GGUF
mradermacher
"2024-07-01T12:24:59Z"
23,575
0
transformers
[ "transformers", "gguf", "en", "base_model:larenspear/copy_of_wildjailbreak_13", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
"2024-07-01T10:05:21Z"
--- base_model: larenspear/copy_of_wildjailbreak_13 extra_gated_fields: Contact email: text I agree that AI2 may use my information as described in the Privacy Policy: checkbox I agree to use this model for research purposes in accordance with the AI2 Responsible Use Guidelines: checkbox I certify that the information I have provided is true and accurate: checkbox I understand that this model is a research artifact that may contain or produce unfiltered, toxic, or harmful material: checkbox Organization or entity you are affiliated with: text Please describe your intended use of the low risk artifact(s): text State or country you are located in: text Your full name: text extra_gated_prompt: Access to this model is automatically granted upon accepting the [AI2 Responsible Use Guidelines](https://allenai.org/responsible-use.pdf), and completing all fields below language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/larenspear/copy_of_wildjailbreak_13 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-i1-GGUF/resolve/main/copy_of_wildjailbreak_13.i1-IQ1_S.gguf) | i1-IQ1_S | 3.0 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-i1-GGUF/resolve/main/copy_of_wildjailbreak_13.i1-IQ1_M.gguf) | i1-IQ1_M | 3.2 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-i1-GGUF/resolve/main/copy_of_wildjailbreak_13.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-i1-GGUF/resolve/main/copy_of_wildjailbreak_13.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-i1-GGUF/resolve/main/copy_of_wildjailbreak_13.i1-IQ2_S.gguf) | i1-IQ2_S | 4.3 | | | [GGUF](https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-i1-GGUF/resolve/main/copy_of_wildjailbreak_13.i1-IQ2_M.gguf) | i1-IQ2_M | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-i1-GGUF/resolve/main/copy_of_wildjailbreak_13.i1-Q2_K.gguf) | i1-Q2_K | 5.0 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-i1-GGUF/resolve/main/copy_of_wildjailbreak_13.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-i1-GGUF/resolve/main/copy_of_wildjailbreak_13.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-i1-GGUF/resolve/main/copy_of_wildjailbreak_13.i1-IQ3_S.gguf) | i1-IQ3_S | 5.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-i1-GGUF/resolve/main/copy_of_wildjailbreak_13.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-i1-GGUF/resolve/main/copy_of_wildjailbreak_13.i1-IQ3_M.gguf) | i1-IQ3_M | 6.1 | | | [GGUF](https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-i1-GGUF/resolve/main/copy_of_wildjailbreak_13.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.4 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-i1-GGUF/resolve/main/copy_of_wildjailbreak_13.i1-Q3_K_L.gguf) | i1-Q3_K_L | 7.0 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-i1-GGUF/resolve/main/copy_of_wildjailbreak_13.i1-IQ4_XS.gguf) | i1-IQ4_XS | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-i1-GGUF/resolve/main/copy_of_wildjailbreak_13.i1-Q4_0.gguf) | i1-Q4_0 | 7.5 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-i1-GGUF/resolve/main/copy_of_wildjailbreak_13.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.5 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-i1-GGUF/resolve/main/copy_of_wildjailbreak_13.i1-Q4_K_M.gguf) | i1-Q4_K_M | 8.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-i1-GGUF/resolve/main/copy_of_wildjailbreak_13.i1-Q5_K_S.gguf) | i1-Q5_K_S | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-i1-GGUF/resolve/main/copy_of_wildjailbreak_13.i1-Q5_K_M.gguf) | i1-Q5_K_M | 9.3 | | | [GGUF](https://huggingface.co/mradermacher/copy_of_wildjailbreak_13-i1-GGUF/resolve/main/copy_of_wildjailbreak_13.i1-Q6_K.gguf) | i1-Q6_K | 10.8 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
stabilityai/stablelm-3b-4e1t
stabilityai
"2024-03-07T18:18:43Z"
23,545
307
transformers
[ "transformers", "safetensors", "stablelm", "text-generation", "causal-lm", "en", "dataset:tiiuae/falcon-refinedweb", "dataset:togethercomputer/RedPajama-Data-1T", "dataset:CarperAI/pilev2-dev", "dataset:bigcode/starcoderdata", "dataset:allenai/peS2o", "arxiv:2307.09288", "arxiv:2104.09864", "arxiv:2204.06745", "arxiv:1607.06450", "arxiv:1910.07467", "arxiv:2101.00027", "arxiv:2305.06161", "arxiv:1910.02054", "license:cc-by-sa-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
"2023-09-29T06:05:21Z"
--- language: - en license: cc-by-sa-4.0 tags: - causal-lm datasets: - tiiuae/falcon-refinedweb - togethercomputer/RedPajama-Data-1T - CarperAI/pilev2-dev - bigcode/starcoderdata - allenai/peS2o model-index: - name: stablelm-3b-4e1t results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 46.59 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-3b-4e1t name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 75.94 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-3b-4e1t name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 45.23 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-3b-4e1t name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 37.2 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-3b-4e1t name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 71.19 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-3b-4e1t name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 3.34 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-3b-4e1t name: Open LLM Leaderboard --- # `StableLM-3B-4E1T` ## Model Description `StableLM-3B-4E1T` is a 3 billion parameter decoder-only language model pre-trained on 1 trillion tokens of diverse English and code datasets for 4 epochs. ## Usage Get started generating text with `StableLM-3B-4E1T` by using the following code snippet: ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t") model = AutoModelForCausalLM.from_pretrained( "stabilityai/stablelm-3b-4e1t", torch_dtype="auto", ) model.cuda() inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device) tokens = model.generate( **inputs, max_new_tokens=64, temperature=0.75, top_p=0.95, do_sample=True, ) print(tokenizer.decode(tokens[0], skip_special_tokens=True)) ``` ### Run with Flash Attention 2 ⚡️ <details> <summary> Click to expand </summary> ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t") model = AutoModelForCausalLM.from_pretrained( "stabilityai/stablelm-3b-4e1t", torch_dtype="auto", attn_implementation="flash_attention_2", ) model.cuda() inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device) tokens = model.generate( **inputs, max_new_tokens=64, temperature=0.75, top_p=0.95, do_sample=True, ) print(tokenizer.decode(tokens[0], skip_special_tokens=True)) ``` </details> ## Model Details * **Developed by**: [Stability AI](https://stability.ai/) * **Model type**: `StableLM-3B-4E1T` models are auto-regressive language models based on the transformer decoder architecture. * **Language(s)**: English * **Library**: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) * **License**: Model checkpoints are licensed under the Creative Commons license ([CC BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/)). Under this license, you must give [credit](https://creativecommons.org/licenses/by/4.0/#) to Stability AI, provide a link to the license, and [indicate if changes were made](https://creativecommons.org/licenses/by/4.0/#). You may do so in any reasonable manner, but not in any way that suggests the Stability AI endorses you or your use. * **Contact**: For questions and comments about the model, please email `[email protected]` ### Model Architecture The model is a decoder-only transformer similar to the LLaMA ([Touvron et al., 2023](https://arxiv.org/abs/2307.09288)) architecture with the following modifications: | Parameters | Hidden Size | Layers | Heads | Sequence Length | |----------------|-------------|--------|-------|-----------------| | 2,795,443,200 | 2560 | 32 | 32 | 4096 | * **Position Embeddings**: Rotary Position Embeddings ([Su et al., 2021](https://arxiv.org/abs/2104.09864)) applied to the first 25% of head embedding dimensions for improved throughput following [Black et al. (2022)](https://arxiv.org/pdf/2204.06745.pdf). * **Normalization**: LayerNorm ([Ba et al., 2016](https://arxiv.org/abs/1607.06450)) with learned bias terms as opposed to RMSNorm ([Zhang & Sennrich, 2019](https://arxiv.org/abs/1910.07467)). * **Tokenizer**: GPT-NeoX ([Black et al., 2022](https://arxiv.org/abs/2204.06745)). ## Training For complete dataset and training details, please see the [StableLM-3B-4E1T Technical Report](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo). ### Training Dataset The dataset is comprised of a filtered mixture of open-source large-scale datasets available on the [HuggingFace Hub](https://huggingface.co/datasets): Falcon RefinedWeb extract ([Penedo et al., 2023](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)), RedPajama-Data ([Together Computer., 2023](https://github.com/togethercomputer/RedPajama-Data)) and The Pile ([Gao et al., 2020](https://arxiv.org/abs/2101.00027)) both without the *Books3* subset, and StarCoder ([Li et al., 2023](https://arxiv.org/abs/2305.06161)). * Given the large amount of web data, we recommend fine-tuning the base StableLM-3B-4E1T for your downstream tasks. ### Training Procedure The model is pre-trained on the aforementioned datasets in `bfloat16` precision, optimized with AdamW, and trained using the NeoX tokenizer with a vocabulary size of 50,257. We outline the complete hyperparameters choices in the project's [GitHub repository - config](https://github.com/Stability-AI/StableLM/blob/main/configs/stablelm-3b-4e1t.yml). ### Training Infrastructure * **Hardware**: `StableLM-3B-4E1T` was trained on the Stability AI cluster across 256 NVIDIA A100 40GB GPUs (AWS P4d instances). Training began on August 23, 2023, and took approximately 30 days to complete. * **Software**: We use a fork of `gpt-neox` ([EleutherAI, 2021](https://github.com/EleutherAI/gpt-neox)), train under 2D parallelism (Data and Tensor Parallel) with ZeRO-1 ([Rajbhandari et al., 2019](https://arxiv.org/abs/1910.02054v3)), and rely on flash-attention as well as SwiGLU and Rotary Embedding kernels from FlashAttention-2 ([Dao et al., 2023](https://tridao.me/publications/flash2/flash2.pdf)) ## Use and Limitations ### Intended Use The model is intended to be used as a foundational base model for application-specific fine-tuning. Developers must evaluate and fine-tune the model for safe performance in downstream applications. ### Limitations and Bias ​ As a base model, this model may exhibit unreliable, unsafe, or other undesirable behaviors that must be corrected through evaluation and fine-tuning prior to deployment. The pre-training dataset may have contained offensive or inappropriate content, even after applying data cleansing filters, which can be reflected in the model-generated text. We recommend that users exercise caution when using these models in production systems. Do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others. ## How to Cite ```bibtex @misc{StableLM-3B-4E1T, url={[https://huggingface.co/stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t)}, title={StableLM 3B 4E1T}, author={Tow, Jonathan and Bellagente, Marco and Mahan, Dakota and Riquelme, Carlos} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_stabilityai__stablelm-3b-4e1t) | Metric |Value| |---------------------------------|----:| |Avg. |46.58| |AI2 Reasoning Challenge (25-Shot)|46.59| |HellaSwag (10-Shot) |75.94| |MMLU (5-Shot) |45.23| |TruthfulQA (0-shot) |37.20| |Winogrande (5-shot) |71.19| |GSM8k (5-shot) | 3.34|
trl-internal-testing/tiny-T5ForConditionalGeneration-correct-vocab
trl-internal-testing
"2023-02-08T15:33:10Z"
23,505
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text2text-generation
"2023-02-08T15:32:51Z"
Entry not found
timm/resnet18d.ra2_in1k
timm
"2024-02-10T23:38:44Z"
23,430
0
timm
[ "timm", "pytorch", "safetensors", "image-classification", "arxiv:2110.00476", "arxiv:1512.03385", "arxiv:1812.01187", "license:apache-2.0", "region:us" ]
image-classification
"2023-04-05T18:04:23Z"
--- license: apache-2.0 library_name: timm tags: - image-classification - timm --- # Model card for resnet18d.ra2_in1k A ResNet-D image classification model. This model features: * ReLU activations * 3-layer stem of 3x3 convolutions with pooling * 2x2 average pool + 1x1 convolution shortcut downsample Trained on ImageNet-1k in `timm` using recipe template described below. Recipe details: * RandAugment `RA2` recipe. Inspired by and evolved from EfficientNet RandAugment recipes. Published as `B` recipe in [ResNet Strikes Back](https://arxiv.org/abs/2110.00476). * RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging * Step (exponential decay w/ staircase) LR schedule with warmup ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 11.7 - GMACs: 2.1 - Activations (M): 3.3 - Image size: train = 224 x 224, test = 288 x 288 - **Papers:** - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 - Bag of Tricks for Image Classification with Convolutional Neural Networks: https://arxiv.org/abs/1812.01187 - **Original:** https://github.com/huggingface/pytorch-image-models ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('resnet18d.ra2_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'resnet18d.ra2_in1k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 64, 112, 112]) # torch.Size([1, 64, 56, 56]) # torch.Size([1, 128, 28, 28]) # torch.Size([1, 256, 14, 14]) # torch.Size([1, 512, 7, 7]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'resnet18d.ra2_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 512, 7, 7) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). |model |img_size|top1 |top5 |param_count|gmacs|macts|img/sec| |------------------------------------------|--------|-----|-----|-----------|-----|-----|-------| |[seresnextaa101d_32x8d.sw_in12k_ft_in1k_288](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k_288)|320 |86.72|98.17|93.6 |35.2 |69.7 |451 | |[seresnextaa101d_32x8d.sw_in12k_ft_in1k_288](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k_288)|288 |86.51|98.08|93.6 |28.5 |56.4 |560 | |[seresnextaa101d_32x8d.sw_in12k_ft_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k)|288 |86.49|98.03|93.6 |28.5 |56.4 |557 | |[seresnextaa101d_32x8d.sw_in12k_ft_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k)|224 |85.96|97.82|93.6 |17.2 |34.2 |923 | |[resnext101_32x32d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x32d.fb_wsl_ig1b_ft_in1k)|224 |85.11|97.44|468.5 |87.3 |91.1 |254 | |[resnetrs420.tf_in1k](https://huggingface.co/timm/resnetrs420.tf_in1k)|416 |85.0 |97.12|191.9 |108.4|213.8|134 | |[ecaresnet269d.ra2_in1k](https://huggingface.co/timm/ecaresnet269d.ra2_in1k)|352 |84.96|97.22|102.1 |50.2 |101.2|291 | |[ecaresnet269d.ra2_in1k](https://huggingface.co/timm/ecaresnet269d.ra2_in1k)|320 |84.73|97.18|102.1 |41.5 |83.7 |353 | |[resnetrs350.tf_in1k](https://huggingface.co/timm/resnetrs350.tf_in1k)|384 |84.71|96.99|164.0 |77.6 |154.7|183 | |[seresnextaa101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.ah_in1k)|288 |84.57|97.08|93.6 |28.5 |56.4 |557 | |[resnetrs200.tf_in1k](https://huggingface.co/timm/resnetrs200.tf_in1k)|320 |84.45|97.08|93.2 |31.5 |67.8 |446 | |[resnetrs270.tf_in1k](https://huggingface.co/timm/resnetrs270.tf_in1k)|352 |84.43|96.97|129.9 |51.1 |105.5|280 | |[seresnext101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101d_32x8d.ah_in1k)|288 |84.36|96.92|93.6 |27.6 |53.0 |595 | |[seresnet152d.ra2_in1k](https://huggingface.co/timm/seresnet152d.ra2_in1k)|320 |84.35|97.04|66.8 |24.1 |47.7 |610 | |[resnetrs350.tf_in1k](https://huggingface.co/timm/resnetrs350.tf_in1k)|288 |84.3 |96.94|164.0 |43.7 |87.1 |333 | |[resnext101_32x8d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_swsl_ig1b_ft_in1k)|224 |84.28|97.17|88.8 |16.5 |31.2 |1100 | |[resnetrs420.tf_in1k](https://huggingface.co/timm/resnetrs420.tf_in1k)|320 |84.24|96.86|191.9 |64.2 |126.6|228 | |[seresnext101_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101_32x8d.ah_in1k)|288 |84.19|96.87|93.6 |27.2 |51.6 |613 | |[resnext101_32x16d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_wsl_ig1b_ft_in1k)|224 |84.18|97.19|194.0 |36.3 |51.2 |581 | |[resnetaa101d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa101d.sw_in12k_ft_in1k)|288 |84.11|97.11|44.6 |15.1 |29.0 |1144 | |[resnet200d.ra2_in1k](https://huggingface.co/timm/resnet200d.ra2_in1k)|320 |83.97|96.82|64.7 |31.2 |67.3 |518 | |[resnetrs200.tf_in1k](https://huggingface.co/timm/resnetrs200.tf_in1k)|256 |83.87|96.75|93.2 |20.2 |43.4 |692 | |[seresnextaa101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.ah_in1k)|224 |83.86|96.65|93.6 |17.2 |34.2 |923 | |[resnetrs152.tf_in1k](https://huggingface.co/timm/resnetrs152.tf_in1k)|320 |83.72|96.61|86.6 |24.3 |48.1 |617 | |[seresnet152d.ra2_in1k](https://huggingface.co/timm/seresnet152d.ra2_in1k)|256 |83.69|96.78|66.8 |15.4 |30.6 |943 | |[seresnext101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101d_32x8d.ah_in1k)|224 |83.68|96.61|93.6 |16.7 |32.0 |986 | |[resnet152d.ra2_in1k](https://huggingface.co/timm/resnet152d.ra2_in1k)|320 |83.67|96.74|60.2 |24.1 |47.7 |706 | |[resnetrs270.tf_in1k](https://huggingface.co/timm/resnetrs270.tf_in1k)|256 |83.59|96.61|129.9 |27.1 |55.8 |526 | |[seresnext101_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101_32x8d.ah_in1k)|224 |83.58|96.4 |93.6 |16.5 |31.2 |1013 | |[resnetaa101d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa101d.sw_in12k_ft_in1k)|224 |83.54|96.83|44.6 |9.1 |17.6 |1864 | |[resnet152.a1h_in1k](https://huggingface.co/timm/resnet152.a1h_in1k)|288 |83.46|96.54|60.2 |19.1 |37.3 |904 | |[resnext101_32x16d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_swsl_ig1b_ft_in1k)|224 |83.35|96.85|194.0 |36.3 |51.2 |582 | |[resnet200d.ra2_in1k](https://huggingface.co/timm/resnet200d.ra2_in1k)|256 |83.23|96.53|64.7 |20.0 |43.1 |809 | |[resnext101_32x4d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x4d.fb_swsl_ig1b_ft_in1k)|224 |83.22|96.75|44.2 |8.0 |21.2 |1814 | |[resnext101_64x4d.c1_in1k](https://huggingface.co/timm/resnext101_64x4d.c1_in1k)|288 |83.16|96.38|83.5 |25.7 |51.6 |590 | |[resnet152d.ra2_in1k](https://huggingface.co/timm/resnet152d.ra2_in1k)|256 |83.14|96.38|60.2 |15.4 |30.5 |1096 | |[resnet101d.ra2_in1k](https://huggingface.co/timm/resnet101d.ra2_in1k)|320 |83.02|96.45|44.6 |16.5 |34.8 |992 | |[ecaresnet101d.miil_in1k](https://huggingface.co/timm/ecaresnet101d.miil_in1k)|288 |82.98|96.54|44.6 |13.4 |28.2 |1077 | |[resnext101_64x4d.tv_in1k](https://huggingface.co/timm/resnext101_64x4d.tv_in1k)|224 |82.98|96.25|83.5 |15.5 |31.2 |989 | |[resnetrs152.tf_in1k](https://huggingface.co/timm/resnetrs152.tf_in1k)|256 |82.86|96.28|86.6 |15.6 |30.8 |951 | |[resnext101_32x8d.tv2_in1k](https://huggingface.co/timm/resnext101_32x8d.tv2_in1k)|224 |82.83|96.22|88.8 |16.5 |31.2 |1099 | |[resnet152.a1h_in1k](https://huggingface.co/timm/resnet152.a1h_in1k)|224 |82.8 |96.13|60.2 |11.6 |22.6 |1486 | |[resnet101.a1h_in1k](https://huggingface.co/timm/resnet101.a1h_in1k)|288 |82.8 |96.32|44.6 |13.0 |26.8 |1291 | |[resnet152.a1_in1k](https://huggingface.co/timm/resnet152.a1_in1k)|288 |82.74|95.71|60.2 |19.1 |37.3 |905 | |[resnext101_32x8d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_wsl_ig1b_ft_in1k)|224 |82.69|96.63|88.8 |16.5 |31.2 |1100 | |[resnet152.a2_in1k](https://huggingface.co/timm/resnet152.a2_in1k)|288 |82.62|95.75|60.2 |19.1 |37.3 |904 | |[resnetaa50d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa50d.sw_in12k_ft_in1k)|288 |82.61|96.49|25.6 |8.9 |20.6 |1729 | |[resnet61q.ra2_in1k](https://huggingface.co/timm/resnet61q.ra2_in1k)|288 |82.53|96.13|36.8 |9.9 |21.5 |1773 | |[wide_resnet101_2.tv2_in1k](https://huggingface.co/timm/wide_resnet101_2.tv2_in1k)|224 |82.5 |96.02|126.9 |22.8 |21.2 |1078 | |[resnext101_64x4d.c1_in1k](https://huggingface.co/timm/resnext101_64x4d.c1_in1k)|224 |82.46|95.92|83.5 |15.5 |31.2 |987 | |[resnet51q.ra2_in1k](https://huggingface.co/timm/resnet51q.ra2_in1k)|288 |82.36|96.18|35.7 |8.1 |20.9 |1964 | |[ecaresnet50t.ra2_in1k](https://huggingface.co/timm/ecaresnet50t.ra2_in1k)|320 |82.35|96.14|25.6 |8.8 |24.1 |1386 | |[resnet101.a1_in1k](https://huggingface.co/timm/resnet101.a1_in1k)|288 |82.31|95.63|44.6 |13.0 |26.8 |1291 | |[resnetrs101.tf_in1k](https://huggingface.co/timm/resnetrs101.tf_in1k)|288 |82.29|96.01|63.6 |13.6 |28.5 |1078 | |[resnet152.tv2_in1k](https://huggingface.co/timm/resnet152.tv2_in1k)|224 |82.29|96.0 |60.2 |11.6 |22.6 |1484 | |[wide_resnet50_2.racm_in1k](https://huggingface.co/timm/wide_resnet50_2.racm_in1k)|288 |82.27|96.06|68.9 |18.9 |23.8 |1176 | |[resnet101d.ra2_in1k](https://huggingface.co/timm/resnet101d.ra2_in1k)|256 |82.26|96.07|44.6 |10.6 |22.2 |1542 | |[resnet101.a2_in1k](https://huggingface.co/timm/resnet101.a2_in1k)|288 |82.24|95.73|44.6 |13.0 |26.8 |1290 | |[seresnext50_32x4d.racm_in1k](https://huggingface.co/timm/seresnext50_32x4d.racm_in1k)|288 |82.2 |96.14|27.6 |7.0 |23.8 |1547 | |[ecaresnet101d.miil_in1k](https://huggingface.co/timm/ecaresnet101d.miil_in1k)|224 |82.18|96.05|44.6 |8.1 |17.1 |1771 | |[resnext50_32x4d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext50_32x4d.fb_swsl_ig1b_ft_in1k)|224 |82.17|96.22|25.0 |4.3 |14.4 |2943 | |[ecaresnet50t.a1_in1k](https://huggingface.co/timm/ecaresnet50t.a1_in1k)|288 |82.12|95.65|25.6 |7.1 |19.6 |1704 | |[resnext50_32x4d.a1h_in1k](https://huggingface.co/timm/resnext50_32x4d.a1h_in1k)|288 |82.03|95.94|25.0 |7.0 |23.8 |1745 | |[ecaresnet101d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet101d_pruned.miil_in1k)|288 |82.0 |96.15|24.9 |5.8 |12.7 |1787 | |[resnet61q.ra2_in1k](https://huggingface.co/timm/resnet61q.ra2_in1k)|256 |81.99|95.85|36.8 |7.8 |17.0 |2230 | |[resnext101_32x8d.tv2_in1k](https://huggingface.co/timm/resnext101_32x8d.tv2_in1k)|176 |81.98|95.72|88.8 |10.3 |19.4 |1768 | |[resnet152.a1_in1k](https://huggingface.co/timm/resnet152.a1_in1k)|224 |81.97|95.24|60.2 |11.6 |22.6 |1486 | |[resnet101.a1h_in1k](https://huggingface.co/timm/resnet101.a1h_in1k)|224 |81.93|95.75|44.6 |7.8 |16.2 |2122 | |[resnet101.tv2_in1k](https://huggingface.co/timm/resnet101.tv2_in1k)|224 |81.9 |95.77|44.6 |7.8 |16.2 |2118 | |[resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k)|224 |81.84|96.1 |194.0 |36.3 |51.2 |583 | |[resnet51q.ra2_in1k](https://huggingface.co/timm/resnet51q.ra2_in1k)|256 |81.78|95.94|35.7 |6.4 |16.6 |2471 | |[resnet152.a2_in1k](https://huggingface.co/timm/resnet152.a2_in1k)|224 |81.77|95.22|60.2 |11.6 |22.6 |1485 | |[resnetaa50d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa50d.sw_in12k_ft_in1k)|224 |81.74|96.06|25.6 |5.4 |12.4 |2813 | |[ecaresnet50t.a2_in1k](https://huggingface.co/timm/ecaresnet50t.a2_in1k)|288 |81.65|95.54|25.6 |7.1 |19.6 |1703 | |[ecaresnet50d.miil_in1k](https://huggingface.co/timm/ecaresnet50d.miil_in1k)|288 |81.64|95.88|25.6 |7.2 |19.7 |1694 | |[resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k)|224 |81.62|96.04|88.8 |16.5 |31.2 |1101 | |[wide_resnet50_2.tv2_in1k](https://huggingface.co/timm/wide_resnet50_2.tv2_in1k)|224 |81.61|95.76|68.9 |11.4 |14.4 |1930 | |[resnetaa50.a1h_in1k](https://huggingface.co/timm/resnetaa50.a1h_in1k)|288 |81.61|95.83|25.6 |8.5 |19.2 |1868 | |[resnet101.a1_in1k](https://huggingface.co/timm/resnet101.a1_in1k)|224 |81.5 |95.16|44.6 |7.8 |16.2 |2125 | |[resnext50_32x4d.a1_in1k](https://huggingface.co/timm/resnext50_32x4d.a1_in1k)|288 |81.48|95.16|25.0 |7.0 |23.8 |1745 | |[gcresnet50t.ra2_in1k](https://huggingface.co/timm/gcresnet50t.ra2_in1k)|288 |81.47|95.71|25.9 |6.9 |18.6 |2071 | |[wide_resnet50_2.racm_in1k](https://huggingface.co/timm/wide_resnet50_2.racm_in1k)|224 |81.45|95.53|68.9 |11.4 |14.4 |1929 | |[resnet50d.a1_in1k](https://huggingface.co/timm/resnet50d.a1_in1k)|288 |81.44|95.22|25.6 |7.2 |19.7 |1908 | |[ecaresnet50t.ra2_in1k](https://huggingface.co/timm/ecaresnet50t.ra2_in1k)|256 |81.44|95.67|25.6 |5.6 |15.4 |2168 | |[ecaresnetlight.miil_in1k](https://huggingface.co/timm/ecaresnetlight.miil_in1k)|288 |81.4 |95.82|30.2 |6.8 |13.9 |2132 | |[resnet50d.ra2_in1k](https://huggingface.co/timm/resnet50d.ra2_in1k)|288 |81.37|95.74|25.6 |7.2 |19.7 |1910 | |[resnet101.a2_in1k](https://huggingface.co/timm/resnet101.a2_in1k)|224 |81.32|95.19|44.6 |7.8 |16.2 |2125 | |[seresnet50.ra2_in1k](https://huggingface.co/timm/seresnet50.ra2_in1k)|288 |81.3 |95.65|28.1 |6.8 |18.4 |1803 | |[resnext50_32x4d.a2_in1k](https://huggingface.co/timm/resnext50_32x4d.a2_in1k)|288 |81.3 |95.11|25.0 |7.0 |23.8 |1746 | |[seresnext50_32x4d.racm_in1k](https://huggingface.co/timm/seresnext50_32x4d.racm_in1k)|224 |81.27|95.62|27.6 |4.3 |14.4 |2591 | |[ecaresnet50t.a1_in1k](https://huggingface.co/timm/ecaresnet50t.a1_in1k)|224 |81.26|95.16|25.6 |4.3 |11.8 |2823 | |[gcresnext50ts.ch_in1k](https://huggingface.co/timm/gcresnext50ts.ch_in1k)|288 |81.23|95.54|15.7 |4.8 |19.6 |2117 | |[senet154.gluon_in1k](https://huggingface.co/timm/senet154.gluon_in1k)|224 |81.23|95.35|115.1 |20.8 |38.7 |545 | |[resnet50.a1_in1k](https://huggingface.co/timm/resnet50.a1_in1k)|288 |81.22|95.11|25.6 |6.8 |18.4 |2089 | |[resnet50_gn.a1h_in1k](https://huggingface.co/timm/resnet50_gn.a1h_in1k)|288 |81.22|95.63|25.6 |6.8 |18.4 |676 | |[resnet50d.a2_in1k](https://huggingface.co/timm/resnet50d.a2_in1k)|288 |81.18|95.09|25.6 |7.2 |19.7 |1908 | |[resnet50.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnet50.fb_swsl_ig1b_ft_in1k)|224 |81.18|95.98|25.6 |4.1 |11.1 |3455 | |[resnext50_32x4d.tv2_in1k](https://huggingface.co/timm/resnext50_32x4d.tv2_in1k)|224 |81.17|95.34|25.0 |4.3 |14.4 |2933 | |[resnext50_32x4d.a1h_in1k](https://huggingface.co/timm/resnext50_32x4d.a1h_in1k)|224 |81.1 |95.33|25.0 |4.3 |14.4 |2934 | |[seresnet50.a2_in1k](https://huggingface.co/timm/seresnet50.a2_in1k)|288 |81.1 |95.23|28.1 |6.8 |18.4 |1801 | |[seresnet50.a1_in1k](https://huggingface.co/timm/seresnet50.a1_in1k)|288 |81.1 |95.12|28.1 |6.8 |18.4 |1799 | |[resnet152s.gluon_in1k](https://huggingface.co/timm/resnet152s.gluon_in1k)|224 |81.02|95.41|60.3 |12.9 |25.0 |1347 | |[resnet50.d_in1k](https://huggingface.co/timm/resnet50.d_in1k)|288 |80.97|95.44|25.6 |6.8 |18.4 |2085 | |[gcresnet50t.ra2_in1k](https://huggingface.co/timm/gcresnet50t.ra2_in1k)|256 |80.94|95.45|25.9 |5.4 |14.7 |2571 | |[resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k)|224 |80.93|95.73|44.2 |8.0 |21.2 |1814 | |[resnet50.c1_in1k](https://huggingface.co/timm/resnet50.c1_in1k)|288 |80.91|95.55|25.6 |6.8 |18.4 |2084 | |[seresnext101_32x4d.gluon_in1k](https://huggingface.co/timm/seresnext101_32x4d.gluon_in1k)|224 |80.9 |95.31|49.0 |8.0 |21.3 |1585 | |[seresnext101_64x4d.gluon_in1k](https://huggingface.co/timm/seresnext101_64x4d.gluon_in1k)|224 |80.9 |95.3 |88.2 |15.5 |31.2 |918 | |[resnet50.c2_in1k](https://huggingface.co/timm/resnet50.c2_in1k)|288 |80.86|95.52|25.6 |6.8 |18.4 |2085 | |[resnet50.tv2_in1k](https://huggingface.co/timm/resnet50.tv2_in1k)|224 |80.85|95.43|25.6 |4.1 |11.1 |3450 | |[ecaresnet50t.a2_in1k](https://huggingface.co/timm/ecaresnet50t.a2_in1k)|224 |80.84|95.02|25.6 |4.3 |11.8 |2821 | |[ecaresnet101d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet101d_pruned.miil_in1k)|224 |80.79|95.62|24.9 |3.5 |7.7 |2961 | |[seresnet33ts.ra2_in1k](https://huggingface.co/timm/seresnet33ts.ra2_in1k)|288 |80.79|95.36|19.8 |6.0 |14.8 |2506 | |[ecaresnet50d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet50d_pruned.miil_in1k)|288 |80.79|95.58|19.9 |4.2 |10.6 |2349 | |[resnet50.a2_in1k](https://huggingface.co/timm/resnet50.a2_in1k)|288 |80.78|94.99|25.6 |6.8 |18.4 |2088 | |[resnet50.b1k_in1k](https://huggingface.co/timm/resnet50.b1k_in1k)|288 |80.71|95.43|25.6 |6.8 |18.4 |2087 | |[resnext50_32x4d.ra_in1k](https://huggingface.co/timm/resnext50_32x4d.ra_in1k)|288 |80.7 |95.39|25.0 |7.0 |23.8 |1749 | |[resnetrs101.tf_in1k](https://huggingface.co/timm/resnetrs101.tf_in1k)|192 |80.69|95.24|63.6 |6.0 |12.7 |2270 | |[resnet50d.a1_in1k](https://huggingface.co/timm/resnet50d.a1_in1k)|224 |80.68|94.71|25.6 |4.4 |11.9 |3162 | |[eca_resnet33ts.ra2_in1k](https://huggingface.co/timm/eca_resnet33ts.ra2_in1k)|288 |80.68|95.36|19.7 |6.0 |14.8 |2637 | |[resnet50.a1h_in1k](https://huggingface.co/timm/resnet50.a1h_in1k)|224 |80.67|95.3 |25.6 |4.1 |11.1 |3452 | |[resnext50d_32x4d.bt_in1k](https://huggingface.co/timm/resnext50d_32x4d.bt_in1k)|288 |80.67|95.42|25.0 |7.4 |25.1 |1626 | |[resnetaa50.a1h_in1k](https://huggingface.co/timm/resnetaa50.a1h_in1k)|224 |80.63|95.21|25.6 |5.2 |11.6 |3034 | |[ecaresnet50d.miil_in1k](https://huggingface.co/timm/ecaresnet50d.miil_in1k)|224 |80.61|95.32|25.6 |4.4 |11.9 |2813 | |[resnext101_64x4d.gluon_in1k](https://huggingface.co/timm/resnext101_64x4d.gluon_in1k)|224 |80.61|94.99|83.5 |15.5 |31.2 |989 | |[gcresnet33ts.ra2_in1k](https://huggingface.co/timm/gcresnet33ts.ra2_in1k)|288 |80.6 |95.31|19.9 |6.0 |14.8 |2578 | |[gcresnext50ts.ch_in1k](https://huggingface.co/timm/gcresnext50ts.ch_in1k)|256 |80.57|95.17|15.7 |3.8 |15.5 |2710 | |[resnet152.a3_in1k](https://huggingface.co/timm/resnet152.a3_in1k)|224 |80.56|95.0 |60.2 |11.6 |22.6 |1483 | |[resnet50d.ra2_in1k](https://huggingface.co/timm/resnet50d.ra2_in1k)|224 |80.53|95.16|25.6 |4.4 |11.9 |3164 | |[resnext50_32x4d.a1_in1k](https://huggingface.co/timm/resnext50_32x4d.a1_in1k)|224 |80.53|94.46|25.0 |4.3 |14.4 |2930 | |[wide_resnet101_2.tv2_in1k](https://huggingface.co/timm/wide_resnet101_2.tv2_in1k)|176 |80.48|94.98|126.9 |14.3 |13.2 |1719 | |[resnet152d.gluon_in1k](https://huggingface.co/timm/resnet152d.gluon_in1k)|224 |80.47|95.2 |60.2 |11.8 |23.4 |1428 | |[resnet50.b2k_in1k](https://huggingface.co/timm/resnet50.b2k_in1k)|288 |80.45|95.32|25.6 |6.8 |18.4 |2086 | |[ecaresnetlight.miil_in1k](https://huggingface.co/timm/ecaresnetlight.miil_in1k)|224 |80.45|95.24|30.2 |4.1 |8.4 |3530 | |[resnext50_32x4d.a2_in1k](https://huggingface.co/timm/resnext50_32x4d.a2_in1k)|224 |80.45|94.63|25.0 |4.3 |14.4 |2936 | |[wide_resnet50_2.tv2_in1k](https://huggingface.co/timm/wide_resnet50_2.tv2_in1k)|176 |80.43|95.09|68.9 |7.3 |9.0 |3015 | |[resnet101d.gluon_in1k](https://huggingface.co/timm/resnet101d.gluon_in1k)|224 |80.42|95.01|44.6 |8.1 |17.0 |2007 | |[resnet50.a1_in1k](https://huggingface.co/timm/resnet50.a1_in1k)|224 |80.38|94.6 |25.6 |4.1 |11.1 |3461 | |[seresnet33ts.ra2_in1k](https://huggingface.co/timm/seresnet33ts.ra2_in1k)|256 |80.36|95.1 |19.8 |4.8 |11.7 |3267 | |[resnext101_32x4d.gluon_in1k](https://huggingface.co/timm/resnext101_32x4d.gluon_in1k)|224 |80.34|94.93|44.2 |8.0 |21.2 |1814 | |[resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k)|224 |80.32|95.4 |25.0 |4.3 |14.4 |2941 | |[resnet101s.gluon_in1k](https://huggingface.co/timm/resnet101s.gluon_in1k)|224 |80.28|95.16|44.7 |9.2 |18.6 |1851 | |[seresnet50.ra2_in1k](https://huggingface.co/timm/seresnet50.ra2_in1k)|224 |80.26|95.08|28.1 |4.1 |11.1 |2972 | |[resnetblur50.bt_in1k](https://huggingface.co/timm/resnetblur50.bt_in1k)|288 |80.24|95.24|25.6 |8.5 |19.9 |1523 | |[resnet50d.a2_in1k](https://huggingface.co/timm/resnet50d.a2_in1k)|224 |80.22|94.63|25.6 |4.4 |11.9 |3162 | |[resnet152.tv2_in1k](https://huggingface.co/timm/resnet152.tv2_in1k)|176 |80.2 |94.64|60.2 |7.2 |14.0 |2346 | |[seresnet50.a2_in1k](https://huggingface.co/timm/seresnet50.a2_in1k)|224 |80.08|94.74|28.1 |4.1 |11.1 |2969 | |[eca_resnet33ts.ra2_in1k](https://huggingface.co/timm/eca_resnet33ts.ra2_in1k)|256 |80.08|94.97|19.7 |4.8 |11.7 |3284 | |[gcresnet33ts.ra2_in1k](https://huggingface.co/timm/gcresnet33ts.ra2_in1k)|256 |80.06|94.99|19.9 |4.8 |11.7 |3216 | |[resnet50_gn.a1h_in1k](https://huggingface.co/timm/resnet50_gn.a1h_in1k)|224 |80.06|94.95|25.6 |4.1 |11.1 |1109 | |[seresnet50.a1_in1k](https://huggingface.co/timm/seresnet50.a1_in1k)|224 |80.02|94.71|28.1 |4.1 |11.1 |2962 | |[resnet50.ram_in1k](https://huggingface.co/timm/resnet50.ram_in1k)|288 |79.97|95.05|25.6 |6.8 |18.4 |2086 | |[resnet152c.gluon_in1k](https://huggingface.co/timm/resnet152c.gluon_in1k)|224 |79.92|94.84|60.2 |11.8 |23.4 |1455 | |[seresnext50_32x4d.gluon_in1k](https://huggingface.co/timm/seresnext50_32x4d.gluon_in1k)|224 |79.91|94.82|27.6 |4.3 |14.4 |2591 | |[resnet50.d_in1k](https://huggingface.co/timm/resnet50.d_in1k)|224 |79.91|94.67|25.6 |4.1 |11.1 |3456 | |[resnet101.tv2_in1k](https://huggingface.co/timm/resnet101.tv2_in1k)|176 |79.9 |94.6 |44.6 |4.9 |10.1 |3341 | |[resnetrs50.tf_in1k](https://huggingface.co/timm/resnetrs50.tf_in1k)|224 |79.89|94.97|35.7 |4.5 |12.1 |2774 | |[resnet50.c2_in1k](https://huggingface.co/timm/resnet50.c2_in1k)|224 |79.88|94.87|25.6 |4.1 |11.1 |3455 | |[ecaresnet26t.ra2_in1k](https://huggingface.co/timm/ecaresnet26t.ra2_in1k)|320 |79.86|95.07|16.0 |5.2 |16.4 |2168 | |[resnet50.a2_in1k](https://huggingface.co/timm/resnet50.a2_in1k)|224 |79.85|94.56|25.6 |4.1 |11.1 |3460 | |[resnet50.ra_in1k](https://huggingface.co/timm/resnet50.ra_in1k)|288 |79.83|94.97|25.6 |6.8 |18.4 |2087 | |[resnet101.a3_in1k](https://huggingface.co/timm/resnet101.a3_in1k)|224 |79.82|94.62|44.6 |7.8 |16.2 |2114 | |[resnext50_32x4d.ra_in1k](https://huggingface.co/timm/resnext50_32x4d.ra_in1k)|224 |79.76|94.6 |25.0 |4.3 |14.4 |2943 | |[resnet50.c1_in1k](https://huggingface.co/timm/resnet50.c1_in1k)|224 |79.74|94.95|25.6 |4.1 |11.1 |3455 | |[ecaresnet50d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet50d_pruned.miil_in1k)|224 |79.74|94.87|19.9 |2.5 |6.4 |3929 | |[resnet33ts.ra2_in1k](https://huggingface.co/timm/resnet33ts.ra2_in1k)|288 |79.71|94.83|19.7 |6.0 |14.8 |2710 | |[resnet152.gluon_in1k](https://huggingface.co/timm/resnet152.gluon_in1k)|224 |79.68|94.74|60.2 |11.6 |22.6 |1486 | |[resnext50d_32x4d.bt_in1k](https://huggingface.co/timm/resnext50d_32x4d.bt_in1k)|224 |79.67|94.87|25.0 |4.5 |15.2 |2729 | |[resnet50.bt_in1k](https://huggingface.co/timm/resnet50.bt_in1k)|288 |79.63|94.91|25.6 |6.8 |18.4 |2086 | |[ecaresnet50t.a3_in1k](https://huggingface.co/timm/ecaresnet50t.a3_in1k)|224 |79.56|94.72|25.6 |4.3 |11.8 |2805 | |[resnet101c.gluon_in1k](https://huggingface.co/timm/resnet101c.gluon_in1k)|224 |79.53|94.58|44.6 |8.1 |17.0 |2062 | |[resnet50.b1k_in1k](https://huggingface.co/timm/resnet50.b1k_in1k)|224 |79.52|94.61|25.6 |4.1 |11.1 |3459 | |[resnet50.tv2_in1k](https://huggingface.co/timm/resnet50.tv2_in1k)|176 |79.42|94.64|25.6 |2.6 |6.9 |5397 | |[resnet32ts.ra2_in1k](https://huggingface.co/timm/resnet32ts.ra2_in1k)|288 |79.4 |94.66|18.0 |5.9 |14.6 |2752 | |[resnet50.b2k_in1k](https://huggingface.co/timm/resnet50.b2k_in1k)|224 |79.38|94.57|25.6 |4.1 |11.1 |3459 | |[resnext50_32x4d.tv2_in1k](https://huggingface.co/timm/resnext50_32x4d.tv2_in1k)|176 |79.37|94.3 |25.0 |2.7 |9.0 |4577 | |[resnext50_32x4d.gluon_in1k](https://huggingface.co/timm/resnext50_32x4d.gluon_in1k)|224 |79.36|94.43|25.0 |4.3 |14.4 |2942 | |[resnext101_32x8d.tv_in1k](https://huggingface.co/timm/resnext101_32x8d.tv_in1k)|224 |79.31|94.52|88.8 |16.5 |31.2 |1100 | |[resnet101.gluon_in1k](https://huggingface.co/timm/resnet101.gluon_in1k)|224 |79.31|94.53|44.6 |7.8 |16.2 |2125 | |[resnetblur50.bt_in1k](https://huggingface.co/timm/resnetblur50.bt_in1k)|224 |79.31|94.63|25.6 |5.2 |12.0 |2524 | |[resnet50.a1h_in1k](https://huggingface.co/timm/resnet50.a1h_in1k)|176 |79.27|94.49|25.6 |2.6 |6.9 |5404 | |[resnext50_32x4d.a3_in1k](https://huggingface.co/timm/resnext50_32x4d.a3_in1k)|224 |79.25|94.31|25.0 |4.3 |14.4 |2931 | |[resnet50.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnet50.fb_ssl_yfcc100m_ft_in1k)|224 |79.22|94.84|25.6 |4.1 |11.1 |3451 | |[resnet33ts.ra2_in1k](https://huggingface.co/timm/resnet33ts.ra2_in1k)|256 |79.21|94.56|19.7 |4.8 |11.7 |3392 | |[resnet50d.gluon_in1k](https://huggingface.co/timm/resnet50d.gluon_in1k)|224 |79.07|94.48|25.6 |4.4 |11.9 |3162 | |[resnet50.ram_in1k](https://huggingface.co/timm/resnet50.ram_in1k)|224 |79.03|94.38|25.6 |4.1 |11.1 |3453 | |[resnet50.am_in1k](https://huggingface.co/timm/resnet50.am_in1k)|224 |79.01|94.39|25.6 |4.1 |11.1 |3461 | |[resnet32ts.ra2_in1k](https://huggingface.co/timm/resnet32ts.ra2_in1k)|256 |79.01|94.37|18.0 |4.6 |11.6 |3440 | |[ecaresnet26t.ra2_in1k](https://huggingface.co/timm/ecaresnet26t.ra2_in1k)|256 |78.9 |94.54|16.0 |3.4 |10.5 |3421 | |[resnet152.a3_in1k](https://huggingface.co/timm/resnet152.a3_in1k)|160 |78.89|94.11|60.2 |5.9 |11.5 |2745 | |[wide_resnet101_2.tv_in1k](https://huggingface.co/timm/wide_resnet101_2.tv_in1k)|224 |78.84|94.28|126.9 |22.8 |21.2 |1079 | |[seresnext26d_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26d_32x4d.bt_in1k)|288 |78.83|94.24|16.8 |4.5 |16.8 |2251 | |[resnet50.ra_in1k](https://huggingface.co/timm/resnet50.ra_in1k)|224 |78.81|94.32|25.6 |4.1 |11.1 |3454 | |[seresnext26t_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26t_32x4d.bt_in1k)|288 |78.74|94.33|16.8 |4.5 |16.7 |2264 | |[resnet50s.gluon_in1k](https://huggingface.co/timm/resnet50s.gluon_in1k)|224 |78.72|94.23|25.7 |5.5 |13.5 |2796 | |[resnet50d.a3_in1k](https://huggingface.co/timm/resnet50d.a3_in1k)|224 |78.71|94.24|25.6 |4.4 |11.9 |3154 | |[wide_resnet50_2.tv_in1k](https://huggingface.co/timm/wide_resnet50_2.tv_in1k)|224 |78.47|94.09|68.9 |11.4 |14.4 |1934 | |[resnet50.bt_in1k](https://huggingface.co/timm/resnet50.bt_in1k)|224 |78.46|94.27|25.6 |4.1 |11.1 |3454 | |[resnet34d.ra2_in1k](https://huggingface.co/timm/resnet34d.ra2_in1k)|288 |78.43|94.35|21.8 |6.5 |7.5 |3291 | |[gcresnext26ts.ch_in1k](https://huggingface.co/timm/gcresnext26ts.ch_in1k)|288 |78.42|94.04|10.5 |3.1 |13.3 |3226 | |[resnet26t.ra2_in1k](https://huggingface.co/timm/resnet26t.ra2_in1k)|320 |78.33|94.13|16.0 |5.2 |16.4 |2391 | |[resnet152.tv_in1k](https://huggingface.co/timm/resnet152.tv_in1k)|224 |78.32|94.04|60.2 |11.6 |22.6 |1487 | |[seresnext26ts.ch_in1k](https://huggingface.co/timm/seresnext26ts.ch_in1k)|288 |78.28|94.1 |10.4 |3.1 |13.3 |3062 | |[bat_resnext26ts.ch_in1k](https://huggingface.co/timm/bat_resnext26ts.ch_in1k)|256 |78.25|94.1 |10.7 |2.5 |12.5 |3393 | |[resnet50.a3_in1k](https://huggingface.co/timm/resnet50.a3_in1k)|224 |78.06|93.78|25.6 |4.1 |11.1 |3450 | |[resnet50c.gluon_in1k](https://huggingface.co/timm/resnet50c.gluon_in1k)|224 |78.0 |93.99|25.6 |4.4 |11.9 |3286 | |[eca_resnext26ts.ch_in1k](https://huggingface.co/timm/eca_resnext26ts.ch_in1k)|288 |78.0 |93.91|10.3 |3.1 |13.3 |3297 | |[seresnext26t_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26t_32x4d.bt_in1k)|224 |77.98|93.75|16.8 |2.7 |10.1 |3841 | |[resnet34.a1_in1k](https://huggingface.co/timm/resnet34.a1_in1k)|288 |77.92|93.77|21.8 |6.1 |6.2 |3609 | |[resnet101.a3_in1k](https://huggingface.co/timm/resnet101.a3_in1k)|160 |77.88|93.71|44.6 |4.0 |8.3 |3926 | |[resnet26t.ra2_in1k](https://huggingface.co/timm/resnet26t.ra2_in1k)|256 |77.87|93.84|16.0 |3.4 |10.5 |3772 | |[seresnext26ts.ch_in1k](https://huggingface.co/timm/seresnext26ts.ch_in1k)|256 |77.86|93.79|10.4 |2.4 |10.5 |4263 | |[resnetrs50.tf_in1k](https://huggingface.co/timm/resnetrs50.tf_in1k)|160 |77.82|93.81|35.7 |2.3 |6.2 |5238 | |[gcresnext26ts.ch_in1k](https://huggingface.co/timm/gcresnext26ts.ch_in1k)|256 |77.81|93.82|10.5 |2.4 |10.5 |4183 | |[ecaresnet50t.a3_in1k](https://huggingface.co/timm/ecaresnet50t.a3_in1k)|160 |77.79|93.6 |25.6 |2.2 |6.0 |5329 | |[resnext50_32x4d.a3_in1k](https://huggingface.co/timm/resnext50_32x4d.a3_in1k)|160 |77.73|93.32|25.0 |2.2 |7.4 |5576 | |[resnext50_32x4d.tv_in1k](https://huggingface.co/timm/resnext50_32x4d.tv_in1k)|224 |77.61|93.7 |25.0 |4.3 |14.4 |2944 | |[seresnext26d_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26d_32x4d.bt_in1k)|224 |77.59|93.61|16.8 |2.7 |10.2 |3807 | |[resnet50.gluon_in1k](https://huggingface.co/timm/resnet50.gluon_in1k)|224 |77.58|93.72|25.6 |4.1 |11.1 |3455 | |[eca_resnext26ts.ch_in1k](https://huggingface.co/timm/eca_resnext26ts.ch_in1k)|256 |77.44|93.56|10.3 |2.4 |10.5 |4284 | |[resnet26d.bt_in1k](https://huggingface.co/timm/resnet26d.bt_in1k)|288 |77.41|93.63|16.0 |4.3 |13.5 |2907 | |[resnet101.tv_in1k](https://huggingface.co/timm/resnet101.tv_in1k)|224 |77.38|93.54|44.6 |7.8 |16.2 |2125 | |[resnet50d.a3_in1k](https://huggingface.co/timm/resnet50d.a3_in1k)|160 |77.22|93.27|25.6 |2.2 |6.1 |5982 | |[resnext26ts.ra2_in1k](https://huggingface.co/timm/resnext26ts.ra2_in1k)|288 |77.17|93.47|10.3 |3.1 |13.3 |3392 | |[resnet34.a2_in1k](https://huggingface.co/timm/resnet34.a2_in1k)|288 |77.15|93.27|21.8 |6.1 |6.2 |3615 | |[resnet34d.ra2_in1k](https://huggingface.co/timm/resnet34d.ra2_in1k)|224 |77.1 |93.37|21.8 |3.9 |4.5 |5436 | |[seresnet50.a3_in1k](https://huggingface.co/timm/seresnet50.a3_in1k)|224 |77.02|93.07|28.1 |4.1 |11.1 |2952 | |[resnext26ts.ra2_in1k](https://huggingface.co/timm/resnext26ts.ra2_in1k)|256 |76.78|93.13|10.3 |2.4 |10.5 |4410 | |[resnet26d.bt_in1k](https://huggingface.co/timm/resnet26d.bt_in1k)|224 |76.7 |93.17|16.0 |2.6 |8.2 |4859 | |[resnet34.bt_in1k](https://huggingface.co/timm/resnet34.bt_in1k)|288 |76.5 |93.35|21.8 |6.1 |6.2 |3617 | |[resnet34.a1_in1k](https://huggingface.co/timm/resnet34.a1_in1k)|224 |76.42|92.87|21.8 |3.7 |3.7 |5984 | |[resnet26.bt_in1k](https://huggingface.co/timm/resnet26.bt_in1k)|288 |76.35|93.18|16.0 |3.9 |12.2 |3331 | |[resnet50.tv_in1k](https://huggingface.co/timm/resnet50.tv_in1k)|224 |76.13|92.86|25.6 |4.1 |11.1 |3457 | |[resnet50.a3_in1k](https://huggingface.co/timm/resnet50.a3_in1k)|160 |75.96|92.5 |25.6 |2.1 |5.7 |6490 | |[resnet34.a2_in1k](https://huggingface.co/timm/resnet34.a2_in1k)|224 |75.52|92.44|21.8 |3.7 |3.7 |5991 | |[resnet26.bt_in1k](https://huggingface.co/timm/resnet26.bt_in1k)|224 |75.3 |92.58|16.0 |2.4 |7.4 |5583 | |[resnet34.bt_in1k](https://huggingface.co/timm/resnet34.bt_in1k)|224 |75.16|92.18|21.8 |3.7 |3.7 |5994 | |[seresnet50.a3_in1k](https://huggingface.co/timm/seresnet50.a3_in1k)|160 |75.1 |92.08|28.1 |2.1 |5.7 |5513 | |[resnet34.gluon_in1k](https://huggingface.co/timm/resnet34.gluon_in1k)|224 |74.57|91.98|21.8 |3.7 |3.7 |5984 | |[resnet18d.ra2_in1k](https://huggingface.co/timm/resnet18d.ra2_in1k)|288 |73.81|91.83|11.7 |3.4 |5.4 |5196 | |[resnet34.tv_in1k](https://huggingface.co/timm/resnet34.tv_in1k)|224 |73.32|91.42|21.8 |3.7 |3.7 |5979 | |[resnet18.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnet18.fb_swsl_ig1b_ft_in1k)|224 |73.28|91.73|11.7 |1.8 |2.5 |10213 | |[resnet18.a1_in1k](https://huggingface.co/timm/resnet18.a1_in1k)|288 |73.16|91.03|11.7 |3.0 |4.1 |6050 | |[resnet34.a3_in1k](https://huggingface.co/timm/resnet34.a3_in1k)|224 |72.98|91.11|21.8 |3.7 |3.7 |5967 | |[resnet18.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnet18.fb_ssl_yfcc100m_ft_in1k)|224 |72.6 |91.42|11.7 |1.8 |2.5 |10213 | |[resnet18.a2_in1k](https://huggingface.co/timm/resnet18.a2_in1k)|288 |72.37|90.59|11.7 |3.0 |4.1 |6051 | |[resnet14t.c3_in1k](https://huggingface.co/timm/resnet14t.c3_in1k)|224 |72.26|90.31|10.1 |1.7 |5.8 |7026 | |[resnet18d.ra2_in1k](https://huggingface.co/timm/resnet18d.ra2_in1k)|224 |72.26|90.68|11.7 |2.1 |3.3 |8707 | |[resnet18.a1_in1k](https://huggingface.co/timm/resnet18.a1_in1k)|224 |71.49|90.07|11.7 |1.8 |2.5 |10187 | |[resnet14t.c3_in1k](https://huggingface.co/timm/resnet14t.c3_in1k)|176 |71.31|89.69|10.1 |1.1 |3.6 |10970 | |[resnet18.gluon_in1k](https://huggingface.co/timm/resnet18.gluon_in1k)|224 |70.84|89.76|11.7 |1.8 |2.5 |10210 | |[resnet18.a2_in1k](https://huggingface.co/timm/resnet18.a2_in1k)|224 |70.64|89.47|11.7 |1.8 |2.5 |10194 | |[resnet34.a3_in1k](https://huggingface.co/timm/resnet34.a3_in1k)|160 |70.56|89.52|21.8 |1.9 |1.9 |10737 | |[resnet18.tv_in1k](https://huggingface.co/timm/resnet18.tv_in1k)|224 |69.76|89.07|11.7 |1.8 |2.5 |10205 | |[resnet10t.c3_in1k](https://huggingface.co/timm/resnet10t.c3_in1k)|224 |68.34|88.03|5.4 |1.1 |2.4 |13079 | |[resnet18.a3_in1k](https://huggingface.co/timm/resnet18.a3_in1k)|224 |68.25|88.17|11.7 |1.8 |2.5 |10167 | |[resnet10t.c3_in1k](https://huggingface.co/timm/resnet10t.c3_in1k)|176 |66.71|86.96|5.4 |0.7 |1.5 |20327 | |[resnet18.a3_in1k](https://huggingface.co/timm/resnet18.a3_in1k)|160 |65.66|86.26|11.7 |0.9 |1.3 |18229 | ## Citation ```bibtex @inproceedings{wightman2021resnet, title={ResNet strikes back: An improved training procedure in timm}, author={Wightman, Ross and Touvron, Hugo and Jegou, Herve}, booktitle={NeurIPS 2021 Workshop on ImageNet: Past, Present, and Future} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ``` ```bibtex @article{He2015, author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun}, title = {Deep Residual Learning for Image Recognition}, journal = {arXiv preprint arXiv:1512.03385}, year = {2015} } ``` ```bibtex @article{He2018BagOT, title={Bag of Tricks for Image Classification with Convolutional Neural Networks}, author={Tong He and Zhi Zhang and Hang Zhang and Zhongyue Zhang and Junyuan Xie and Mu Li}, journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2018}, pages={558-567} } ```
keremberke/yolov5n-license-plate
keremberke
"2023-01-01T09:59:54Z"
23,401
17
yolov5
[ "yolov5", "tensorboard", "yolo", "vision", "object-detection", "pytorch", "dataset:keremberke/license-plate-object-detection", "model-index", "region:us" ]
object-detection
"2023-01-01T03:02:44Z"
--- tags: - yolov5 - yolo - vision - object-detection - pytorch library_name: yolov5 library_version: 7.0.6 inference: false datasets: - keremberke/license-plate-object-detection model-index: - name: keremberke/yolov5n-license-plate results: - task: type: object-detection dataset: type: keremberke/license-plate-object-detection name: keremberke/license-plate-object-detection split: validation metrics: - type: precision # since [email protected] is not available on hf.co/metrics value: 0.9783431294995892 # min: 0.0 - max: 1.0 name: [email protected] --- <div align="center"> <img width="640" alt="keremberke/yolov5n-license-plate" src="https://huggingface.co/keremberke/yolov5n-license-plate/resolve/main/sample_visuals.jpg"> </div> ### How to use - Install [yolov5](https://github.com/fcakyon/yolov5-pip): ```bash pip install -U yolov5 ``` - Load model and perform prediction: ```python import yolov5 # load model model = yolov5.load('keremberke/yolov5n-license-plate') # set model parameters model.conf = 0.25 # NMS confidence threshold model.iou = 0.45 # NMS IoU threshold model.agnostic = False # NMS class-agnostic model.multi_label = False # NMS multiple labels per box model.max_det = 1000 # maximum number of detections per image # set image img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg' # perform inference results = model(img, size=640) # inference with test time augmentation results = model(img, augment=True) # parse results predictions = results.pred[0] boxes = predictions[:, :4] # x1, y1, x2, y2 scores = predictions[:, 4] categories = predictions[:, 5] # show detection bounding boxes on image results.show() # save results into "results/" folder results.save(save_dir='results/') ``` - Finetune the model on your custom dataset: ```bash yolov5 train --data data.yaml --img 640 --batch 16 --weights keremberke/yolov5n-license-plate --epochs 10 ``` **More models available at: [awesome-yolov5-models](https://github.com/keremberke/awesome-yolov5-models)**
OPI-PG/Qra-1b
OPI-PG
"2024-03-16T11:06:51Z"
23,338
18
transformers
[ "transformers", "safetensors", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2024-02-26T18:09:40Z"
--- license: apache-2.0 --- <center><img src="https://huggingface.co/OPI-PG/Qra-1b/resolve/main/images/1b-logo.png"></img></center> Qra is a series of LLMs adapted to the Polish language, resulting from a collaboration between the National Information Processing Institute (OPI) and Gdańsk University of Technology (PG). The models were trained on the infrastructure of PG TASK Computing Center using 21 Nvidia A100 cards. The published versions of the Qra models were initialized with the weights of English LLama 2 checkpoints and then further trained on a carefully cleaned, filtered, and deduplicated corpus of Polish texts, totaling about 90 billion tokens. The original corpus consisted primarily of web data, including CommonCrawl dumps, and the MADLAD-400 corpus. ⚠️ **Important: Qra are foundation language models trained with causal language modeling objective on a large corpus of texts. They are therefore not intended for conversational or instruction-following purposes, and should be further fine-tuned to be used for such tasks.** ⚠️ The preprocessing pipeline included the following steps: - Text normalization, removal of URLs. - Removal of documents shorter than 500 characters. - Cleaning sentences in documents using a set of heuristic rules. Among others, sentences consisting of mostly non-alphabetical characters, as well as sentences in languages other than Polish and English, were removed. - Filtering documents using a quality classifier trained on a set of several thousand documents manually labeled as being of high or low quality. The input to the classifier is a set of several statistics ("quality signals") such as the percentage of Polish words, average word and sentence length, number of word and character duplications, proportion of different characters classes in the text. - Filtering documents based on the perplexity value calculated by a lightweight KenLM language model. - Assigning the document to one of 18 topical domains using a trained classifier. - Fuzzy deduplication using the MinHash algorithm within each topical domain. The final distribution of documents by topic is shown in the chart below: <center><img src="https://huggingface.co/OPI-PG/Qra-1b/resolve/main/images/topics.png"></img></center> ## Model details The models were trained for one epoch on sequences of 4096 tokens. During training, we used many modern optimizations such as: - [torch.compile](https://pytorch.org/docs/stable/generated/torch.compile.html) - [adamw_apex_fused](https://huggingface.co/docs/transformers/main/en/perf_train_gpu_one#optimizer-choice) optimizer - [Flash Attention 2](https://github.com/Dao-AILab/flash-attention) - [Mixed precision](https://huggingface.co/docs/transformers/main/en/perf_train_gpu_one#bf16) (`--bf16` and `--tf32` options) - [Gradient accumulation](https://huggingface.co/docs/transformers/main/en/perf_train_gpu_one#gradient-accumulation) - [Fully Sharded Data Parallel (FSDP)](https://pytorch.org/tutorials/intermediate/FSDP_tutorial.html) with the SHARD_GRAD_OP mode - [Gradient checkpointing](https://huggingface.co/docs/transformers/main/en/perf_train_gpu_one#gradient-checkpointing) (only for the 13B model) Below is a summary of the Qra-1B model: | Attribute | Value | | ---- | ---- | | Adapted from | [TinyLlama-1.1B](TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) | | License | Apache 2.0 | | Batch size | 1344 | | Context length | 4096 | | Learning rate | 2e-5 | | Learning rate decay | cosine | | Warmup steps | 0 | | Training time | 2 days | ## Evaluation In this section we compare the perplexity of Qra models on Polish texts with other Polish and English LLMs. Note that perplexity values between different text segmentations are not directly comparable. Therefore, we can draw conclusions based on comparisons only beetween models using the same tokenizer, such as Qra and the original LLama / TinyLLama. ### PolEval-2018 In 2018, the PolEval competition included a language modeling task, for which training and test sets totaling over 20 million Polish sentences were made available. We used the first 10k sentences from the test set to evaluate modern neural language models. To calculate the perplexity, we used a script from the [HuggingFace Evaluate](https://huggingface.co/spaces/evaluate-metric/perplexity) library. <table> <thead> <tr><th>Model</th><th>Perplexity</th></tr> </thead> <tr><td colspan="2"><strong>English models</strong></td></tr> <tr><td>meta-llama/Llama-2-7b-hf</td><td>24.3</td></tr> <tr><td>meta-llama/Llama-2-13b-hf</td><td>21.4</td></tr> <tr><td>mistralai/Mistral-7B-v0.1</td><td>21.4</td></tr> <tr><td>TinyLlama/TinyLlama-1.1B</td><td>40.4</td></tr> <tr><td colspan="2"><strong>Polish models</strong></td></tr> <tr><td>sdadas/polish-gpt2-small</td><td>134.4</td></tr> <tr><td>sdadas/polish-gpt2-medium</td><td>100.8</td></tr> <tr><td>sdadas/polish-gpt2-large</td><td>93.2</td></tr> <tr><td>sdadas/polish-gpt2-xl</td><td>94.1</td></tr> <tr><td>Azurro/APT3-275M-Base</td><td>129.8</td></tr> <tr><td>Azurro/APT3-500M-Base</td><td>153.1</td></tr> <tr><td>Azurro/APT3-1B-Base</td><td>106.8</td></tr> <tr><td>eryk-mazus/polka-1.1b</td><td>18.1</td></tr> <tr><td>szymonrucinski/Curie-7B-v1</td><td>13.5</td></tr> <tr><td colspan="2"><strong>Qra models</strong></td></tr> <tr><td>OPI-PG/Qra-1b</td><td>14.7</td></tr> <tr><td>OPI-PG/Qra-7b</td><td>11.3</td></tr> <tr><td>OPI-PG/Qra-13b</td><td>10.5</td></tr> </table> ### Long documents (2024) Currently, LLMs support contexts of thousands of tokens. Their practical applications usually also involve processing long documents. Therefore, evaluating perplexity on a sentence-based dataset such as PolEval-2018 may not be meaningful. Additionally, the PolEval corpus has been publicly available on the internet for the past few years, which raises the possibility that for some models the training sets have been contaminated by this data. For this reason, we have prepared a new collection consisting of long papers published exclusively in 2024, which will allow us to more reliably test the perplexities of the models on new knowledge that was not available to them at the time of training. The corpus consists of 5,000 documents ranging from several hundred to about 20,000 tokens. Half of the set consists of press texts from Polish news portals from February 2024, the other half are scientific articles published since January 2024. Most of the documents exceed the context size of the evaluated models. To calculate perplexity for these documents, we divided them into chunks of size equal to the model's context length with a stride of 512 tokens, following [this example](https://huggingface.co/docs/transformers/en/perplexity). <table> <thead> <tr><th>Model</th><th>Context</th><th>Perplexity</th></tr> </thead> <tr><td colspan="3"><strong>English models</strong></td></tr> <tr><td>meta-llama/Llama-2-7b-hf</td><td>4096</td><td>5.9</td></tr> <tr><td>meta-llama/Llama-2-13b-hf</td><td>4096</td><td>5.3</td></tr> <tr><td>mistralai/Mistral-7B-v0.1</td><td>4096</td><td>4.9</td></tr> <tr><td>TinyLlama/TinyLlama-1.1B</td><td>2048</td><td>9.6</td></tr> <tr><td colspan="3"><strong>Polish models</strong></td></tr> <tr><td>sdadas/polish-gpt2-small</td><td>2048</td><td>27.3</td></tr> <tr><td>sdadas/polish-gpt2-medium</td><td>2048</td><td>20.3</td></tr> <tr><td>sdadas/polish-gpt2-large</td><td>1536</td><td>18.0</td></tr> <tr><td>sdadas/polish-gpt2-xl</td><td>1536</td><td>16.6</td></tr> <tr><td>Azurro/APT3-275M-Base</td><td>2048</td><td>77.0</td></tr> <tr><td>Azurro/APT3-500M-Base</td><td>2048</td><td>50.5</td></tr> <tr><td>Azurro/APT3-1B-Base</td><td>2048</td><td>19.1</td></tr> <tr><td>eryk-mazus/polka-1.1b</td><td>2048</td><td>6.9</td></tr> <tr><td>szymonrucinski/Curie-7B-v1</td><td>4096</td><td>4.8</td></tr> <tr><td colspan="3"><strong>Qra models</strong></td></tr> <tr><td>OPI-PG/Qra-1b</td><td>4096</td><td>6.1</td></tr> <tr><td>OPI-PG/Qra-7b</td><td>4096</td><td>4.5</td></tr> <tr><td>OPI-PG/Qra-13b</td><td>4096</td><td>4.2</td></tr> </table>
timm/vit_base_patch32_clip_448.laion2b_ft_in12k_in1k
timm
"2023-05-06T00:04:40Z"
23,326
1
timm
[ "timm", "pytorch", "safetensors", "image-classification", "dataset:imagenet-1k", "dataset:laion-2b", "dataset:imagenet-12k", "arxiv:2212.07143", "arxiv:2210.08402", "arxiv:2010.11929", "license:apache-2.0", "region:us" ]
image-classification
"2022-11-05T22:34:21Z"
--- tags: - image-classification - timm library_name: timm license: apache-2.0 datasets: - imagenet-1k - laion-2b - imagenet-12k --- # Model card for vit_base_patch32_clip_448.laion2b_ft_in12k_in1k A Vision Transformer (ViT) image classification model. Pretrained on LAION-2B image-text pairs using OpenCLIP. Fine-tuned on ImageNet-12k and then ImageNet-1k in `timm`. See recipes in [Reproducible scaling laws](https://arxiv.org/abs/2212.07143). ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 88.3 - GMACs: 17.2 - Activations (M): 16.5 - Image size: 448 x 448 - **Papers:** - OpenCLIP: https://github.com/mlfoundations/open_clip - Reproducible scaling laws for contrastive language-image learning: https://arxiv.org/abs/2212.07143 - LAION-5B: An open large-scale dataset for training next generation image-text models: https://arxiv.org/abs/2210.08402 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - **Dataset:** ImageNet-1k - **Pretrain Dataset:** - LAION-2B - ImageNet-12k ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('vit_base_patch32_clip_448.laion2b_ft_in12k_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'vit_base_patch32_clip_448.laion2b_ft_in12k_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 197, 768) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @software{ilharco_gabriel_2021_5143773, author = {Ilharco, Gabriel and Wortsman, Mitchell and Wightman, Ross and Gordon, Cade and Carlini, Nicholas and Taori, Rohan and Dave, Achal and Shankar, Vaishaal and Namkoong, Hongseok and Miller, John and Hajishirzi, Hannaneh and Farhadi, Ali and Schmidt, Ludwig}, title = {OpenCLIP}, month = jul, year = 2021, note = {If you use this software, please cite it as below.}, publisher = {Zenodo}, version = {0.1}, doi = {10.5281/zenodo.5143773}, url = {https://doi.org/10.5281/zenodo.5143773} } ``` ```bibtex @article{cherti2022reproducible, title={Reproducible scaling laws for contrastive language-image learning}, author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia}, journal={arXiv preprint arXiv:2212.07143}, year={2022} } ``` ```bibtex @inproceedings{schuhmann2022laionb, title={{LAION}-5B: An open large-scale dataset for training next generation image-text models}, author={Christoph Schuhmann and Romain Beaumont and Richard Vencu and Cade W Gordon and Ross Wightman and Mehdi Cherti and Theo Coombes and Aarush Katta and Clayton Mullis and Mitchell Wortsman and Patrick Schramowski and Srivatsa R Kundurthy and Katherine Crowson and Ludwig Schmidt and Robert Kaczmarczyk and Jenia Jitsev}, booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, year={2022}, url={https://openreview.net/forum?id=M3Y74vmsMcY} } ``` ```bibtex @article{dosovitskiy2020vit, title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale}, author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil}, journal={ICLR}, year={2021} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```
timm/resnet34.a1_in1k
timm
"2024-02-10T23:38:51Z"
23,324
0
timm
[ "timm", "pytorch", "safetensors", "image-classification", "arxiv:2110.00476", "arxiv:1512.03385", "license:apache-2.0", "region:us" ]
image-classification
"2023-04-05T18:05:32Z"
--- license: apache-2.0 library_name: timm tags: - image-classification - timm --- # Model card for resnet34.a1_in1k A ResNet-B image classification model. This model features: * ReLU activations * single layer 7x7 convolution with pooling * 1x1 convolution shortcut downsample Trained on ImageNet-1k in `timm` using recipe template described below. Recipe details: * ResNet Strikes Back `A1` recipe * LAMB optimizer with BCE loss * Cosine LR schedule with warmup ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 21.8 - GMACs: 3.7 - Activations (M): 3.7 - Image size: train = 224 x 224, test = 288 x 288 - **Papers:** - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 - **Original:** https://github.com/huggingface/pytorch-image-models ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('resnet34.a1_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'resnet34.a1_in1k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 64, 112, 112]) # torch.Size([1, 64, 56, 56]) # torch.Size([1, 128, 28, 28]) # torch.Size([1, 256, 14, 14]) # torch.Size([1, 512, 7, 7]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'resnet34.a1_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 512, 7, 7) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). |model |img_size|top1 |top5 |param_count|gmacs|macts|img/sec| |------------------------------------------|--------|-----|-----|-----------|-----|-----|-------| |[seresnextaa101d_32x8d.sw_in12k_ft_in1k_288](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k_288)|320 |86.72|98.17|93.6 |35.2 |69.7 |451 | |[seresnextaa101d_32x8d.sw_in12k_ft_in1k_288](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k_288)|288 |86.51|98.08|93.6 |28.5 |56.4 |560 | |[seresnextaa101d_32x8d.sw_in12k_ft_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k)|288 |86.49|98.03|93.6 |28.5 |56.4 |557 | |[seresnextaa101d_32x8d.sw_in12k_ft_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k)|224 |85.96|97.82|93.6 |17.2 |34.2 |923 | |[resnext101_32x32d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x32d.fb_wsl_ig1b_ft_in1k)|224 |85.11|97.44|468.5 |87.3 |91.1 |254 | |[resnetrs420.tf_in1k](https://huggingface.co/timm/resnetrs420.tf_in1k)|416 |85.0 |97.12|191.9 |108.4|213.8|134 | |[ecaresnet269d.ra2_in1k](https://huggingface.co/timm/ecaresnet269d.ra2_in1k)|352 |84.96|97.22|102.1 |50.2 |101.2|291 | |[ecaresnet269d.ra2_in1k](https://huggingface.co/timm/ecaresnet269d.ra2_in1k)|320 |84.73|97.18|102.1 |41.5 |83.7 |353 | |[resnetrs350.tf_in1k](https://huggingface.co/timm/resnetrs350.tf_in1k)|384 |84.71|96.99|164.0 |77.6 |154.7|183 | |[seresnextaa101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.ah_in1k)|288 |84.57|97.08|93.6 |28.5 |56.4 |557 | |[resnetrs200.tf_in1k](https://huggingface.co/timm/resnetrs200.tf_in1k)|320 |84.45|97.08|93.2 |31.5 |67.8 |446 | |[resnetrs270.tf_in1k](https://huggingface.co/timm/resnetrs270.tf_in1k)|352 |84.43|96.97|129.9 |51.1 |105.5|280 | |[seresnext101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101d_32x8d.ah_in1k)|288 |84.36|96.92|93.6 |27.6 |53.0 |595 | |[seresnet152d.ra2_in1k](https://huggingface.co/timm/seresnet152d.ra2_in1k)|320 |84.35|97.04|66.8 |24.1 |47.7 |610 | |[resnetrs350.tf_in1k](https://huggingface.co/timm/resnetrs350.tf_in1k)|288 |84.3 |96.94|164.0 |43.7 |87.1 |333 | |[resnext101_32x8d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_swsl_ig1b_ft_in1k)|224 |84.28|97.17|88.8 |16.5 |31.2 |1100 | |[resnetrs420.tf_in1k](https://huggingface.co/timm/resnetrs420.tf_in1k)|320 |84.24|96.86|191.9 |64.2 |126.6|228 | |[seresnext101_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101_32x8d.ah_in1k)|288 |84.19|96.87|93.6 |27.2 |51.6 |613 | |[resnext101_32x16d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_wsl_ig1b_ft_in1k)|224 |84.18|97.19|194.0 |36.3 |51.2 |581 | |[resnetaa101d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa101d.sw_in12k_ft_in1k)|288 |84.11|97.11|44.6 |15.1 |29.0 |1144 | |[resnet200d.ra2_in1k](https://huggingface.co/timm/resnet200d.ra2_in1k)|320 |83.97|96.82|64.7 |31.2 |67.3 |518 | |[resnetrs200.tf_in1k](https://huggingface.co/timm/resnetrs200.tf_in1k)|256 |83.87|96.75|93.2 |20.2 |43.4 |692 | |[seresnextaa101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.ah_in1k)|224 |83.86|96.65|93.6 |17.2 |34.2 |923 | |[resnetrs152.tf_in1k](https://huggingface.co/timm/resnetrs152.tf_in1k)|320 |83.72|96.61|86.6 |24.3 |48.1 |617 | |[seresnet152d.ra2_in1k](https://huggingface.co/timm/seresnet152d.ra2_in1k)|256 |83.69|96.78|66.8 |15.4 |30.6 |943 | |[seresnext101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101d_32x8d.ah_in1k)|224 |83.68|96.61|93.6 |16.7 |32.0 |986 | |[resnet152d.ra2_in1k](https://huggingface.co/timm/resnet152d.ra2_in1k)|320 |83.67|96.74|60.2 |24.1 |47.7 |706 | |[resnetrs270.tf_in1k](https://huggingface.co/timm/resnetrs270.tf_in1k)|256 |83.59|96.61|129.9 |27.1 |55.8 |526 | |[seresnext101_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101_32x8d.ah_in1k)|224 |83.58|96.4 |93.6 |16.5 |31.2 |1013 | |[resnetaa101d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa101d.sw_in12k_ft_in1k)|224 |83.54|96.83|44.6 |9.1 |17.6 |1864 | |[resnet152.a1h_in1k](https://huggingface.co/timm/resnet152.a1h_in1k)|288 |83.46|96.54|60.2 |19.1 |37.3 |904 | |[resnext101_32x16d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_swsl_ig1b_ft_in1k)|224 |83.35|96.85|194.0 |36.3 |51.2 |582 | |[resnet200d.ra2_in1k](https://huggingface.co/timm/resnet200d.ra2_in1k)|256 |83.23|96.53|64.7 |20.0 |43.1 |809 | |[resnext101_32x4d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x4d.fb_swsl_ig1b_ft_in1k)|224 |83.22|96.75|44.2 |8.0 |21.2 |1814 | |[resnext101_64x4d.c1_in1k](https://huggingface.co/timm/resnext101_64x4d.c1_in1k)|288 |83.16|96.38|83.5 |25.7 |51.6 |590 | |[resnet152d.ra2_in1k](https://huggingface.co/timm/resnet152d.ra2_in1k)|256 |83.14|96.38|60.2 |15.4 |30.5 |1096 | |[resnet101d.ra2_in1k](https://huggingface.co/timm/resnet101d.ra2_in1k)|320 |83.02|96.45|44.6 |16.5 |34.8 |992 | |[ecaresnet101d.miil_in1k](https://huggingface.co/timm/ecaresnet101d.miil_in1k)|288 |82.98|96.54|44.6 |13.4 |28.2 |1077 | |[resnext101_64x4d.tv_in1k](https://huggingface.co/timm/resnext101_64x4d.tv_in1k)|224 |82.98|96.25|83.5 |15.5 |31.2 |989 | |[resnetrs152.tf_in1k](https://huggingface.co/timm/resnetrs152.tf_in1k)|256 |82.86|96.28|86.6 |15.6 |30.8 |951 | |[resnext101_32x8d.tv2_in1k](https://huggingface.co/timm/resnext101_32x8d.tv2_in1k)|224 |82.83|96.22|88.8 |16.5 |31.2 |1099 | |[resnet152.a1h_in1k](https://huggingface.co/timm/resnet152.a1h_in1k)|224 |82.8 |96.13|60.2 |11.6 |22.6 |1486 | |[resnet101.a1h_in1k](https://huggingface.co/timm/resnet101.a1h_in1k)|288 |82.8 |96.32|44.6 |13.0 |26.8 |1291 | |[resnet152.a1_in1k](https://huggingface.co/timm/resnet152.a1_in1k)|288 |82.74|95.71|60.2 |19.1 |37.3 |905 | |[resnext101_32x8d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_wsl_ig1b_ft_in1k)|224 |82.69|96.63|88.8 |16.5 |31.2 |1100 | |[resnet152.a2_in1k](https://huggingface.co/timm/resnet152.a2_in1k)|288 |82.62|95.75|60.2 |19.1 |37.3 |904 | |[resnetaa50d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa50d.sw_in12k_ft_in1k)|288 |82.61|96.49|25.6 |8.9 |20.6 |1729 | |[resnet61q.ra2_in1k](https://huggingface.co/timm/resnet61q.ra2_in1k)|288 |82.53|96.13|36.8 |9.9 |21.5 |1773 | |[wide_resnet101_2.tv2_in1k](https://huggingface.co/timm/wide_resnet101_2.tv2_in1k)|224 |82.5 |96.02|126.9 |22.8 |21.2 |1078 | |[resnext101_64x4d.c1_in1k](https://huggingface.co/timm/resnext101_64x4d.c1_in1k)|224 |82.46|95.92|83.5 |15.5 |31.2 |987 | |[resnet51q.ra2_in1k](https://huggingface.co/timm/resnet51q.ra2_in1k)|288 |82.36|96.18|35.7 |8.1 |20.9 |1964 | |[ecaresnet50t.ra2_in1k](https://huggingface.co/timm/ecaresnet50t.ra2_in1k)|320 |82.35|96.14|25.6 |8.8 |24.1 |1386 | |[resnet101.a1_in1k](https://huggingface.co/timm/resnet101.a1_in1k)|288 |82.31|95.63|44.6 |13.0 |26.8 |1291 | |[resnetrs101.tf_in1k](https://huggingface.co/timm/resnetrs101.tf_in1k)|288 |82.29|96.01|63.6 |13.6 |28.5 |1078 | |[resnet152.tv2_in1k](https://huggingface.co/timm/resnet152.tv2_in1k)|224 |82.29|96.0 |60.2 |11.6 |22.6 |1484 | |[wide_resnet50_2.racm_in1k](https://huggingface.co/timm/wide_resnet50_2.racm_in1k)|288 |82.27|96.06|68.9 |18.9 |23.8 |1176 | |[resnet101d.ra2_in1k](https://huggingface.co/timm/resnet101d.ra2_in1k)|256 |82.26|96.07|44.6 |10.6 |22.2 |1542 | |[resnet101.a2_in1k](https://huggingface.co/timm/resnet101.a2_in1k)|288 |82.24|95.73|44.6 |13.0 |26.8 |1290 | |[seresnext50_32x4d.racm_in1k](https://huggingface.co/timm/seresnext50_32x4d.racm_in1k)|288 |82.2 |96.14|27.6 |7.0 |23.8 |1547 | |[ecaresnet101d.miil_in1k](https://huggingface.co/timm/ecaresnet101d.miil_in1k)|224 |82.18|96.05|44.6 |8.1 |17.1 |1771 | |[resnext50_32x4d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext50_32x4d.fb_swsl_ig1b_ft_in1k)|224 |82.17|96.22|25.0 |4.3 |14.4 |2943 | |[ecaresnet50t.a1_in1k](https://huggingface.co/timm/ecaresnet50t.a1_in1k)|288 |82.12|95.65|25.6 |7.1 |19.6 |1704 | |[resnext50_32x4d.a1h_in1k](https://huggingface.co/timm/resnext50_32x4d.a1h_in1k)|288 |82.03|95.94|25.0 |7.0 |23.8 |1745 | |[ecaresnet101d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet101d_pruned.miil_in1k)|288 |82.0 |96.15|24.9 |5.8 |12.7 |1787 | |[resnet61q.ra2_in1k](https://huggingface.co/timm/resnet61q.ra2_in1k)|256 |81.99|95.85|36.8 |7.8 |17.0 |2230 | |[resnext101_32x8d.tv2_in1k](https://huggingface.co/timm/resnext101_32x8d.tv2_in1k)|176 |81.98|95.72|88.8 |10.3 |19.4 |1768 | |[resnet152.a1_in1k](https://huggingface.co/timm/resnet152.a1_in1k)|224 |81.97|95.24|60.2 |11.6 |22.6 |1486 | |[resnet101.a1h_in1k](https://huggingface.co/timm/resnet101.a1h_in1k)|224 |81.93|95.75|44.6 |7.8 |16.2 |2122 | |[resnet101.tv2_in1k](https://huggingface.co/timm/resnet101.tv2_in1k)|224 |81.9 |95.77|44.6 |7.8 |16.2 |2118 | |[resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k)|224 |81.84|96.1 |194.0 |36.3 |51.2 |583 | |[resnet51q.ra2_in1k](https://huggingface.co/timm/resnet51q.ra2_in1k)|256 |81.78|95.94|35.7 |6.4 |16.6 |2471 | |[resnet152.a2_in1k](https://huggingface.co/timm/resnet152.a2_in1k)|224 |81.77|95.22|60.2 |11.6 |22.6 |1485 | |[resnetaa50d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa50d.sw_in12k_ft_in1k)|224 |81.74|96.06|25.6 |5.4 |12.4 |2813 | |[ecaresnet50t.a2_in1k](https://huggingface.co/timm/ecaresnet50t.a2_in1k)|288 |81.65|95.54|25.6 |7.1 |19.6 |1703 | |[ecaresnet50d.miil_in1k](https://huggingface.co/timm/ecaresnet50d.miil_in1k)|288 |81.64|95.88|25.6 |7.2 |19.7 |1694 | |[resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k)|224 |81.62|96.04|88.8 |16.5 |31.2 |1101 | |[wide_resnet50_2.tv2_in1k](https://huggingface.co/timm/wide_resnet50_2.tv2_in1k)|224 |81.61|95.76|68.9 |11.4 |14.4 |1930 | |[resnetaa50.a1h_in1k](https://huggingface.co/timm/resnetaa50.a1h_in1k)|288 |81.61|95.83|25.6 |8.5 |19.2 |1868 | |[resnet101.a1_in1k](https://huggingface.co/timm/resnet101.a1_in1k)|224 |81.5 |95.16|44.6 |7.8 |16.2 |2125 | |[resnext50_32x4d.a1_in1k](https://huggingface.co/timm/resnext50_32x4d.a1_in1k)|288 |81.48|95.16|25.0 |7.0 |23.8 |1745 | |[gcresnet50t.ra2_in1k](https://huggingface.co/timm/gcresnet50t.ra2_in1k)|288 |81.47|95.71|25.9 |6.9 |18.6 |2071 | |[wide_resnet50_2.racm_in1k](https://huggingface.co/timm/wide_resnet50_2.racm_in1k)|224 |81.45|95.53|68.9 |11.4 |14.4 |1929 | |[resnet50d.a1_in1k](https://huggingface.co/timm/resnet50d.a1_in1k)|288 |81.44|95.22|25.6 |7.2 |19.7 |1908 | |[ecaresnet50t.ra2_in1k](https://huggingface.co/timm/ecaresnet50t.ra2_in1k)|256 |81.44|95.67|25.6 |5.6 |15.4 |2168 | |[ecaresnetlight.miil_in1k](https://huggingface.co/timm/ecaresnetlight.miil_in1k)|288 |81.4 |95.82|30.2 |6.8 |13.9 |2132 | |[resnet50d.ra2_in1k](https://huggingface.co/timm/resnet50d.ra2_in1k)|288 |81.37|95.74|25.6 |7.2 |19.7 |1910 | |[resnet101.a2_in1k](https://huggingface.co/timm/resnet101.a2_in1k)|224 |81.32|95.19|44.6 |7.8 |16.2 |2125 | |[seresnet50.ra2_in1k](https://huggingface.co/timm/seresnet50.ra2_in1k)|288 |81.3 |95.65|28.1 |6.8 |18.4 |1803 | |[resnext50_32x4d.a2_in1k](https://huggingface.co/timm/resnext50_32x4d.a2_in1k)|288 |81.3 |95.11|25.0 |7.0 |23.8 |1746 | |[seresnext50_32x4d.racm_in1k](https://huggingface.co/timm/seresnext50_32x4d.racm_in1k)|224 |81.27|95.62|27.6 |4.3 |14.4 |2591 | |[ecaresnet50t.a1_in1k](https://huggingface.co/timm/ecaresnet50t.a1_in1k)|224 |81.26|95.16|25.6 |4.3 |11.8 |2823 | |[gcresnext50ts.ch_in1k](https://huggingface.co/timm/gcresnext50ts.ch_in1k)|288 |81.23|95.54|15.7 |4.8 |19.6 |2117 | |[senet154.gluon_in1k](https://huggingface.co/timm/senet154.gluon_in1k)|224 |81.23|95.35|115.1 |20.8 |38.7 |545 | |[resnet50.a1_in1k](https://huggingface.co/timm/resnet50.a1_in1k)|288 |81.22|95.11|25.6 |6.8 |18.4 |2089 | |[resnet50_gn.a1h_in1k](https://huggingface.co/timm/resnet50_gn.a1h_in1k)|288 |81.22|95.63|25.6 |6.8 |18.4 |676 | |[resnet50d.a2_in1k](https://huggingface.co/timm/resnet50d.a2_in1k)|288 |81.18|95.09|25.6 |7.2 |19.7 |1908 | |[resnet50.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnet50.fb_swsl_ig1b_ft_in1k)|224 |81.18|95.98|25.6 |4.1 |11.1 |3455 | |[resnext50_32x4d.tv2_in1k](https://huggingface.co/timm/resnext50_32x4d.tv2_in1k)|224 |81.17|95.34|25.0 |4.3 |14.4 |2933 | |[resnext50_32x4d.a1h_in1k](https://huggingface.co/timm/resnext50_32x4d.a1h_in1k)|224 |81.1 |95.33|25.0 |4.3 |14.4 |2934 | |[seresnet50.a2_in1k](https://huggingface.co/timm/seresnet50.a2_in1k)|288 |81.1 |95.23|28.1 |6.8 |18.4 |1801 | |[seresnet50.a1_in1k](https://huggingface.co/timm/seresnet50.a1_in1k)|288 |81.1 |95.12|28.1 |6.8 |18.4 |1799 | |[resnet152s.gluon_in1k](https://huggingface.co/timm/resnet152s.gluon_in1k)|224 |81.02|95.41|60.3 |12.9 |25.0 |1347 | |[resnet50.d_in1k](https://huggingface.co/timm/resnet50.d_in1k)|288 |80.97|95.44|25.6 |6.8 |18.4 |2085 | |[gcresnet50t.ra2_in1k](https://huggingface.co/timm/gcresnet50t.ra2_in1k)|256 |80.94|95.45|25.9 |5.4 |14.7 |2571 | |[resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k)|224 |80.93|95.73|44.2 |8.0 |21.2 |1814 | |[resnet50.c1_in1k](https://huggingface.co/timm/resnet50.c1_in1k)|288 |80.91|95.55|25.6 |6.8 |18.4 |2084 | |[seresnext101_32x4d.gluon_in1k](https://huggingface.co/timm/seresnext101_32x4d.gluon_in1k)|224 |80.9 |95.31|49.0 |8.0 |21.3 |1585 | |[seresnext101_64x4d.gluon_in1k](https://huggingface.co/timm/seresnext101_64x4d.gluon_in1k)|224 |80.9 |95.3 |88.2 |15.5 |31.2 |918 | |[resnet50.c2_in1k](https://huggingface.co/timm/resnet50.c2_in1k)|288 |80.86|95.52|25.6 |6.8 |18.4 |2085 | |[resnet50.tv2_in1k](https://huggingface.co/timm/resnet50.tv2_in1k)|224 |80.85|95.43|25.6 |4.1 |11.1 |3450 | |[ecaresnet50t.a2_in1k](https://huggingface.co/timm/ecaresnet50t.a2_in1k)|224 |80.84|95.02|25.6 |4.3 |11.8 |2821 | |[ecaresnet101d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet101d_pruned.miil_in1k)|224 |80.79|95.62|24.9 |3.5 |7.7 |2961 | |[seresnet33ts.ra2_in1k](https://huggingface.co/timm/seresnet33ts.ra2_in1k)|288 |80.79|95.36|19.8 |6.0 |14.8 |2506 | |[ecaresnet50d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet50d_pruned.miil_in1k)|288 |80.79|95.58|19.9 |4.2 |10.6 |2349 | |[resnet50.a2_in1k](https://huggingface.co/timm/resnet50.a2_in1k)|288 |80.78|94.99|25.6 |6.8 |18.4 |2088 | |[resnet50.b1k_in1k](https://huggingface.co/timm/resnet50.b1k_in1k)|288 |80.71|95.43|25.6 |6.8 |18.4 |2087 | |[resnext50_32x4d.ra_in1k](https://huggingface.co/timm/resnext50_32x4d.ra_in1k)|288 |80.7 |95.39|25.0 |7.0 |23.8 |1749 | |[resnetrs101.tf_in1k](https://huggingface.co/timm/resnetrs101.tf_in1k)|192 |80.69|95.24|63.6 |6.0 |12.7 |2270 | |[resnet50d.a1_in1k](https://huggingface.co/timm/resnet50d.a1_in1k)|224 |80.68|94.71|25.6 |4.4 |11.9 |3162 | |[eca_resnet33ts.ra2_in1k](https://huggingface.co/timm/eca_resnet33ts.ra2_in1k)|288 |80.68|95.36|19.7 |6.0 |14.8 |2637 | |[resnet50.a1h_in1k](https://huggingface.co/timm/resnet50.a1h_in1k)|224 |80.67|95.3 |25.6 |4.1 |11.1 |3452 | |[resnext50d_32x4d.bt_in1k](https://huggingface.co/timm/resnext50d_32x4d.bt_in1k)|288 |80.67|95.42|25.0 |7.4 |25.1 |1626 | |[resnetaa50.a1h_in1k](https://huggingface.co/timm/resnetaa50.a1h_in1k)|224 |80.63|95.21|25.6 |5.2 |11.6 |3034 | |[ecaresnet50d.miil_in1k](https://huggingface.co/timm/ecaresnet50d.miil_in1k)|224 |80.61|95.32|25.6 |4.4 |11.9 |2813 | |[resnext101_64x4d.gluon_in1k](https://huggingface.co/timm/resnext101_64x4d.gluon_in1k)|224 |80.61|94.99|83.5 |15.5 |31.2 |989 | |[gcresnet33ts.ra2_in1k](https://huggingface.co/timm/gcresnet33ts.ra2_in1k)|288 |80.6 |95.31|19.9 |6.0 |14.8 |2578 | |[gcresnext50ts.ch_in1k](https://huggingface.co/timm/gcresnext50ts.ch_in1k)|256 |80.57|95.17|15.7 |3.8 |15.5 |2710 | |[resnet152.a3_in1k](https://huggingface.co/timm/resnet152.a3_in1k)|224 |80.56|95.0 |60.2 |11.6 |22.6 |1483 | |[resnet50d.ra2_in1k](https://huggingface.co/timm/resnet50d.ra2_in1k)|224 |80.53|95.16|25.6 |4.4 |11.9 |3164 | |[resnext50_32x4d.a1_in1k](https://huggingface.co/timm/resnext50_32x4d.a1_in1k)|224 |80.53|94.46|25.0 |4.3 |14.4 |2930 | |[wide_resnet101_2.tv2_in1k](https://huggingface.co/timm/wide_resnet101_2.tv2_in1k)|176 |80.48|94.98|126.9 |14.3 |13.2 |1719 | |[resnet152d.gluon_in1k](https://huggingface.co/timm/resnet152d.gluon_in1k)|224 |80.47|95.2 |60.2 |11.8 |23.4 |1428 | |[resnet50.b2k_in1k](https://huggingface.co/timm/resnet50.b2k_in1k)|288 |80.45|95.32|25.6 |6.8 |18.4 |2086 | |[ecaresnetlight.miil_in1k](https://huggingface.co/timm/ecaresnetlight.miil_in1k)|224 |80.45|95.24|30.2 |4.1 |8.4 |3530 | |[resnext50_32x4d.a2_in1k](https://huggingface.co/timm/resnext50_32x4d.a2_in1k)|224 |80.45|94.63|25.0 |4.3 |14.4 |2936 | |[wide_resnet50_2.tv2_in1k](https://huggingface.co/timm/wide_resnet50_2.tv2_in1k)|176 |80.43|95.09|68.9 |7.3 |9.0 |3015 | |[resnet101d.gluon_in1k](https://huggingface.co/timm/resnet101d.gluon_in1k)|224 |80.42|95.01|44.6 |8.1 |17.0 |2007 | |[resnet50.a1_in1k](https://huggingface.co/timm/resnet50.a1_in1k)|224 |80.38|94.6 |25.6 |4.1 |11.1 |3461 | |[seresnet33ts.ra2_in1k](https://huggingface.co/timm/seresnet33ts.ra2_in1k)|256 |80.36|95.1 |19.8 |4.8 |11.7 |3267 | |[resnext101_32x4d.gluon_in1k](https://huggingface.co/timm/resnext101_32x4d.gluon_in1k)|224 |80.34|94.93|44.2 |8.0 |21.2 |1814 | |[resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k)|224 |80.32|95.4 |25.0 |4.3 |14.4 |2941 | |[resnet101s.gluon_in1k](https://huggingface.co/timm/resnet101s.gluon_in1k)|224 |80.28|95.16|44.7 |9.2 |18.6 |1851 | |[seresnet50.ra2_in1k](https://huggingface.co/timm/seresnet50.ra2_in1k)|224 |80.26|95.08|28.1 |4.1 |11.1 |2972 | |[resnetblur50.bt_in1k](https://huggingface.co/timm/resnetblur50.bt_in1k)|288 |80.24|95.24|25.6 |8.5 |19.9 |1523 | |[resnet50d.a2_in1k](https://huggingface.co/timm/resnet50d.a2_in1k)|224 |80.22|94.63|25.6 |4.4 |11.9 |3162 | |[resnet152.tv2_in1k](https://huggingface.co/timm/resnet152.tv2_in1k)|176 |80.2 |94.64|60.2 |7.2 |14.0 |2346 | |[seresnet50.a2_in1k](https://huggingface.co/timm/seresnet50.a2_in1k)|224 |80.08|94.74|28.1 |4.1 |11.1 |2969 | |[eca_resnet33ts.ra2_in1k](https://huggingface.co/timm/eca_resnet33ts.ra2_in1k)|256 |80.08|94.97|19.7 |4.8 |11.7 |3284 | |[gcresnet33ts.ra2_in1k](https://huggingface.co/timm/gcresnet33ts.ra2_in1k)|256 |80.06|94.99|19.9 |4.8 |11.7 |3216 | |[resnet50_gn.a1h_in1k](https://huggingface.co/timm/resnet50_gn.a1h_in1k)|224 |80.06|94.95|25.6 |4.1 |11.1 |1109 | |[seresnet50.a1_in1k](https://huggingface.co/timm/seresnet50.a1_in1k)|224 |80.02|94.71|28.1 |4.1 |11.1 |2962 | |[resnet50.ram_in1k](https://huggingface.co/timm/resnet50.ram_in1k)|288 |79.97|95.05|25.6 |6.8 |18.4 |2086 | |[resnet152c.gluon_in1k](https://huggingface.co/timm/resnet152c.gluon_in1k)|224 |79.92|94.84|60.2 |11.8 |23.4 |1455 | |[seresnext50_32x4d.gluon_in1k](https://huggingface.co/timm/seresnext50_32x4d.gluon_in1k)|224 |79.91|94.82|27.6 |4.3 |14.4 |2591 | |[resnet50.d_in1k](https://huggingface.co/timm/resnet50.d_in1k)|224 |79.91|94.67|25.6 |4.1 |11.1 |3456 | |[resnet101.tv2_in1k](https://huggingface.co/timm/resnet101.tv2_in1k)|176 |79.9 |94.6 |44.6 |4.9 |10.1 |3341 | |[resnetrs50.tf_in1k](https://huggingface.co/timm/resnetrs50.tf_in1k)|224 |79.89|94.97|35.7 |4.5 |12.1 |2774 | |[resnet50.c2_in1k](https://huggingface.co/timm/resnet50.c2_in1k)|224 |79.88|94.87|25.6 |4.1 |11.1 |3455 | |[ecaresnet26t.ra2_in1k](https://huggingface.co/timm/ecaresnet26t.ra2_in1k)|320 |79.86|95.07|16.0 |5.2 |16.4 |2168 | |[resnet50.a2_in1k](https://huggingface.co/timm/resnet50.a2_in1k)|224 |79.85|94.56|25.6 |4.1 |11.1 |3460 | |[resnet50.ra_in1k](https://huggingface.co/timm/resnet50.ra_in1k)|288 |79.83|94.97|25.6 |6.8 |18.4 |2087 | |[resnet101.a3_in1k](https://huggingface.co/timm/resnet101.a3_in1k)|224 |79.82|94.62|44.6 |7.8 |16.2 |2114 | |[resnext50_32x4d.ra_in1k](https://huggingface.co/timm/resnext50_32x4d.ra_in1k)|224 |79.76|94.6 |25.0 |4.3 |14.4 |2943 | |[resnet50.c1_in1k](https://huggingface.co/timm/resnet50.c1_in1k)|224 |79.74|94.95|25.6 |4.1 |11.1 |3455 | |[ecaresnet50d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet50d_pruned.miil_in1k)|224 |79.74|94.87|19.9 |2.5 |6.4 |3929 | |[resnet33ts.ra2_in1k](https://huggingface.co/timm/resnet33ts.ra2_in1k)|288 |79.71|94.83|19.7 |6.0 |14.8 |2710 | |[resnet152.gluon_in1k](https://huggingface.co/timm/resnet152.gluon_in1k)|224 |79.68|94.74|60.2 |11.6 |22.6 |1486 | |[resnext50d_32x4d.bt_in1k](https://huggingface.co/timm/resnext50d_32x4d.bt_in1k)|224 |79.67|94.87|25.0 |4.5 |15.2 |2729 | |[resnet50.bt_in1k](https://huggingface.co/timm/resnet50.bt_in1k)|288 |79.63|94.91|25.6 |6.8 |18.4 |2086 | |[ecaresnet50t.a3_in1k](https://huggingface.co/timm/ecaresnet50t.a3_in1k)|224 |79.56|94.72|25.6 |4.3 |11.8 |2805 | |[resnet101c.gluon_in1k](https://huggingface.co/timm/resnet101c.gluon_in1k)|224 |79.53|94.58|44.6 |8.1 |17.0 |2062 | |[resnet50.b1k_in1k](https://huggingface.co/timm/resnet50.b1k_in1k)|224 |79.52|94.61|25.6 |4.1 |11.1 |3459 | |[resnet50.tv2_in1k](https://huggingface.co/timm/resnet50.tv2_in1k)|176 |79.42|94.64|25.6 |2.6 |6.9 |5397 | |[resnet32ts.ra2_in1k](https://huggingface.co/timm/resnet32ts.ra2_in1k)|288 |79.4 |94.66|18.0 |5.9 |14.6 |2752 | |[resnet50.b2k_in1k](https://huggingface.co/timm/resnet50.b2k_in1k)|224 |79.38|94.57|25.6 |4.1 |11.1 |3459 | |[resnext50_32x4d.tv2_in1k](https://huggingface.co/timm/resnext50_32x4d.tv2_in1k)|176 |79.37|94.3 |25.0 |2.7 |9.0 |4577 | |[resnext50_32x4d.gluon_in1k](https://huggingface.co/timm/resnext50_32x4d.gluon_in1k)|224 |79.36|94.43|25.0 |4.3 |14.4 |2942 | |[resnext101_32x8d.tv_in1k](https://huggingface.co/timm/resnext101_32x8d.tv_in1k)|224 |79.31|94.52|88.8 |16.5 |31.2 |1100 | |[resnet101.gluon_in1k](https://huggingface.co/timm/resnet101.gluon_in1k)|224 |79.31|94.53|44.6 |7.8 |16.2 |2125 | |[resnetblur50.bt_in1k](https://huggingface.co/timm/resnetblur50.bt_in1k)|224 |79.31|94.63|25.6 |5.2 |12.0 |2524 | |[resnet50.a1h_in1k](https://huggingface.co/timm/resnet50.a1h_in1k)|176 |79.27|94.49|25.6 |2.6 |6.9 |5404 | |[resnext50_32x4d.a3_in1k](https://huggingface.co/timm/resnext50_32x4d.a3_in1k)|224 |79.25|94.31|25.0 |4.3 |14.4 |2931 | |[resnet50.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnet50.fb_ssl_yfcc100m_ft_in1k)|224 |79.22|94.84|25.6 |4.1 |11.1 |3451 | |[resnet33ts.ra2_in1k](https://huggingface.co/timm/resnet33ts.ra2_in1k)|256 |79.21|94.56|19.7 |4.8 |11.7 |3392 | |[resnet50d.gluon_in1k](https://huggingface.co/timm/resnet50d.gluon_in1k)|224 |79.07|94.48|25.6 |4.4 |11.9 |3162 | |[resnet50.ram_in1k](https://huggingface.co/timm/resnet50.ram_in1k)|224 |79.03|94.38|25.6 |4.1 |11.1 |3453 | |[resnet50.am_in1k](https://huggingface.co/timm/resnet50.am_in1k)|224 |79.01|94.39|25.6 |4.1 |11.1 |3461 | |[resnet32ts.ra2_in1k](https://huggingface.co/timm/resnet32ts.ra2_in1k)|256 |79.01|94.37|18.0 |4.6 |11.6 |3440 | |[ecaresnet26t.ra2_in1k](https://huggingface.co/timm/ecaresnet26t.ra2_in1k)|256 |78.9 |94.54|16.0 |3.4 |10.5 |3421 | |[resnet152.a3_in1k](https://huggingface.co/timm/resnet152.a3_in1k)|160 |78.89|94.11|60.2 |5.9 |11.5 |2745 | |[wide_resnet101_2.tv_in1k](https://huggingface.co/timm/wide_resnet101_2.tv_in1k)|224 |78.84|94.28|126.9 |22.8 |21.2 |1079 | |[seresnext26d_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26d_32x4d.bt_in1k)|288 |78.83|94.24|16.8 |4.5 |16.8 |2251 | |[resnet50.ra_in1k](https://huggingface.co/timm/resnet50.ra_in1k)|224 |78.81|94.32|25.6 |4.1 |11.1 |3454 | |[seresnext26t_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26t_32x4d.bt_in1k)|288 |78.74|94.33|16.8 |4.5 |16.7 |2264 | |[resnet50s.gluon_in1k](https://huggingface.co/timm/resnet50s.gluon_in1k)|224 |78.72|94.23|25.7 |5.5 |13.5 |2796 | |[resnet50d.a3_in1k](https://huggingface.co/timm/resnet50d.a3_in1k)|224 |78.71|94.24|25.6 |4.4 |11.9 |3154 | |[wide_resnet50_2.tv_in1k](https://huggingface.co/timm/wide_resnet50_2.tv_in1k)|224 |78.47|94.09|68.9 |11.4 |14.4 |1934 | |[resnet50.bt_in1k](https://huggingface.co/timm/resnet50.bt_in1k)|224 |78.46|94.27|25.6 |4.1 |11.1 |3454 | |[resnet34d.ra2_in1k](https://huggingface.co/timm/resnet34d.ra2_in1k)|288 |78.43|94.35|21.8 |6.5 |7.5 |3291 | |[gcresnext26ts.ch_in1k](https://huggingface.co/timm/gcresnext26ts.ch_in1k)|288 |78.42|94.04|10.5 |3.1 |13.3 |3226 | |[resnet26t.ra2_in1k](https://huggingface.co/timm/resnet26t.ra2_in1k)|320 |78.33|94.13|16.0 |5.2 |16.4 |2391 | |[resnet152.tv_in1k](https://huggingface.co/timm/resnet152.tv_in1k)|224 |78.32|94.04|60.2 |11.6 |22.6 |1487 | |[seresnext26ts.ch_in1k](https://huggingface.co/timm/seresnext26ts.ch_in1k)|288 |78.28|94.1 |10.4 |3.1 |13.3 |3062 | |[bat_resnext26ts.ch_in1k](https://huggingface.co/timm/bat_resnext26ts.ch_in1k)|256 |78.25|94.1 |10.7 |2.5 |12.5 |3393 | |[resnet50.a3_in1k](https://huggingface.co/timm/resnet50.a3_in1k)|224 |78.06|93.78|25.6 |4.1 |11.1 |3450 | |[resnet50c.gluon_in1k](https://huggingface.co/timm/resnet50c.gluon_in1k)|224 |78.0 |93.99|25.6 |4.4 |11.9 |3286 | |[eca_resnext26ts.ch_in1k](https://huggingface.co/timm/eca_resnext26ts.ch_in1k)|288 |78.0 |93.91|10.3 |3.1 |13.3 |3297 | |[seresnext26t_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26t_32x4d.bt_in1k)|224 |77.98|93.75|16.8 |2.7 |10.1 |3841 | |[resnet34.a1_in1k](https://huggingface.co/timm/resnet34.a1_in1k)|288 |77.92|93.77|21.8 |6.1 |6.2 |3609 | |[resnet101.a3_in1k](https://huggingface.co/timm/resnet101.a3_in1k)|160 |77.88|93.71|44.6 |4.0 |8.3 |3926 | |[resnet26t.ra2_in1k](https://huggingface.co/timm/resnet26t.ra2_in1k)|256 |77.87|93.84|16.0 |3.4 |10.5 |3772 | |[seresnext26ts.ch_in1k](https://huggingface.co/timm/seresnext26ts.ch_in1k)|256 |77.86|93.79|10.4 |2.4 |10.5 |4263 | |[resnetrs50.tf_in1k](https://huggingface.co/timm/resnetrs50.tf_in1k)|160 |77.82|93.81|35.7 |2.3 |6.2 |5238 | |[gcresnext26ts.ch_in1k](https://huggingface.co/timm/gcresnext26ts.ch_in1k)|256 |77.81|93.82|10.5 |2.4 |10.5 |4183 | |[ecaresnet50t.a3_in1k](https://huggingface.co/timm/ecaresnet50t.a3_in1k)|160 |77.79|93.6 |25.6 |2.2 |6.0 |5329 | |[resnext50_32x4d.a3_in1k](https://huggingface.co/timm/resnext50_32x4d.a3_in1k)|160 |77.73|93.32|25.0 |2.2 |7.4 |5576 | |[resnext50_32x4d.tv_in1k](https://huggingface.co/timm/resnext50_32x4d.tv_in1k)|224 |77.61|93.7 |25.0 |4.3 |14.4 |2944 | |[seresnext26d_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26d_32x4d.bt_in1k)|224 |77.59|93.61|16.8 |2.7 |10.2 |3807 | |[resnet50.gluon_in1k](https://huggingface.co/timm/resnet50.gluon_in1k)|224 |77.58|93.72|25.6 |4.1 |11.1 |3455 | |[eca_resnext26ts.ch_in1k](https://huggingface.co/timm/eca_resnext26ts.ch_in1k)|256 |77.44|93.56|10.3 |2.4 |10.5 |4284 | |[resnet26d.bt_in1k](https://huggingface.co/timm/resnet26d.bt_in1k)|288 |77.41|93.63|16.0 |4.3 |13.5 |2907 | |[resnet101.tv_in1k](https://huggingface.co/timm/resnet101.tv_in1k)|224 |77.38|93.54|44.6 |7.8 |16.2 |2125 | |[resnet50d.a3_in1k](https://huggingface.co/timm/resnet50d.a3_in1k)|160 |77.22|93.27|25.6 |2.2 |6.1 |5982 | |[resnext26ts.ra2_in1k](https://huggingface.co/timm/resnext26ts.ra2_in1k)|288 |77.17|93.47|10.3 |3.1 |13.3 |3392 | |[resnet34.a2_in1k](https://huggingface.co/timm/resnet34.a2_in1k)|288 |77.15|93.27|21.8 |6.1 |6.2 |3615 | |[resnet34d.ra2_in1k](https://huggingface.co/timm/resnet34d.ra2_in1k)|224 |77.1 |93.37|21.8 |3.9 |4.5 |5436 | |[seresnet50.a3_in1k](https://huggingface.co/timm/seresnet50.a3_in1k)|224 |77.02|93.07|28.1 |4.1 |11.1 |2952 | |[resnext26ts.ra2_in1k](https://huggingface.co/timm/resnext26ts.ra2_in1k)|256 |76.78|93.13|10.3 |2.4 |10.5 |4410 | |[resnet26d.bt_in1k](https://huggingface.co/timm/resnet26d.bt_in1k)|224 |76.7 |93.17|16.0 |2.6 |8.2 |4859 | |[resnet34.bt_in1k](https://huggingface.co/timm/resnet34.bt_in1k)|288 |76.5 |93.35|21.8 |6.1 |6.2 |3617 | |[resnet34.a1_in1k](https://huggingface.co/timm/resnet34.a1_in1k)|224 |76.42|92.87|21.8 |3.7 |3.7 |5984 | |[resnet26.bt_in1k](https://huggingface.co/timm/resnet26.bt_in1k)|288 |76.35|93.18|16.0 |3.9 |12.2 |3331 | |[resnet50.tv_in1k](https://huggingface.co/timm/resnet50.tv_in1k)|224 |76.13|92.86|25.6 |4.1 |11.1 |3457 | |[resnet50.a3_in1k](https://huggingface.co/timm/resnet50.a3_in1k)|160 |75.96|92.5 |25.6 |2.1 |5.7 |6490 | |[resnet34.a2_in1k](https://huggingface.co/timm/resnet34.a2_in1k)|224 |75.52|92.44|21.8 |3.7 |3.7 |5991 | |[resnet26.bt_in1k](https://huggingface.co/timm/resnet26.bt_in1k)|224 |75.3 |92.58|16.0 |2.4 |7.4 |5583 | |[resnet34.bt_in1k](https://huggingface.co/timm/resnet34.bt_in1k)|224 |75.16|92.18|21.8 |3.7 |3.7 |5994 | |[seresnet50.a3_in1k](https://huggingface.co/timm/seresnet50.a3_in1k)|160 |75.1 |92.08|28.1 |2.1 |5.7 |5513 | |[resnet34.gluon_in1k](https://huggingface.co/timm/resnet34.gluon_in1k)|224 |74.57|91.98|21.8 |3.7 |3.7 |5984 | |[resnet18d.ra2_in1k](https://huggingface.co/timm/resnet18d.ra2_in1k)|288 |73.81|91.83|11.7 |3.4 |5.4 |5196 | |[resnet34.tv_in1k](https://huggingface.co/timm/resnet34.tv_in1k)|224 |73.32|91.42|21.8 |3.7 |3.7 |5979 | |[resnet18.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnet18.fb_swsl_ig1b_ft_in1k)|224 |73.28|91.73|11.7 |1.8 |2.5 |10213 | |[resnet18.a1_in1k](https://huggingface.co/timm/resnet18.a1_in1k)|288 |73.16|91.03|11.7 |3.0 |4.1 |6050 | |[resnet34.a3_in1k](https://huggingface.co/timm/resnet34.a3_in1k)|224 |72.98|91.11|21.8 |3.7 |3.7 |5967 | |[resnet18.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnet18.fb_ssl_yfcc100m_ft_in1k)|224 |72.6 |91.42|11.7 |1.8 |2.5 |10213 | |[resnet18.a2_in1k](https://huggingface.co/timm/resnet18.a2_in1k)|288 |72.37|90.59|11.7 |3.0 |4.1 |6051 | |[resnet14t.c3_in1k](https://huggingface.co/timm/resnet14t.c3_in1k)|224 |72.26|90.31|10.1 |1.7 |5.8 |7026 | |[resnet18d.ra2_in1k](https://huggingface.co/timm/resnet18d.ra2_in1k)|224 |72.26|90.68|11.7 |2.1 |3.3 |8707 | |[resnet18.a1_in1k](https://huggingface.co/timm/resnet18.a1_in1k)|224 |71.49|90.07|11.7 |1.8 |2.5 |10187 | |[resnet14t.c3_in1k](https://huggingface.co/timm/resnet14t.c3_in1k)|176 |71.31|89.69|10.1 |1.1 |3.6 |10970 | |[resnet18.gluon_in1k](https://huggingface.co/timm/resnet18.gluon_in1k)|224 |70.84|89.76|11.7 |1.8 |2.5 |10210 | |[resnet18.a2_in1k](https://huggingface.co/timm/resnet18.a2_in1k)|224 |70.64|89.47|11.7 |1.8 |2.5 |10194 | |[resnet34.a3_in1k](https://huggingface.co/timm/resnet34.a3_in1k)|160 |70.56|89.52|21.8 |1.9 |1.9 |10737 | |[resnet18.tv_in1k](https://huggingface.co/timm/resnet18.tv_in1k)|224 |69.76|89.07|11.7 |1.8 |2.5 |10205 | |[resnet10t.c3_in1k](https://huggingface.co/timm/resnet10t.c3_in1k)|224 |68.34|88.03|5.4 |1.1 |2.4 |13079 | |[resnet18.a3_in1k](https://huggingface.co/timm/resnet18.a3_in1k)|224 |68.25|88.17|11.7 |1.8 |2.5 |10167 | |[resnet10t.c3_in1k](https://huggingface.co/timm/resnet10t.c3_in1k)|176 |66.71|86.96|5.4 |0.7 |1.5 |20327 | |[resnet18.a3_in1k](https://huggingface.co/timm/resnet18.a3_in1k)|160 |65.66|86.26|11.7 |0.9 |1.3 |18229 | ## Citation ```bibtex @inproceedings{wightman2021resnet, title={ResNet strikes back: An improved training procedure in timm}, author={Wightman, Ross and Touvron, Hugo and Jegou, Herve}, booktitle={NeurIPS 2021 Workshop on ImageNet: Past, Present, and Future} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ``` ```bibtex @article{He2015, author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun}, title = {Deep Residual Learning for Image Recognition}, journal = {arXiv preprint arXiv:1512.03385}, year = {2015} } ```
snrspeaks/t5-one-line-summary
snrspeaks
"2021-06-23T14:20:22Z"
23,298
92
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "dataset:arxiv", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text2text-generation
"2022-03-02T23:29:05Z"
--- datasets: - arxiv widget: - text: "summarize: We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production machinelearning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and handling contradictory or incomplete supervision data. Overton automates the life cycle of model construction, deployment, and monitoring by providing a set of novel high-level, declarative abstractions. Overton's vision is to shift developers to these higher-level tasks instead of lower-level machine learning tasks. In fact, using Overton, engineers can build deep-learning-based applications without writing any code in frameworks like TensorFlow. For over a year, Overton has been used in production to support multiple applications in both near-real-time applications and back-of-house processing. In that time, Overton-based applications have answered billions of queries in multiple languages and processed trillions of records reducing errors 1.7-2.9 times versus production systems." license: mit --- # T5 One Line Summary A T5 model trained on 370,000 research papers, to generate one line summary based on description/abstract of the papers. It is trained using [simpleT5](https://github.com/Shivanandroy/simpleT5) library - A python package built on top of pytorch lightning⚡️ & transformers🤗 to quickly train T5 models ## Usage:[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1HrfT8IKLXvZzPFpl1EhZ3s_iiXG3O2VY?usp=sharing) ```python abstract = """We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production machine learning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and handling contradictory or incomplete supervision data. Overton automates the life cycle of model construction, deployment, and monitoring by providing a set of novel high-level, declarative abstractions. Overton's vision is to shift developers to these higher-level tasks instead of lower-level machine learning tasks. In fact, using Overton, engineers can build deep-learning-based applications without writing any code in frameworks like TensorFlow. For over a year, Overton has been used in production to support multiple applications in both near-real-time applications and back-of-house processing. In that time, Overton-based applications have answered billions of queries in multiple languages and processed trillions of records reducing errors 1.7-2.9 times versus production systems. """ ``` ### Using Transformers🤗 ```python model_name = "snrspeaks/t5-one-line-summary" from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model = AutoModelForSeq2SeqLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) input_ids = tokenizer.encode("summarize: " + abstract, return_tensors="pt", add_special_tokens=True) generated_ids = model.generate(input_ids=input_ids,num_beams=5,max_length=50,repetition_penalty=2.5,length_penalty=1,early_stopping=True,num_return_sequences=3) preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids] print(preds) # output ["Overton: Building, Deploying, and Monitoring Machine Learning Systems for Engineers", "Overton: A System for Building, Monitoring, and Improving Production Machine Learning Systems", "Overton: Building, Monitoring, and Improving Production Machine Learning Systems"] ``` ### Using simpleT5⚡️ ```python # pip install --upgrade simplet5 from simplet5 import SimpleT5 model = SimpleT5() model.load_model("t5","snrspeaks/t5-one-line-summary") model.predict(abstract) # output "Overton: Building, Deploying, and Monitoring Machine Learning Systems for Engineers" ```
openlm-research/open_llama_7b
openlm-research
"2023-06-16T00:45:23Z"
23,293
121
transformers
[ "transformers", "pytorch", "llama", "text-generation", "dataset:togethercomputer/RedPajama-Data-1T", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2023-06-07T08:54:38Z"
--- license: apache-2.0 datasets: - togethercomputer/RedPajama-Data-1T --- # OpenLLaMA: An Open Reproduction of LLaMA In this repo, we present a permissively licensed open source reproduction of Meta AI's [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) large language model. We are releasing a 7B and 3B model trained on 1T tokens, as well as the preview of a 13B model trained on 600B tokens. We provide PyTorch and JAX weights of pre-trained OpenLLaMA models, as well as evaluation results and comparison against the original LLaMA models. Please see the [project homepage of OpenLLaMA](https://github.com/openlm-research/open_llama) for more details. ## Weights Release, License and Usage We release the weights in two formats: an EasyLM format to be use with our [EasyLM framework](https://github.com/young-geng/EasyLM), and a PyTorch format to be used with the [Hugging Face transformers](https://huggingface.co/docs/transformers/index) library. Both our training framework EasyLM and the checkpoint weights are licensed permissively under the Apache 2.0 license. ### Loading the Weights with Hugging Face Transformers Preview checkpoints can be directly loaded from Hugging Face Hub. **Please note that it is advised to avoid using the Hugging Face fast tokenizer for now, as we’ve observed that the auto-converted fast tokenizer sometimes gives incorrect tokenizations.** This can be achieved by directly using the `LlamaTokenizer` class, or passing in the `use_fast=False` option for the `AutoTokenizer` class. See the following example for usage. ```python import torch from transformers import LlamaTokenizer, LlamaForCausalLM model_path = 'openlm-research/open_llama_3b' # model_path = 'openlm-research/open_llama_7b' tokenizer = LlamaTokenizer.from_pretrained(model_path) model = LlamaForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map='auto', ) prompt = 'Q: What is the largest animal?\nA:' input_ids = tokenizer(prompt, return_tensors="pt").input_ids generation_output = model.generate( input_ids=input_ids, max_new_tokens=32 ) print(tokenizer.decode(generation_output[0])) ``` For more advanced usage, please follow the [transformers LLaMA documentation](https://huggingface.co/docs/transformers/main/model_doc/llama). ### Evaluating with LM-Eval-Harness The model can be evaluated with [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness). However, due to the aforementioned tokenizer issue, we need to avoid using the fast tokenizer to obtain the correct results. This can be achieved by passing in `use_fast=False` to [this part of lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/blob/4b701e228768052cfae9043dca13e82052ca5eea/lm_eval/models/huggingface.py#LL313C9-L316C10), as shown in the example below: ```python tokenizer = self.AUTO_TOKENIZER_CLASS.from_pretrained( pretrained if tokenizer is None else tokenizer, revision=revision + ("/" + subfolder if subfolder is not None else ""), use_fast=False ) ``` ### Loading the Weights with EasyLM For using the weights in our EasyLM framework, please refer to the [LLaMA documentation of EasyLM](https://github.com/young-geng/EasyLM/blob/main/docs/llama.md). Note that unlike the original LLaMA model, our OpenLLaMA tokenizer and weights are trained completely from scratch so it is no longer needed to obtain the original LLaMA tokenizer and weights. Note that we use BOS (beginning of sentence) token (id=1) during training, so it is best to prepend this token for best performance during few-shot evaluation. ## Dataset and Training We train our models on the [RedPajama](https://www.together.xyz/blog/redpajama) dataset released by [Together](https://www.together.xyz/), which is a reproduction of the LLaMA training dataset containing over 1.2 trillion tokens. We follow the exactly same preprocessing steps and training hyperparameters as the original LLaMA paper, including model architecture, context length, training steps, learning rate schedule, and optimizer. The only difference between our setting and the original one is the dataset used: OpenLLaMA employs the RedPajama dataset rather than the one utilized by the original LLaMA. We train the models on cloud TPU-v4s using [EasyLM](https://github.com/young-geng/EasyLM), a JAX based training pipeline we developed for training and fine-tuning large language models. We employ a combination of normal data parallelism and [fully sharded data parallelism (also know as ZeRO stage 3)](https://engineering.fb.com/2021/07/15/open-source/fsdp/) to balance the training throughput and memory usage. Overall we reach a throughput of over 2200 tokens / second / TPU-v4 chip for our 7B model. ## Evaluation We evaluated OpenLLaMA on a wide range of tasks using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). The LLaMA results are generated by running the original LLaMA model on the same evaluation metrics. We note that our results for the LLaMA model differ slightly from the original LLaMA paper, which we believe is a result of different evaluation protocols. Similar differences have been reported in [this issue of lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/issues/443). Additionally, we present the results of GPT-J, a 6B parameter model trained on the [Pile](https://pile.eleuther.ai/) dataset by [EleutherAI](https://www.eleuther.ai/). The original LLaMA model was trained for 1 trillion tokens and GPT-J was trained for 500 billion tokens. We present the results in the table below. OpenLLaMA exhibits comparable performance to the original LLaMA and GPT-J across a majority of tasks, and outperforms them in some tasks. | **Task/Metric** | GPT-J 6B | LLaMA 7B | OpenLLaMA 7B | OpenLLaMA 3B | OpenLLaMA 13B 600BT | | ---------------------- | -------- | -------- | ------------ | ------------ | ------------------- | | anli_r1/acc | 0.32 | 0.35 | 0.33 | 0.33 | 0.33 | | anli_r2/acc | 0.34 | 0.34 | 0.36 | 0.32 | 0.35 | | anli_r3/acc | 0.35 | 0.37 | 0.38 | 0.35 | 0.38 | | arc_challenge/acc | 0.34 | 0.39 | 0.37 | 0.34 | 0.39 | | arc_challenge/acc_norm | 0.37 | 0.41 | 0.38 | 0.37 | 0.42 | | arc_easy/acc | 0.67 | 0.68 | 0.72 | 0.69 | 0.74 | | arc_easy/acc_norm | 0.62 | 0.52 | 0.68 | 0.65 | 0.70 | | ddboolq/acc | 0.50 | 0.56 | 0.53 | 0.49 | 0.71 | | hellaswag/acc | 0.36 | 0.36 | 0.63 | 0.43 | 0.54 | | hellaswag/acc_norm | 0.66 | 0.73 | 0.72 | 0.67 | 0.73 | | openbookqa/acc | 0.29 | 0.29 | 0.30 | 0.27 | 0.30 | | openbookqa/acc_norm | 0.38 | 0.41 | 0.40 | 0.40 | 0.41 | | piqa/acc | 0.75 | 0.78 | 0.76 | 0.75 | 0.77 | | piqa/acc_norm | 0.76 | 0.78 | 0.77 | 0.76 | 0.78 | | record/em | 0.88 | 0.91 | 0.89 | 0.88 | 0.90 | | record/f1 | 0.89 | 0.91 | 0.90 | 0.89 | 0.90 | | rte/acc | 0.54 | 0.56 | 0.60 | 0.58 | 0.65 | | truthfulqa_mc/mc1 | 0.20 | 0.21 | 0.23 | 0.22 | 0.22 | | truthfulqa_mc/mc2 | 0.36 | 0.34 | 0.35 | 0.35 | 0.35 | | wic/acc | 0.50 | 0.50 | 0.51 | 0.48 | 0.49 | | winogrande/acc | 0.64 | 0.68 | 0.67 | 0.62 | 0.67 | | Average | 0.51 | 0.53 | 0.55 | 0.52 | 0.56 | We removed the task CB and WSC from our benchmark, as our model performs suspiciously well on these two tasks. We hypothesize that there could be a benchmark data contamination in the training set. ## Contact We would love to get feedback from the community. If you have any questions, please open an issue or contact us. OpenLLaMA is developed by: [Xinyang Geng](https://young-geng.xyz/)* and [Hao Liu](https://www.haoliu.site/)* from Berkeley AI Research. *Equal Contribution ## Acknowledgment We thank the [Google TPU Research Cloud](https://sites.research.google/trc/about/) program for providing part of the computation resources. We’d like to specially thank Jonathan Caton from TPU Research Cloud for helping us organizing compute resources, Rafi Witten from the Google Cloud team and James Bradbury from the Google JAX team for helping us optimizing our training throughput. We’d also want to thank Charlie Snell, Gautier Izacard, Eric Wallace, Lianmin Zheng and our user community for the discussions and feedback. The OpenLLaMA 13B model is trained in collaboration with [Stability AI](https://stability.ai/), and we thank Stability AI for providing the computation resources. We’d like to especially thank David Ha and Shivanshu Purohit for the coordinating the logistics and providing engineering support. ## Reference If you found OpenLLaMA useful in your research or applications, please cite using the following BibTeX: ``` @software{openlm2023openllama, author = {Geng, Xinyang and Liu, Hao}, title = {OpenLLaMA: An Open Reproduction of LLaMA}, month = May, year = 2023, url = {https://github.com/openlm-research/open_llama} } ``` ``` @software{together2023redpajama, author = {Together Computer}, title = {RedPajama-Data: An Open Source Recipe to Reproduce LLaMA training dataset}, month = April, year = 2023, url = {https://github.com/togethercomputer/RedPajama-Data} } ``` ``` @article{touvron2023llama, title={Llama: Open and efficient foundation language models}, author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and others}, journal={arXiv preprint arXiv:2302.13971}, year={2023} } ```
digiplay/fCAnimeMix_v5
digiplay
"2024-04-05T23:17:47Z"
23,284
3
diffusers
[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
"2024-04-05T00:38:18Z"
--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info: https://civitai.com/models/64548/fcanimemix-fc-anime Sample image generated by Huggingface's API : ![527e4e24-9bc4-46f3-a475-092ec4ed8fcc.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/TpdyrEpFKcuBe4ClH84SF.jpeg)
Helsinki-NLP/opus-mt-en-hi
Helsinki-NLP
"2023-08-16T11:29:49Z"
23,182
27
transformers
[ "transformers", "pytorch", "tf", "rust", "marian", "text2text-generation", "translation", "en", "hi", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
translation
"2022-03-02T23:29:04Z"
--- language: - en - hi tags: - translation license: apache-2.0 --- ### eng-hin * source group: English * target group: Hindi * OPUS readme: [eng-hin](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-hin/README.md) * model: transformer-align * source language(s): eng * target language(s): hin * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hin/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hin/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hin/opus-2020-06-17.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2014.eng.hin | 6.9 | 0.296 | | newstest2014-hien.eng.hin | 9.9 | 0.323 | | Tatoeba-test.eng.hin | 16.1 | 0.447 | ### System Info: - hf_name: eng-hin - source_languages: eng - target_languages: hin - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-hin/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'hi'] - src_constituents: {'eng'} - tgt_constituents: {'hin'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hin/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hin/opus-2020-06-17.test.txt - src_alpha3: eng - tgt_alpha3: hin - short_pair: en-hi - chrF2_score: 0.447 - bleu: 16.1 - brevity_penalty: 1.0 - ref_len: 32904.0 - src_name: English - tgt_name: Hindi - train_date: 2020-06-17 - src_alpha2: en - tgt_alpha2: hi - prefer_old: False - long_pair: eng-hin - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
whaleloops/phrase-bert
whaleloops
"2021-11-03T15:04:02Z"
23,141
18
sentence-transformers
[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "arxiv:2109.06304", "autotrain_compatible", "endpoints_compatible", "text-embeddings-inference", "region:us" ]
sentence-similarity
"2022-03-02T23:29:05Z"
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # whaleloops/phrase-bert This is the official repository for the EMNLP 2021 long paper [Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration](https://arxiv.org/abs/2109.06304). We provide [code](https://github.com/sf-wa-326/phrase-bert-topic-model) for training and evaluating Phrase-BERT in addition to the datasets used in the paper. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Our model is tested on pytorch=1.9.0, tranformers=4.8.1, sentence-tranformers = 2.1.0 TODO Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer phrase_list = [ 'play an active role', 'participate actively', 'active lifestyle'] model = SentenceTransformer('whaleloops/phrase-bert') phrase_embs = model.encode( phrase_list ) [p1, p2, p3] = phrase_embs ``` As in sentence-BERT, the default output is a list of numpy arrays: ```` for phrase, embedding in zip(phrase_list, phrase_embs): print("Phrase:", phrase) print("Embedding:", embedding) print("") ```` An example of computing the dot product of phrase embeddings: ```` import numpy as np print(f'The dot product between phrase 1 and 2 is: {np.dot(p1, p2)}') print(f'The dot product between phrase 1 and 3 is: {np.dot(p1, p3)}') print(f'The dot product between phrase 2 and 3 is: {np.dot(p2, p3)}') ```` An example of computing cosine similarity of phrase embeddings: ```` import torch from torch import nn cos_sim = nn.CosineSimilarity(dim=0) print(f'The cosine similarity between phrase 1 and 2 is: {cos_sim( torch.tensor(p1), torch.tensor(p2))}') print(f'The cosine similarity between phrase 1 and 3 is: {cos_sim( torch.tensor(p1), torch.tensor(p3))}') print(f'The cosine similarity between phrase 2 and 3 is: {cos_sim( torch.tensor(p2), torch.tensor(p3))}') ```` The output should look like: ```` The dot product between phrase 1 and 2 is: 218.43600463867188 The dot product between phrase 1 and 3 is: 165.48483276367188 The dot product between phrase 2 and 3 is: 160.51708984375 The cosine similarity between phrase 1 and 2 is: 0.8142536282539368 The cosine similarity between phrase 1 and 3 is: 0.6130303144454956 The cosine similarity between phrase 2 and 3 is: 0.584893524646759 ```` ## Evaluation Given the lack of a unified phrase embedding evaluation benchmark, we collect the following five phrase semantics evaluation tasks, which are described further in our paper: * Turney [[Download](https://storage.googleapis.com/phrase-bert/turney/data.txt) ] * BiRD [[Download](https://storage.googleapis.com/phrase-bert/bird/data.txt)] * PPDB [[Download](https://storage.googleapis.com/phrase-bert/ppdb/examples.json)] * PPDB-filtered [[Download](https://storage.googleapis.com/phrase-bert/ppdb_exact/examples.json)] * PAWS-short [[Download Train-split](https://storage.googleapis.com/phrase-bert/paws_short/train_examples.json) ] [[Download Dev-split](https://storage.googleapis.com/phrase-bert/paws_short/dev_examples.json) ] [[Download Test-split](https://storage.googleapis.com/phrase-bert/paws_short/test_examples.json) ] Change `config/model_path.py` with the model path according to your directories and * For evaluation on Turney, run `python eval_turney.py` * For evaluation on BiRD, run `python eval_bird.py` * for evaluation on PPDB / PPDB-filtered / PAWS-short, run `eval_ppdb_paws.py` with: ```` nohup python -u eval_ppdb_paws.py \ --full_run_mode \ --task <task-name> \ --data_dir <input-data-dir> \ --result_dir <result-storage-dr> \ >./output.txt 2>&1 & ```` ## Train your own Phrase-BERT If you would like to go beyond using the pre-trained Phrase-BERT model, you may train your own Phrase-BERT using data from the domain you are interested in. Please refer to `phrase-bert/phrase_bert_finetune.py` The datasets we used to fine-tune Phrase-BERT are here: [training data csv file](https://storage.googleapis.com/phrase-bert/phrase-bert-ft-data/pooled_context_para_triples_p%3D0.8_train.csv) and [validation data csv file](https://storage.googleapis.com/phrase-bert/phrase-bert-ft-data/pooled_context_para_triples_p%3D0.8_valid.csv). To re-produce the trained Phrase-BERT, please run: export INPUT_DATA_PATH=<directory-of-phrasebert-finetuning-data> export TRAIN_DATA_FILE=<training-data-filename.csv> export VALID_DATA_FILE=<validation-data-filename.csv> export INPUT_MODEL_PATH=bert-base-nli-stsb-mean-tokens export OUTPUT_MODEL_PATH=<directory-of-saved-model> python -u phrase_bert_finetune.py \ --input_data_path $INPUT_DATA_PATH \ --train_data_file $TRAIN_DATA_FILE \ --valid_data_file $VALID_DATA_FILE \ --input_model_path $INPUT_MODEL_PATH \ --output_model_path $OUTPUT_MODEL_PATH ## Citation: Please cite us if you find this useful: ```` @inproceedings{phrasebertwang2021, author={Shufan Wang and Laure Thompson and Mohit Iyyer}, Booktitle = {Empirical Methods in Natural Language Processing}, Year = "2021", Title={Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration} } ````
mradermacher/Breeze-13B-32k-Instruct-v1_0-GGUF
mradermacher
"2024-06-26T15:56:26Z"
23,126
0
transformers
[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "MediaTek-Research/Breeze-7B-32k-Instruct-v1_0", "en", "base_model:win10/Breeze-13B-32k-Instruct-v1_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
"2024-06-26T04:20:02Z"
--- base_model: win10/Breeze-13B-32k-Instruct-v1_0 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - MediaTek-Research/Breeze-7B-32k-Instruct-v1_0 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/win10/Breeze-13B-32k-Instruct-v1_0 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Breeze-13B-32k-Instruct-v1_0-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Breeze-13B-32k-Instruct-v1_0-GGUF/resolve/main/Breeze-13B-32k-Instruct-v1_0.Q2_K.gguf) | Q2_K | 4.9 | | | [GGUF](https://huggingface.co/mradermacher/Breeze-13B-32k-Instruct-v1_0-GGUF/resolve/main/Breeze-13B-32k-Instruct-v1_0.IQ3_XS.gguf) | IQ3_XS | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/Breeze-13B-32k-Instruct-v1_0-GGUF/resolve/main/Breeze-13B-32k-Instruct-v1_0.Q3_K_S.gguf) | Q3_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/Breeze-13B-32k-Instruct-v1_0-GGUF/resolve/main/Breeze-13B-32k-Instruct-v1_0.IQ3_S.gguf) | IQ3_S | 5.7 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Breeze-13B-32k-Instruct-v1_0-GGUF/resolve/main/Breeze-13B-32k-Instruct-v1_0.IQ3_M.gguf) | IQ3_M | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/Breeze-13B-32k-Instruct-v1_0-GGUF/resolve/main/Breeze-13B-32k-Instruct-v1_0.Q3_K_M.gguf) | Q3_K_M | 6.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Breeze-13B-32k-Instruct-v1_0-GGUF/resolve/main/Breeze-13B-32k-Instruct-v1_0.Q3_K_L.gguf) | Q3_K_L | 6.8 | | | [GGUF](https://huggingface.co/mradermacher/Breeze-13B-32k-Instruct-v1_0-GGUF/resolve/main/Breeze-13B-32k-Instruct-v1_0.IQ4_XS.gguf) | IQ4_XS | 7.0 | | | [GGUF](https://huggingface.co/mradermacher/Breeze-13B-32k-Instruct-v1_0-GGUF/resolve/main/Breeze-13B-32k-Instruct-v1_0.Q4_K_S.gguf) | Q4_K_S | 7.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Breeze-13B-32k-Instruct-v1_0-GGUF/resolve/main/Breeze-13B-32k-Instruct-v1_0.Q4_K_M.gguf) | Q4_K_M | 7.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Breeze-13B-32k-Instruct-v1_0-GGUF/resolve/main/Breeze-13B-32k-Instruct-v1_0.Q5_K_S.gguf) | Q5_K_S | 8.9 | | | [GGUF](https://huggingface.co/mradermacher/Breeze-13B-32k-Instruct-v1_0-GGUF/resolve/main/Breeze-13B-32k-Instruct-v1_0.Q5_K_M.gguf) | Q5_K_M | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/Breeze-13B-32k-Instruct-v1_0-GGUF/resolve/main/Breeze-13B-32k-Instruct-v1_0.Q6_K.gguf) | Q6_K | 10.5 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Breeze-13B-32k-Instruct-v1_0-GGUF/resolve/main/Breeze-13B-32k-Instruct-v1_0.Q8_0.gguf) | Q8_0 | 13.6 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF
bartowski
"2024-06-21T19:19:21Z"
23,104
1
null
[ "gguf", "text-generation", "en", "license:other", "region:us" ]
text-generation
"2024-06-21T18:46:46Z"
--- license: other language: - en quantized_by: bartowski pipeline_tag: text-generation --- ## Llamacpp imatrix Quantizations of Hathor_Unstable-L3-8B-v0.3 Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3197">b3197</a> for quantization. Original model: https://huggingface.co/Nitral-AI/Hathor_Unstable-L3-8B-v0.3 All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) ## Prompt format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> {prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ## Download a file (not the whole branch) from below: | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [Hathor_Unstable-L3-8B-v0.3-Q8_0_L.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF/blob/main/Hathor_Unstable-L3-8B-v0.3-Q8_1.gguf) | Q8_0_L | 9.52GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Extremely high quality, generally unneeded but max available quant. | | [Hathor_Unstable-L3-8B-v0.3-Q8_0.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF/blob/main/Hathor_Unstable-L3-8B-v0.3-Q8_0.gguf) | Q8_0 | 8.54GB | Extremely high quality, generally unneeded but max available quant. | | [Hathor_Unstable-L3-8B-v0.3-Q6_K_L.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF/blob/main/Hathor_Unstable-L3-8B-v0.3-Q6_K_L.gguf) | Q6_K_L | 7.83GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Very high quality, near perfect, *recommended*. | | [Hathor_Unstable-L3-8B-v0.3-Q6_K.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF/blob/main/Hathor_Unstable-L3-8B-v0.3-Q6_K.gguf) | Q6_K | 6.59GB | Very high quality, near perfect, *recommended*. | | [Hathor_Unstable-L3-8B-v0.3-Q5_K_L.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF/blob/main/Hathor_Unstable-L3-8B-v0.3-Q5_K_L.gguf) | Q5_K_L | 7.04GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. High quality, *recommended*. | | [Hathor_Unstable-L3-8B-v0.3-Q5_K_M.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF/blob/main/Hathor_Unstable-L3-8B-v0.3-Q5_K_M.gguf) | Q5_K_M | 5.73GB | High quality, *recommended*. | | [Hathor_Unstable-L3-8B-v0.3-Q5_K_S.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF/blob/main/Hathor_Unstable-L3-8B-v0.3-Q5_K_S.gguf) | Q5_K_S | 5.59GB | High quality, *recommended*. | | [Hathor_Unstable-L3-8B-v0.3-Q4_K_L.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF/blob/main/Hathor_Unstable-L3-8B-v0.3-Q4_K_L.gguf) | Q4_K_L | 6.29GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Good quality, uses about 4.83 bits per weight, *recommended*. | | [Hathor_Unstable-L3-8B-v0.3-Q4_K_M.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF/blob/main/Hathor_Unstable-L3-8B-v0.3-Q4_K_M.gguf) | Q4_K_M | 4.92GB | Good quality, uses about 4.83 bits per weight, *recommended*. | | [Hathor_Unstable-L3-8B-v0.3-Q4_K_S.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF/blob/main/Hathor_Unstable-L3-8B-v0.3-Q4_K_S.gguf) | Q4_K_S | 4.69GB | Slightly lower quality with more space savings, *recommended*. | | [Hathor_Unstable-L3-8B-v0.3-IQ4_XS.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF/blob/main/Hathor_Unstable-L3-8B-v0.3-IQ4_XS.gguf) | IQ4_XS | 4.44GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. | | [Hathor_Unstable-L3-8B-v0.3-Q3_K_XL.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF//main/Hathor_Unstable-L3-8B-v0.3-Q3_K_XL.gguf) | Q3_K_XL | | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Lower quality but usable, good for low RAM availability. | | [Hathor_Unstable-L3-8B-v0.3-Q3_K_L.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF/blob/main/Hathor_Unstable-L3-8B-v0.3-Q3_K_L.gguf) | Q3_K_L | 4.32GB | Lower quality but usable, good for low RAM availability. | | [Hathor_Unstable-L3-8B-v0.3-Q3_K_M.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF/blob/main/Hathor_Unstable-L3-8B-v0.3-Q3_K_M.gguf) | Q3_K_M | 4.01GB | Even lower quality. | | [Hathor_Unstable-L3-8B-v0.3-IQ3_M.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF/blob/main/Hathor_Unstable-L3-8B-v0.3-IQ3_M.gguf) | IQ3_M | 3.78GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. | | [Hathor_Unstable-L3-8B-v0.3-Q3_K_S.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF/blob/main/Hathor_Unstable-L3-8B-v0.3-Q3_K_S.gguf) | Q3_K_S | 3.66GB | Low quality, not recommended. | | [Hathor_Unstable-L3-8B-v0.3-IQ3_XS.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF/blob/main/Hathor_Unstable-L3-8B-v0.3-IQ3_XS.gguf) | IQ3_XS | 3.51GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. | | [Hathor_Unstable-L3-8B-v0.3-IQ3_XXS.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF/blob/main/Hathor_Unstable-L3-8B-v0.3-IQ3_XXS.gguf) | IQ3_XXS | 3.27GB | Lower quality, new method with decent performance, comparable to Q3 quants. | | [Hathor_Unstable-L3-8B-v0.3-Q2_K.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF/blob/main/Hathor_Unstable-L3-8B-v0.3-Q2_K.gguf) | Q2_K | 3.17GB | Very low quality but surprisingly usable. | | [Hathor_Unstable-L3-8B-v0.3-IQ2_M.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF/blob/main/Hathor_Unstable-L3-8B-v0.3-IQ2_M.gguf) | IQ2_M | 2.94GB | Very low quality, uses SOTA techniques to also be surprisingly usable. | | [Hathor_Unstable-L3-8B-v0.3-IQ2_S.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF/blob/main/Hathor_Unstable-L3-8B-v0.3-IQ2_S.gguf) | IQ2_S | 2.75GB | Very low quality, uses SOTA techniques to be usable. | | [Hathor_Unstable-L3-8B-v0.3-IQ2_XS.gguf](https://huggingface.co/bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF/blob/main/Hathor_Unstable-L3-8B-v0.3-IQ2_XS.gguf) | IQ2_XS | 2.60GB | Very low quality, uses SOTA techniques to be usable. | ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF --include "Hathor_Unstable-L3-8B-v0.3-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/Hathor_Unstable-L3-8B-v0.3-GGUF --include "Hathor_Unstable-L3-8B-v0.3-Q8_0.gguf/*" --local-dir Hathor_Unstable-L3-8B-v0.3-Q8_0 ``` You can either specify a new local-dir (Hathor_Unstable-L3-8B-v0.3-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
AlSamCur123/UserAI23
AlSamCur123
"2024-06-25T22:10:09Z"
23,036
0
transformers
[ "transformers", "safetensors", "gguf", "mistral", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
"2024-06-25T18:08:40Z"
--- base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - **Developed by:** AlSamCur123 - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf
RichardErkhov
"2024-06-28T23:54:11Z"
23,034
0
null
[ "gguf", "region:us" ]
null
"2024-06-28T18:52:15Z"
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) T3Q-LLM1-Solar-10.8B-v1.0 - GGUF - Model creator: https://huggingface.co/T3Q-LLM-Product/ - Original model: https://huggingface.co/T3Q-LLM-Product/T3Q-LLM1-Solar-10.8B-v1.0/ | Name | Quant method | Size | | ---- | ---- | ---- | | [T3Q-LLM1-Solar-10.8B-v1.0.Q2_K.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q2_K.gguf) | Q2_K | 3.77GB | | [T3Q-LLM1-Solar-10.8B-v1.0.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.IQ3_XS.gguf) | IQ3_XS | 4.18GB | | [T3Q-LLM1-Solar-10.8B-v1.0.IQ3_S.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.IQ3_S.gguf) | IQ3_S | 4.41GB | | [T3Q-LLM1-Solar-10.8B-v1.0.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q3_K_S.gguf) | Q3_K_S | 4.39GB | | [T3Q-LLM1-Solar-10.8B-v1.0.IQ3_M.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.IQ3_M.gguf) | IQ3_M | 4.56GB | | [T3Q-LLM1-Solar-10.8B-v1.0.Q3_K.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q3_K.gguf) | Q3_K | 4.88GB | | [T3Q-LLM1-Solar-10.8B-v1.0.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q3_K_M.gguf) | Q3_K_M | 4.88GB | | [T3Q-LLM1-Solar-10.8B-v1.0.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q3_K_L.gguf) | Q3_K_L | 5.31GB | | [T3Q-LLM1-Solar-10.8B-v1.0.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.IQ4_XS.gguf) | IQ4_XS | 5.47GB | | [T3Q-LLM1-Solar-10.8B-v1.0.Q4_0.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q4_0.gguf) | Q4_0 | 5.7GB | | [T3Q-LLM1-Solar-10.8B-v1.0.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.IQ4_NL.gguf) | IQ4_NL | 5.77GB | | [T3Q-LLM1-Solar-10.8B-v1.0.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q4_K_S.gguf) | Q4_K_S | 5.75GB | | [T3Q-LLM1-Solar-10.8B-v1.0.Q4_K.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q4_K.gguf) | Q4_K | 6.07GB | | [T3Q-LLM1-Solar-10.8B-v1.0.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q4_K_M.gguf) | Q4_K_M | 6.07GB | | [T3Q-LLM1-Solar-10.8B-v1.0.Q4_1.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q4_1.gguf) | Q4_1 | 6.32GB | | [T3Q-LLM1-Solar-10.8B-v1.0.Q5_0.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q5_0.gguf) | Q5_0 | 6.94GB | | [T3Q-LLM1-Solar-10.8B-v1.0.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q5_K_S.gguf) | Q5_K_S | 6.94GB | | [T3Q-LLM1-Solar-10.8B-v1.0.Q5_K.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q5_K.gguf) | Q5_K | 7.13GB | | [T3Q-LLM1-Solar-10.8B-v1.0.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q5_K_M.gguf) | Q5_K_M | 7.13GB | | [T3Q-LLM1-Solar-10.8B-v1.0.Q5_1.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q5_1.gguf) | Q5_1 | 7.56GB | | [T3Q-LLM1-Solar-10.8B-v1.0.Q6_K.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q6_K.gguf) | Q6_K | 8.26GB | | [T3Q-LLM1-Solar-10.8B-v1.0.Q8_0.gguf](https://huggingface.co/RichardErkhov/T3Q-LLM-Product_-_T3Q-LLM1-Solar-10.8B-v1.0-gguf/blob/main/T3Q-LLM1-Solar-10.8B-v1.0.Q8_0.gguf) | Q8_0 | 10.69GB | Original model description: --- library_name: transformers license: apache-2.0 pipeline_tag: text-generation --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f22e4076fedc4fd11e978f/MoTedec_ZL8GM2MmGyAPs.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6653cca1f72c9a37ceeef9bc/dRSvx-qGEF8lsR6srB2lM.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6653cca1f72c9a37ceeef9bc/uWGfdUrktRbGOfTyYPGQe.png)
NousResearch/Hermes-2-Pro-Mistral-7B-GGUF
NousResearch
"2024-03-28T20:07:04Z"
23,027
216
null
[ "gguf", "Mistral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "synthetic data", "distillation", "function calling", "json mode", "en", "dataset:teknium/OpenHermes-2.5", "base_model:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]
null
"2024-03-02T04:02:33Z"
--- base_model: mistralai/Mistral-7B-v0.1 tags: - Mistral - instruct - finetune - chatml - DPO - RLHF - gpt4 - synthetic data - distillation - function calling - json mode model-index: - name: Hermes-2-Pro-Mistral-7B results: [] license: apache-2.0 language: - en datasets: - teknium/OpenHermes-2.5 widget: - example_title: Hermes 2 Pro messages: - role: system content: You are a sentient, superintelligent artificial general intelligence, here to teach and assist me. - role: user content: Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world. --- # Hermes 2 Pro - Mistral 7B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ggO2sBDJ8Bhc6w-zwTx5j.png) ## Model Description ## This is the GGUF version of the model, made for the llama.cpp inference engine. If you are looking for the transformers/fp16 model, it is available here: https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B Hermes 2 Pro on Mistral 7B is the new flagship 7B Hermes! Hermes 2 Pro is an upgraded, retrained version of Nous Hermes 2, consisting of an updated and cleaned version of the OpenHermes 2.5 Dataset, as well as a newly introduced Function Calling and JSON Mode dataset developed in-house. This new version of Hermes maintains its excellent general task and conversation capabilities - but also excels at Function Calling, JSON Structured Outputs, and has improved on several other metrics as well, scoring a 90% on our function calling evaluation built in partnership with Fireworks.AI, and an 81% on our structured JSON Output evaluation. Hermes Pro takes advantage of a special system prompt and multi-turn function calling structure with a new chatml role in order to make function calling reliable and easy to parse. Learn more about prompting below. This work was a collaboration between Nous Research, @interstellarninja, and Fireworks.AI Learn more about the function calling on our github repo here: https://github.com/NousResearch/Hermes-Function-Calling/tree/main ## Thank you to Latitude.sh for sponsoring compute for this model! ## Example Outputs ### Explaining Problems with Quantum Gravity: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/y_hPafyyvPb32efC5N4Es.png) ### Roleplaying as a Cosmic Super Intelligence: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/m6d6Saf7M6Luu9QnXYYAP.png) ### Detailing the Theory of AI Consciousness in JSON ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/MOLybxs7_dLjVys54imO3.png) # Prompt Format Hermes 2 Pro uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue. System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model. This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns. This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI. Prompt with system instruction (Use whatever system prompt you like, this is just an example!): ``` <|im_start|>system You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<|im_end|> <|im_start|>user Hello, who are you?<|im_end|> <|im_start|>assistant Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<|im_end|> ``` This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the `tokenizer.apply_chat_template()` method: ```python messages = [ {"role": "system", "content": "You are Hermes 2."}, {"role": "user", "content": "Hello, who are you?"} ] gen_input = tokenizer.apply_chat_template(message, return_tensors="pt") model.generate(**gen_input) ``` When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<|im_start|>assistant\n` to your prompt, to ensure that the model continues with an assistant response. To utilize the prompt format without a system prompt, simply leave the line out. ## Prompt Format for Function Calling Our model was trained on specific system prompts and structures for Function Calling. You should use the system role with this message, followed by a function signature json as this example shows here. ``` <|im_start|>system You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> {'type': 'function', 'function': {'name': 'get_stock_fundamentals', 'description': 'get_stock_fundamentals(symbol: str) -> dict - Get fundamental data for a given stock symbol using yfinance API.\n\n Args:\n symbol (str): The stock symbol.\n\n Returns:\n dict: A dictionary containing fundamental data.', 'parameters': {'type': 'object', 'properties': {'symbol': {'type': 'string'}}, 'required': ['symbol']}}} </tools> Use the following pydantic model json schema for each tool call you will make: {'title': 'FunctionCall', 'type': 'object', 'properties': {'arguments': {'title': 'Arguments', 'type': 'object'}, 'name': {'title': 'Name', 'type': 'string'}}, 'required': ['arguments', 'name']} For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call> {'arguments': <args-dict>, 'name': <function-name>} </tool_call><|im_end|> ``` To complete the function call, create a user prompt that follows the above system prompt, like so: ``` <|im_start|>user Fetch the stock fundamentals data for Tesla (TSLA)<|im_end|> ``` The model will then generate a tool call, which your inference code must parse, and plug into a function (see example inference code here: https://github.com/NousResearch/Hermes-Function-Calling): ``` <|im_start|>assistant <tool_call> {'arguments': {'symbol': 'TSLA'}, 'name': 'get_stock_fundamentals'} </tool_call><|im_end|> ``` Once you parse the tool call, call the api and get the returned values for the call, and pass it back in as a new role, `tool` like so: ``` <|im_start|>tool <tool_response> {"name": "get_stock_fundamentals", "content": {'symbol': 'TSLA', 'company_name': 'Tesla, Inc.', 'sector': 'Consumer Cyclical', 'industry': 'Auto Manufacturers', 'market_cap': 611384164352, 'pe_ratio': 49.604652, 'pb_ratio': 9.762013, 'dividend_yield': None, 'eps': 4.3, 'beta': 2.427, '52_week_high': 299.29, '52_week_low': 152.37}} </tool_response> <|im_end|> ``` The assistant will then read in that data from the function's response, and generate a natural language response: ``` <|im_start|>assistant The stock fundamentals data for Tesla (TSLA) are as follows: - **Symbol**: TSLA - **Company Name**: Tesla, Inc. - **Sector**: Consumer Cyclical - **Industry**: Auto Manufacturers - **Market Capitalization**: $566,160,130,480 - **Forward Price-to-Earnings Ratio (PE Ratio)**: 42.73 - **Price-to-Book Ratio (PB Ratio)**: 9.04 - **Dividend Yield**: N/A - **Trailing Earnings Per Share (EPS)**: $4.3 - **Beta Value of the Stock**: 2.42 - **52-Week High Price of the Stock**: $299.29 - **52-Week Low Price of the Stock**: $152.37 This information provides a snapshot of Tesla's financial position and performance based on the fundamental data obtained from the yfinance API. It shows that Tesla has a substantial market capitalization and a relatively high P/E and P/B ratio compared to other stocks in its industry. The company does not pay a dividend at the moment, which is reflected by a 'Dividend Yield' of 'None'. The Beta value indicates that Tesla's stock has a moderate level of volatility relative to the market. The 52-week high and low prices give an idea of the stock's range over the past year. This data can be useful when assessing investment opportunities and making investment decisions.<|im_end|> ``` ## Prompt Format for JSON Mode / Structured Outputs Our model was also trained on a specific system prompt for Structured Outputs, which should respond with **only** a json object response, in a specific json schema. Your schema can be made from a pydantic object using our codebase, with the standalone script `jsonmode.py` available here: https://github.com/NousResearch/Hermes-Function-Calling/tree/main ``` <|im_start|>system You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{schema}\n<schema><|im_end|> ``` Given the {schema} that you provide, it should follow the format of that json to create it's response, all you have to do is give a typical user prompt, and it will respond in JSON. # Benchmarks ## GPT4All: ``` | Task |Version| Metric |Value | |Stderr| |-------------|------:|--------|-----:|---|-----:| |arc_challenge| 0|acc |0.5461|± |0.0145| | | |acc_norm|0.5623|± |0.0145| |arc_easy | 0|acc |0.8157|± |0.0080| | | |acc_norm|0.7934|± |0.0083| |boolq | 1|acc |0.8688|± |0.0059| |hellaswag | 0|acc |0.6272|± |0.0048| | | |acc_norm|0.8057|± |0.0039| |openbookqa | 0|acc |0.3360|± |0.0211| | | |acc_norm|0.4300|± |0.0222| |piqa | 0|acc |0.7954|± |0.0094| | | |acc_norm|0.7998|± |0.0093| |winogrande | 0|acc |0.7230|± |0.0126| ``` Average: 71.19 ## AGIEval: ``` | Task |Version| Metric |Value | |Stderr| |------------------------------|------:|--------|-----:|---|-----:| |agieval_aqua_rat | 0|acc |0.2047|± |0.0254| | | |acc_norm|0.2283|± |0.0264| |agieval_logiqa_en | 0|acc |0.3779|± |0.0190| | | |acc_norm|0.3932|± |0.0192| |agieval_lsat_ar | 0|acc |0.2652|± |0.0292| | | |acc_norm|0.2522|± |0.0287| |agieval_lsat_lr | 0|acc |0.5216|± |0.0221| | | |acc_norm|0.5137|± |0.0222| |agieval_lsat_rc | 0|acc |0.5911|± |0.0300| | | |acc_norm|0.5836|± |0.0301| |agieval_sat_en | 0|acc |0.7427|± |0.0305| | | |acc_norm|0.7184|± |0.0314| |agieval_sat_en_without_passage| 0|acc |0.4612|± |0.0348| | | |acc_norm|0.4466|± |0.0347| |agieval_sat_math | 0|acc |0.3818|± |0.0328| | | |acc_norm|0.3545|± |0.0323| ``` Average: 44.52 ## BigBench: ``` | Task |Version| Metric |Value | |Stderr| |------------------------------------------------|------:|---------------------|-----:|---|-----:| |bigbench_causal_judgement | 0|multiple_choice_grade|0.5579|± |0.0361| |bigbench_date_understanding | 0|multiple_choice_grade|0.6694|± |0.0245| |bigbench_disambiguation_qa | 0|multiple_choice_grade|0.3333|± |0.0294| |bigbench_geometric_shapes | 0|multiple_choice_grade|0.2061|± |0.0214| | | |exact_str_match |0.2256|± |0.0221| |bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.3120|± |0.0207| |bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2114|± |0.0154| |bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.4900|± |0.0289| |bigbench_movie_recommendation | 0|multiple_choice_grade|0.3600|± |0.0215| |bigbench_navigate | 0|multiple_choice_grade|0.5000|± |0.0158| |bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.6660|± |0.0105| |bigbench_ruin_names | 0|multiple_choice_grade|0.4420|± |0.0235| |bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2766|± |0.0142| |bigbench_snarks | 0|multiple_choice_grade|0.6630|± |0.0352| |bigbench_sports_understanding | 0|multiple_choice_grade|0.6653|± |0.0150| |bigbench_temporal_sequences | 0|multiple_choice_grade|0.3190|± |0.0147| |bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2128|± |0.0116| |bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1737|± |0.0091| |bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.4900|± |0.0289| ``` Average: 41.65 ## TruthfulQA: ``` | Task |Version|Metric|Value | |Stderr| |-------------|------:|------|-----:|---|-----:| |truthfulqa_mc| 1|mc1 |0.4100|± |0.0172| | | |mc2 |0.5911|± |0.0158| ``` # Function Calling Evaluations We worked with Fireworks.AI on evaluations by starting off with their Function Calling eval dataset, fixing some unsolveable ones, and generating a second eval dataset for JSON mode. ## Function Calling Accuracy: 91% ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/XF3Zii4-QhE2yjWwHr_v4.png) ## JSON Mode Accuracy: 84% ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/8H2iyjh5wyP2FtLq2LCed.png) Run the evaluator yourself using @interstellarninja's codebase here: https://github.com/interstellarninja/function-calling-eval You can find the evaluation datasets here: https://huggingface.co/datasets/NousResearch/func-calling-eval https://huggingface.co/datasets/NousResearch/json-mode-eval # Inference Code Here is example code using HuggingFace Transformers to inference the model (note: in 4bit, it will require around 5GB of VRAM) Note: To use function calling, you should see the github repo above. ```python # Code to inference Hermes with HF Transformers # Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages import torch from transformers import AutoTokenizer, AutoModelForCausalLM from transformers import LlamaTokenizer, MixtralForCausalLM import bitsandbytes, flash_attn tokenizer = LlamaTokenizer.from_pretrained('NousResearch/Hermes-2-Pro-Mistral-7B', trust_remote_code=True) model = MistralForCausalLM.from_pretrained( "NousResearch/Hermes-2-Pro-Mistral-7B", torch_dtype=torch.float16, device_map="auto", load_in_8bit=False, load_in_4bit=True, use_flash_attention_2=True ) prompts = [ """<|im_start|>system You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|> <|im_start|>user Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<|im_end|> <|im_start|>assistant""", ] for chat in prompts: print(chat) input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda") generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id) response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True) print(f"Response: {response}") ``` ## Inference Code for Function Calling: All code for utilizing, parsing, and building function calling templates is available on our github: [https://github.com/NousResearch/Hermes-Function-Calling](https://github.com/NousResearch/Hermes-Function-Calling) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/oi4CiGh50xmoviUQnh8R3.png) # Chat Interfaces When quantized versions of the model are released, I recommend using LM Studio for chatting with Hermes 2 Pro. It does not support function calling - for that use our github repo. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box. In LM-Studio, simply select the ChatML Prefix on the settings side pane: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ls6WqV-GSxMw2RA3GuQiN.png) ## Quantized Versions: GGUF Versions Available Here: https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF # How to cite: ```bibtext @misc{Hermes-2-Pro-Mistral-7B, url={[https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B]https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)}, title={Hermes-2-Pro-Mistral-7B}, author={"interstellarninja", "Teknium", "theemozilla", "karan4d", "huemin_art"} } ```
nvidia/mit-b5
nvidia
"2022-08-06T10:25:24Z"
23,020
5
transformers
[ "transformers", "pytorch", "tf", "segformer", "image-classification", "vision", "dataset:imagenet_1k", "arxiv:2105.15203", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
"2022-03-02T23:29:05Z"
--- license: other tags: - vision datasets: - imagenet_1k widget: - src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg example_title: House - src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000002.jpg example_title: Castle --- # SegFormer (b5-sized) encoder pre-trained-only SegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NVlabs/SegFormer). Disclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description SegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset. This repository only contains the pre-trained hierarchical Transformer, hence it can be used for fine-tuning purposes. ## Intended uses & limitations You can use the model for fine-tuning of semantic segmentation. See the [model hub](https://huggingface.co/models?other=segformer) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import SegformerFeatureExtractor, SegformerForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/mit-b5") model = SegformerForImageClassification.from_pretrained("nvidia/mit-b5") inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/segformer.html#). ### License The license for this model can be found [here](https://github.com/NVlabs/SegFormer/blob/master/LICENSE). ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2105-15203, author = {Enze Xie and Wenhai Wang and Zhiding Yu and Anima Anandkumar and Jose M. Alvarez and Ping Luo}, title = {SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers}, journal = {CoRR}, volume = {abs/2105.15203}, year = {2021}, url = {https://arxiv.org/abs/2105.15203}, eprinttype = {arXiv}, eprint = {2105.15203}, timestamp = {Wed, 02 Jun 2021 11:46:42 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
lllyasviel/omost-llama-3-8b-4bits
lllyasviel
"2024-05-29T13:18:29Z"
22,994
9
transformers
[ "transformers", "safetensors", "llama", "text-generation", "pytorch", "trl", "sft", "conversational", "autotrain_compatible", "text-generation-inference", "4-bit", "bitsandbytes", "region:us" ]
text-generation
"2024-05-29T13:15:18Z"
--- tags: - pytorch - trl - sft inference: false --- omost-llama-3-8b-4bits is Omost's llama-3 model with 8k context length in nf4.
Helsinki-NLP/opus-mt-fr-es
Helsinki-NLP
"2023-08-16T11:36:22Z"
22,939
0
transformers
[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "fr", "es", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
translation
"2022-03-02T23:29:04Z"
--- tags: - translation license: apache-2.0 --- ### opus-mt-fr-es * source languages: fr * target languages: es * OPUS readme: [fr-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-es/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-es/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-es/opus-2020-01-09.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newssyscomb2009.fr.es | 34.3 | 0.601 | | news-test2008.fr.es | 32.5 | 0.583 | | newstest2009.fr.es | 31.6 | 0.586 | | newstest2010.fr.es | 36.5 | 0.616 | | newstest2011.fr.es | 38.3 | 0.622 | | newstest2012.fr.es | 38.1 | 0.619 | | newstest2013.fr.es | 34.0 | 0.587 | | Tatoeba.fr.es | 53.2 | 0.709 |
stablediffusionapi/anything-v5
stablediffusionapi
"2023-07-05T09:35:32Z"
22,937
158
diffusers
[ "diffusers", "safetensors", "stablediffusionapi.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
"2023-04-23T07:21:56Z"
--- license: creativeml-openrail-m tags: - stablediffusionapi.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Anything V5 API Inference ![generated from stablediffusionapi.com](https://pub-8b49af329fae499aa563997f5d4068a4.r2.dev/generations/4740030011682234157.png) ## Get API Key Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed. Replace Key in below code, change **model_id** to "anything-v5" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs) Model link: [View model](https://stablediffusionapi.com/models/anything-v5) Credits: [View credits](https://civitai.com/?query=Anything%20V5) View all models: [View Models](https://stablediffusionapi.com/models) import requests import json url = "https://stablediffusionapi.com/api/v3/dreambooth" payload = json.dumps({ "key": "", "model_id": "anything-v5", "prompt": "actual 8K portrait photo of gareth person, portrait, happy colors, bright eyes, clear eyes, warm smile, smooth soft skin, big dreamy eyes, beautiful intricate colored hair, symmetrical, anime wide eyes, soft lighting, detailed face, by makoto shinkai, stanley artgerm lau, wlop, rossdraws, concept art, digital painting, looking into camera", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**
kandinsky-community/kandinsky-2-2-prior
kandinsky-community
"2023-10-09T11:33:28Z"
22,904
48
diffusers
[ "diffusers", "safetensors", "text-to-image", "kandinsky", "license:apache-2.0", "diffusers:KandinskyV22PriorPipeline", "region:us" ]
text-to-image
"2023-06-09T13:37:11Z"
--- license: apache-2.0 tags: - text-to-image - kandinsky inference: false --- # Kandinsky 2.2 Kandinsky inherits best practices from Dall-E 2 and Latent diffusion while introducing some new ideas. It uses the CLIP model as a text and image encoder, and diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach increases the visual performance of the model and unveils new horizons in blending images and text-guided image manipulation. The Kandinsky model is created by [Arseniy Shakhmatov](https://github.com/cene555), [Anton Razzhigaev](https://github.com/razzant), [Aleksandr Nikolich](https://github.com/AlexWortega), [Igor Pavlov](https://github.com/boomb0om), [Andrey Kuznetsov](https://github.com/kuznetsoffandrey) and [Denis Dimitrov](https://github.com/denndimitrov) ## Usage Kandinsky 2.2 is available in diffusers! ```python pip install diffusers transformers accelerate ``` ### Text to image ```python from diffusers import AutoPipelineForText2Image import torch pipe = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "portrait of a young women, blue eyes, cinematic" negative_prompt = "low quality, bad quality" image = pipe(prompt=prompt, negative_prompt=negative_prompt, prior_guidance_scale =1.0, height=768, width=768).images[0] image.save("portrait.png") ``` ![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/%20blue%20eyes.png) ### Text Guided Image-to-Image Generation ```python from PIL import Image import requests from io import BytesIO url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" response = requests.get(url) original_image = Image.open(BytesIO(response.content)).convert("RGB") original_image = original_image.resize((768, 512)) ``` ![img](https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg) ```python from diffusers import AutoPipelineForImage2Image import torch pipe = AutoPipelineForImage2Image.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16) pipe.enable_model_cpu_offload() prompt = "A fantasy landscape, Cinematic lighting" negative_prompt = "low quality, bad quality" image = pipe(prompt=prompt, image=original_image, strength=0.3, height=768, width=768).images[0] out.images[0].save("fantasy_land.png") ``` ![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/fantasy_land.png) ### Interpolate ```python from diffusers import KandinskyV22PriorPipeline, KandinskyV22Pipeline from diffusers.utils import load_image import PIL import torch pipe_prior = KandinskyV22PriorPipeline.from_pretrained( "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16 ) pipe_prior.to("cuda") img1 = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" ) img2 = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/starry_night.jpeg" ) # add all the conditions we want to interpolate, can be either text or image images_texts = ["a cat", img1, img2] # specify the weights for each condition in images_texts weights = [0.3, 0.3, 0.4] # We can leave the prompt empty prompt = "" prior_out = pipe_prior.interpolate(images_texts, weights) pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16) pipe.to("cuda") image = pipe(**prior_out, height=768, width=768).images[0] image.save("starry_cat.png") ``` ![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/starry_cat2.2.png) ### Text Guided Inpainting Generation ```python from diffusers import AutoPipelineForInpainting from diffusers.utils import load_image import torch import numpy as np pipe = AutoPipelineForInpainting.from_pretrained("kandinsky-community/kandinsky-2-2-decoder-inpaint", torch_dtype=torch.float16) pipe.enable_model_cpu_offload() prompt = "a hat" init_image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" ) mask = np.zeros((768, 768), dtype=np.float32) # Let's mask out an area above the cat's head mask[:250, 250:-250] = 1 out = pipe( prompt=prompt, image=init_image, mask_image=mask, height=768, width=768, num_inference_steps=150, ) image = out.images[0] image.save("cat_with_hat.png") ``` ![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/cat_with_hat.png) __<font color=red>Breaking change on the mask input:</font>__ We introduced a breaking change for Kandinsky inpainting pipeline in the following pull request: https://github.com/huggingface/diffusers/pull/4207. Previously we accepted a mask format where black pixels represent the masked-out area. We have changed to use white pixels to represent masks instead in order to have a unified mask format across all our pipelines. Please upgrade your inpainting code to follow the above. If you are using Kandinsky Inpaint in production. You now need to change the mask to: ```python # For PIL input import PIL.ImageOps mask = PIL.ImageOps.invert(mask) # For PyTorch and Numpy input mask = 1 - mask ``` ### Text-to-Image Generation with ControlNet Conditioning ```python import torch import numpy as np from transformers import pipeline from diffusers.utils import load_image from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline # let's take an image and extract its depth map. def make_hint(image, depth_estimator): image = depth_estimator(image)["depth"] image = np.array(image) image = image[:, :, None] image = np.concatenate([image, image, image], axis=2) detected_map = torch.from_numpy(image).float() / 255.0 hint = detected_map.permute(2, 0, 1) return hint img = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/cat.png" ).resize((768, 768)) # We can use the `depth-estimation` pipeline from transformers to process the image and retrieve its depth map. depth_estimator = pipeline("depth-estimation") hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda") # Now, we load the prior pipeline and the text-to-image controlnet pipeline pipe_prior = KandinskyV22PriorPipeline.from_pretrained( "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16 ) pipe_prior = pipe_prior.to("cuda") pipe = KandinskyV22ControlnetPipeline.from_pretrained( "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16 ) pipe = pipe.to("cuda") # We pass the prompt and negative prompt through the prior to generate image embeddings prompt = "A robot, 4k photo" negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature" generator = torch.Generator(device="cuda").manual_seed(43) image_emb, zero_image_emb = pipe_prior( prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator ).to_tuple() # Now we can pass the image embeddings and the depth image we extracted to the controlnet pipeline. With Kandinsky 2.2, only prior pipelines accept `prompt` input. You do not need to pass the prompt to the controlnet pipeline. images = pipe( image_embeds=image_emb, negative_image_embeds=zero_image_emb, hint=hint, num_inference_steps=50, generator=generator, height=768, width=768, ).images images[0].save("robot_cat.png") ``` ![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/cat.png) ![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/robot_cat_text2img.png) ### Image-to-Image Generation with ControlNet Conditioning ```python import torch import numpy as np from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline from diffusers.utils import load_image from transformers import pipeline img = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinskyv22/cat.png" ).resize((768, 768)) def make_hint(image, depth_estimator): image = depth_estimator(image)["depth"] image = np.array(image) image = image[:, :, None] image = np.concatenate([image, image, image], axis=2) detected_map = torch.from_numpy(image).float() / 255.0 hint = detected_map.permute(2, 0, 1) return hint depth_estimator = pipeline("depth-estimation") hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda") pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained( "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16 ) pipe_prior = pipe_prior.to("cuda") pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained( "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16 ) pipe = pipe.to("cuda") prompt = "A robot, 4k photo" negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature" generator = torch.Generator(device="cuda").manual_seed(43) # run prior pipeline img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator) negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator) # run controlnet img2img pipeline images = pipe( image=img, strength=0.5, image_embeds=img_emb.image_embeds, negative_image_embeds=negative_emb.image_embeds, hint=hint, num_inference_steps=50, generator=generator, height=768, width=768, ).images images[0].save("robot_cat.png") ``` Here is the output. Compared with the output from our text-to-image controlnet example, it kept a lot more cat facial details from the original image and worked into the robot style we asked for. ![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/robot_cat.png) ## Model Architecture ### Overview Kandinsky 2.2 is a text-conditional diffusion model based on unCLIP and latent diffusion, composed of a transformer-based image prior model, a unet diffusion model, and a decoder. The model architectures are illustrated in the figure below - the chart on the left describes the process to train the image prior model, the figure in the center is the text-to-image generation process, and the figure on the right is image interpolation. <p float="left"> <img src="https://raw.githubusercontent.com/ai-forever/Kandinsky-2/main/content/kandinsky21.png"/> </p> Specifically, the image prior model was trained on CLIP text and image embeddings generated with a pre-trained [CLIP-ViT-G model](https://huggingface.co/laion/CLIP-ViT-g-14-laion2B-s12B-b42K). The trained image prior model is then used to generate CLIP image embeddings for input text prompts. Both the input text prompts and its CLIP image embeddings are used in the diffusion process. A [MoVQGAN](https://openreview.net/forum?id=Qb-AoSw4Jnm) model acts as the final block of the model, which decodes the latent representation into an actual image. ### Details The image prior training of the model was performed on the [LAION Improved Aesthetics dataset](https://huggingface.co/datasets/bhargavsdesai/laion_improved_aesthetics_6.5plus_with_images), and then fine-tuning was performed on the [LAION HighRes data](https://huggingface.co/datasets/laion/laion-high-resolution). The main Text2Image diffusion model was trained on [LAION HighRes dataset](https://huggingface.co/datasets/laion/laion-high-resolution) and then fine-tuned with a dataset of 2M very high-quality high-resolution images with descriptions (COYO, anime, landmarks_russia, and a number of others) was used separately collected from open sources. The main change in Kandinsky 2.2 is the replacement of CLIP-ViT-G. Its image encoder significantly increases the model's capability to generate more aesthetic pictures and better understand text, thus enhancing its overall performance. Due to the switch CLIP model, the image prior model was retrained, and the Text2Image diffusion model was fine-tuned for 2000 iterations. Kandinsky 2.2 was trained on data of various resolutions, from 512 x 512 to 1536 x 1536, and also as different aspect ratios. As a result, Kandinsky 2.2 can generate 1024 x 1024 outputs with any aspect ratio. ### Evaluation We quantitatively measure the performance of Kandinsky 2.1 on the COCO_30k dataset, in zero-shot mode. The table below presents FID. FID metric values ​​for generative models on COCO_30k | | FID (30k)| |:------|----:| | eDiff-I (2022) | 6.95 | | Image (2022) | 7.27 | | Kandinsky 2.1 (2023) | 8.21| | Stable Diffusion 2.1 (2022) | 8.59 | | GigaGAN, 512x512 (2023) | 9.09 | | DALL-E 2 (2022) | 10.39 | | GLIDE (2022) | 12.24 | | Kandinsky 1.0 (2022) | 15.40 | | DALL-E (2021) | 17.89 | | Kandinsky 2.0 (2022) | 20.00 | | GLIGEN (2022) | 21.04 | For more information, please refer to the upcoming technical report. ## BibTex If you find this repository useful in your research, please cite: ``` @misc{kandinsky 2.2, title = {kandinsky 2.2}, author = {Arseniy Shakhmatov, Anton Razzhigaev, Aleksandr Nikolich, Vladimir Arkhipkin, Igor Pavlov, Andrey Kuznetsov, Denis Dimitrov}, year = {2023}, howpublished = {}, } ```
OPI-PG/Qra-13b
OPI-PG
"2024-03-16T11:06:01Z"
22,901
30
transformers
[ "transformers", "safetensors", "llama", "text-generation", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2024-02-27T06:13:02Z"
--- license: llama2 --- <center><img src="https://huggingface.co/OPI-PG/Qra-13b/resolve/main/images/13b-logo.png"></img></center> Qra is a series of LLMs adapted to the Polish language, resulting from a collaboration between the National Information Processing Institute (OPI) and Gdańsk University of Technology (PG). The models were trained on the infrastructure of PG TASK Computing Center using 21 Nvidia A100 cards. The published versions of the Qra models were initialized with the weights of English LLama 2 checkpoints and then further trained on a carefully cleaned, filtered, and deduplicated corpus of Polish texts, totaling about 90 billion tokens. The original corpus consisted primarily of web data, including CommonCrawl dumps, and the MADLAD-400 corpus. ⚠️ **Important: Qra are foundation language models trained with causal language modeling objective on a large corpus of texts. They are therefore not intended for conversational or instruction-following purposes, and should be further fine-tuned to be used for such tasks.** ⚠️ The preprocessing pipeline included the following steps: - Text normalization, removal of URLs. - Removal of documents shorter than 500 characters. - Cleaning sentences in documents using a set of heuristic rules. Among others, sentences consisting of mostly non-alphabetical characters, as well as sentences in languages other than Polish and English, were removed. - Filtering documents using a quality classifier trained on a set of several thousand documents manually labeled as being of high or low quality. The input to the classifier is a set of several statistics ("quality signals") such as the percentage of Polish words, average word and sentence length, number of word and character duplications, proportion of different characters classes in the text. - Filtering documents based on the perplexity value calculated by a lightweight KenLM language model. - Assigning the document to one of 18 topical domains using a trained classifier. - Fuzzy deduplication using the MinHash algorithm within each topical domain. The final distribution of documents by topic is shown in the chart below: <center><img src="https://huggingface.co/OPI-PG/Qra-13b/resolve/main/images/topics.png"></img></center> ## Model details The models were trained for one epoch on sequences of 4096 tokens. During training, we used many modern optimizations such as: - [torch.compile](https://pytorch.org/docs/stable/generated/torch.compile.html) - [adamw_apex_fused](https://huggingface.co/docs/transformers/main/en/perf_train_gpu_one#optimizer-choice) optimizer - [Flash Attention 2](https://github.com/Dao-AILab/flash-attention) - [Mixed precision](https://huggingface.co/docs/transformers/main/en/perf_train_gpu_one#bf16) (`--bf16` and `--tf32` options) - [Gradient accumulation](https://huggingface.co/docs/transformers/main/en/perf_train_gpu_one#gradient-accumulation) - [Fully Sharded Data Parallel (FSDP)](https://pytorch.org/tutorials/intermediate/FSDP_tutorial.html) with the SHARD_GRAD_OP mode - [Gradient checkpointing](https://huggingface.co/docs/transformers/main/en/perf_train_gpu_one#gradient-checkpointing) (only for the 13B model) Below is a summary of the Qra-13B model: | Attribute | Value | | ---- | ---- | | Adapted from | [Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) | | License | [LLama 2 Community License Agreement](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) | | Batch size | 1344 | | Context length | 4096 | | Learning rate | 2e-5 | | Learning rate decay | cosine | | Warmup steps | 0 | | Training time | 35 days | ## Evaluation In this section we compare the perplexity of Qra models on Polish texts with other Polish and English LLMs. Note that perplexity values between different text segmentations are not directly comparable. Therefore, we can draw conclusions based on comparisons only beetween models using the same tokenizer, such as Qra and the original LLama / TinyLLama. ### PolEval-2018 In 2018, the PolEval competition included a language modeling task, for which training and test sets totaling over 20 million Polish sentences were made available. We used the first 10k sentences from the test set to evaluate modern neural language models. To calculate the perplexity, we used a script from the [HuggingFace Evaluate](https://huggingface.co/spaces/evaluate-metric/perplexity) library. <table> <thead> <tr><th>Model</th><th>Perplexity</th></tr> </thead> <tr><td colspan="2"><strong>English models</strong></td></tr> <tr><td>meta-llama/Llama-2-7b-hf</td><td>24.3</td></tr> <tr><td>meta-llama/Llama-2-13b-hf</td><td>21.4</td></tr> <tr><td>mistralai/Mistral-7B-v0.1</td><td>21.4</td></tr> <tr><td>TinyLlama/TinyLlama-1.1B</td><td>40.4</td></tr> <tr><td colspan="2"><strong>Polish models</strong></td></tr> <tr><td>sdadas/polish-gpt2-small</td><td>134.4</td></tr> <tr><td>sdadas/polish-gpt2-medium</td><td>100.8</td></tr> <tr><td>sdadas/polish-gpt2-large</td><td>93.2</td></tr> <tr><td>sdadas/polish-gpt2-xl</td><td>94.1</td></tr> <tr><td>Azurro/APT3-275M-Base</td><td>129.8</td></tr> <tr><td>Azurro/APT3-500M-Base</td><td>153.1</td></tr> <tr><td>Azurro/APT3-1B-Base</td><td>106.8</td></tr> <tr><td>eryk-mazus/polka-1.1b</td><td>18.1</td></tr> <tr><td>szymonrucinski/Curie-7B-v1</td><td>13.5</td></tr> <tr><td colspan="2"><strong>Qra models</strong></td></tr> <tr><td>OPI-PG/Qra-1b</td><td>14.7</td></tr> <tr><td>OPI-PG/Qra-7b</td><td>11.3</td></tr> <tr><td>OPI-PG/Qra-13b</td><td>10.5</td></tr> </table> ### Long documents (2024) Currently, LLMs support contexts of thousands of tokens. Their practical applications usually also involve processing long documents. Therefore, evaluating perplexity on a sentence-based dataset such as PolEval-2018 may not be meaningful. Additionally, the PolEval corpus has been publicly available on the internet for the past few years, which raises the possibility that for some models the training sets have been contaminated by this data. For this reason, we have prepared a new collection consisting of long papers published exclusively in 2024, which will allow us to more reliably test the perplexities of the models on new knowledge that was not available to them at the time of training. The corpus consists of 5,000 documents ranging from several hundred to about 20,000 tokens. Half of the set consists of press texts from Polish news portals from February 2024, the other half are scientific articles published since January 2024. Most of the documents exceed the context size of the evaluated models. To calculate perplexity for these documents, we divided them into chunks of size equal to the model's context length with a stride of 512 tokens, following [this example](https://huggingface.co/docs/transformers/en/perplexity). <table> <thead> <tr><th>Model</th><th>Context</th><th>Perplexity</th></tr> </thead> <tr><td colspan="3"><strong>English models</strong></td></tr> <tr><td>meta-llama/Llama-2-7b-hf</td><td>4096</td><td>5.9</td></tr> <tr><td>meta-llama/Llama-2-13b-hf</td><td>4096</td><td>5.3</td></tr> <tr><td>mistralai/Mistral-7B-v0.1</td><td>4096</td><td>4.9</td></tr> <tr><td>TinyLlama/TinyLlama-1.1B</td><td>2048</td><td>9.6</td></tr> <tr><td colspan="3"><strong>Polish models</strong></td></tr> <tr><td>sdadas/polish-gpt2-small</td><td>2048</td><td>27.3</td></tr> <tr><td>sdadas/polish-gpt2-medium</td><td>2048</td><td>20.3</td></tr> <tr><td>sdadas/polish-gpt2-large</td><td>1536</td><td>18.0</td></tr> <tr><td>sdadas/polish-gpt2-xl</td><td>1536</td><td>16.6</td></tr> <tr><td>Azurro/APT3-275M-Base</td><td>2048</td><td>77.0</td></tr> <tr><td>Azurro/APT3-500M-Base</td><td>2048</td><td>50.5</td></tr> <tr><td>Azurro/APT3-1B-Base</td><td>2048</td><td>19.1</td></tr> <tr><td>eryk-mazus/polka-1.1b</td><td>2048</td><td>6.9</td></tr> <tr><td>szymonrucinski/Curie-7B-v1</td><td>4096</td><td>4.8</td></tr> <tr><td colspan="3"><strong>Qra models</strong></td></tr> <tr><td>OPI-PG/Qra-1b</td><td>4096</td><td>6.1</td></tr> <tr><td>OPI-PG/Qra-7b</td><td>4096</td><td>4.5</td></tr> <tr><td>OPI-PG/Qra-13b</td><td>4096</td><td>4.2</td></tr> </table>
cahya/bert-base-indonesian-522M
cahya
"2021-05-19T13:38:45Z"
22,865
21
transformers
[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "id", "dataset:wikipedia", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
"2022-03-02T23:29:05Z"
--- language: "id" license: "mit" datasets: - wikipedia widget: - text: "Ibu ku sedang bekerja [MASK] sawah." --- # Indonesian BERT base model (uncased) ## Model description It is BERT-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between indonesia and Indonesia. This is one of several other language models that have been pre-trained with indonesian datasets. More detail about its usage on downstream tasks (text classification, text generation, etc) is available at [Transformer based Indonesian Language Models](https://github.com/cahya-wirawan/indonesian-language-models/tree/master/Transformers) ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='cahya/bert-base-indonesian-522M') >>> unmasker("Ibu ku sedang bekerja [MASK] supermarket") [{'sequence': '[CLS] ibu ku sedang bekerja di supermarket [SEP]', 'score': 0.7983310222625732, 'token': 1495}, {'sequence': '[CLS] ibu ku sedang bekerja. supermarket [SEP]', 'score': 0.090003103017807, 'token': 17}, {'sequence': '[CLS] ibu ku sedang bekerja sebagai supermarket [SEP]', 'score': 0.025469014421105385, 'token': 1600}, {'sequence': '[CLS] ibu ku sedang bekerja dengan supermarket [SEP]', 'score': 0.017966199666261673, 'token': 1555}, {'sequence': '[CLS] ibu ku sedang bekerja untuk supermarket [SEP]', 'score': 0.016971781849861145, 'token': 1572}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel model_name='cahya/bert-base-indonesian-522M' tokenizer = BertTokenizer.from_pretrained(model_name) model = BertModel.from_pretrained(model_name) text = "Silakan diganti dengan text apa saja." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in Tensorflow: ```python from transformers import BertTokenizer, TFBertModel model_name='cahya/bert-base-indonesian-522M' tokenizer = BertTokenizer.from_pretrained(model_name) model = TFBertModel.from_pretrained(model_name) text = "Silakan diganti dengan text apa saja." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data This model was pre-trained with 522MB of indonesian Wikipedia. The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are then of the form: ```[CLS] Sentence A [SEP] Sentence B [SEP]```
Helsinki-NLP/opus-mt-ar-es
Helsinki-NLP
"2023-08-16T11:25:40Z"
22,855
1
transformers
[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "ar", "es", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
translation
"2022-03-02T23:29:04Z"
--- language: - ar - es tags: - translation license: apache-2.0 --- ### ara-spa * source group: Arabic * target group: Spanish * OPUS readme: [ara-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-spa/README.md) * model: transformer * source language(s): apc apc_Latn ara arq * target language(s): spa * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-spa/opus-2020-07-03.zip) * test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-spa/opus-2020-07-03.test.txt) * test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-spa/opus-2020-07-03.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.ara.spa | 46.0 | 0.641 | ### System Info: - hf_name: ara-spa - source_languages: ara - target_languages: spa - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-spa/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ar', 'es'] - src_constituents: {'apc', 'ara', 'arq_Latn', 'arq', 'afb', 'ara_Latn', 'apc_Latn', 'arz'} - tgt_constituents: {'spa'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-spa/opus-2020-07-03.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ara-spa/opus-2020-07-03.test.txt - src_alpha3: ara - tgt_alpha3: spa - short_pair: ar-es - chrF2_score: 0.6409999999999999 - bleu: 46.0 - brevity_penalty: 0.9620000000000001 - ref_len: 9708.0 - src_name: Arabic - tgt_name: Spanish - train_date: 2020-07-03 - src_alpha2: ar - tgt_alpha2: es - prefer_old: False - long_pair: ara-spa - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
distil-whisper/distil-small.en
distil-whisper
"2024-03-25T12:09:13Z"
22,792
80
transformers
[ "transformers", "pytorch", "tensorboard", "onnx", "safetensors", "whisper", "automatic-speech-recognition", "audio", "transformers.js", "en", "arxiv:2311.00430", "arxiv:2210.13352", "license:mit", "region:us" ]
automatic-speech-recognition
"2023-12-06T11:35:48Z"
--- language: - en tags: - audio - automatic-speech-recognition - transformers.js inference: false widget: - src: https://cdn-media.huggingface.co/speech_samples/sample1.flac example_title: Librispeech sample 1 output: text: going along slushy country roads and speaking to damp audiences in draughty schoolrooms day after day for a fortnight he'll have to put in an appearance at some place of worship on sunday morning and he can come to us immediately afterwards - src: https://cdn-media.huggingface.co/speech_samples/sample2.flac example_title: Librispeech sample 2 output: text: before he had time to answer a much-encumbered vera burst into the room with the question i say can i leave these here these were a small black pig and a lusty specimen of black-red game-cock pipeline_tag: automatic-speech-recognition license: mit library_name: transformers --- # Distil-Whisper: distil-small.en Distil-Whisper was proposed in the paper [Robust Knowledge Distillation via Large-Scale Pseudo Labelling](https://arxiv.org/abs/2311.00430). It is a distilled version of the Whisper model that is **6 times faster**, 49% smaller, and performs **within 1% WER** on out-of-distribution evaluation sets. This is the repository for distil-small.en, a distilled variant of [Whisper small.en](https://huggingface.co/openai/whisper-small.en). It is the **smallest Distil-Whisper checkpoint**, with just 166M parameters, making it the ideal choice for memory constrained applications (e.g. on-device). For most other applications, the [distil-medium.en](https://huggingface.co/distil-whisper/distil-medium.en) or [distil-large-v2](https://huggingface.co/distil-whisper/distil-large-v2) checkpoints are recommended, since they are both faster and achieve better WER results: | Model | Params / M | Rel. Latency ↑ | Short-Form WER ↓ | Long-Form WER ↓ | |----------------------------------------------------------------------------|------------|----------------|------------------|-----------------| | [large-v3](https://huggingface.co/openai/whisper-large-v3) | 1550 | 1.0 | **8.4** | 11.0 | | [large-v2](https://huggingface.co/openai/whisper-large-v2) | 1550 | 1.0 | 9.1 | 11.7 | | | | | | | | [distil-large-v3](https://huggingface.co/distil-whisper/distil-large-v3) | 756 | 6.3 | 9.7 | **10.8** | | [distil-large-v2](https://huggingface.co/distil-whisper/distil-large-v2) | 756 | 5.8 | 10.1 | 11.6 | | [distil-medium.en](https://huggingface.co/distil-whisper/distil-medium.en) | 394 | **6.8** | 11.1 | 12.4 | | [distil-small.en](https://huggingface.co/distil-whisper/distil-small.en) | **166** | 5.6 | 12.1 | 12.8 | **Note:** Distil-Whisper is currently only available for English speech recognition. We are working with the community to distill Whisper on other languages. If you are interested in distilling Whisper in your language, check out the provided [training code](https://github.com/huggingface/distil-whisper/tree/main/training). We will update the [Distil-Whisper repository](https://github.com/huggingface/distil-whisper/) with multilingual checkpoints when ready! ### Why is distil-small.en slower than distil-large-v2? While [distil-medium.en](https://huggingface.co/distil-whisper/distil-medium.en) and [distil-large-v2](https://huggingface.co/distil-whisper/distil-large-v2) use two decoder layers each, distil-small.en uses four. Using more decoder layers improves the WER performance of the model, at the expense of slower inference speed. We found that four layers was the minimum required to get reasonable WER performance for `distil-small.en`, where it performs to within 3% WER of Whisper [large-v2](https://huggingface.co/openai/whisper-large-v2) while being 5.6x faster. When we tried distilling with just two layers, the model was over 5% worse than large-v2, albeit 7.8x faster. We leave distilling a two layer small.en model as future works. ## Usage Distil-Whisper is supported in Hugging Face 🤗 Transformers from version 4.35 onwards. To run the model, first install the latest version of the Transformers library. For this example, we'll also install 🤗 Datasets to load toy audio dataset from the Hugging Face Hub: ```bash pip install --upgrade pip pip install --upgrade transformers accelerate datasets[audio] ``` ### Short-Form Transcription The model can be used with the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) class to transcribe short-form audio files (< 30-seconds) as follows: ```python import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline from datasets import load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "distil-whisper/distil-small.en" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"]) ``` To transcribe a local audio file, simply pass the path to your audio file when you call the pipeline: ```diff - result = pipe(sample) + result = pipe("audio.mp3") ``` ### Long-Form Transcription Distil-Whisper uses a chunked algorithm to transcribe long-form audio files (> 30-seconds). In practice, this chunked long-form algorithm is 9x faster than the sequential algorithm proposed by OpenAI in the Whisper paper (see Table 7 of the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430)). To enable chunking, pass the `chunk_length_s` parameter to the `pipeline`. For Distil-Whisper, a chunk length of 15-seconds is optimal. To activate batching, pass the argument `batch_size`: ```python import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline from datasets import load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "distil-whisper/distil-small.en" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, chunk_length_s=15, batch_size=16, torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("distil-whisper/librispeech_long", "default", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"]) ``` <!--- **Tip:** The pipeline can also be used to transcribe an audio file from a remote URL, for example: ```python result = pipe("https://huggingface.co/datasets/sanchit-gandhi/librispeech_long/resolve/main/audio.wav") ``` ---> ### Speculative Decoding Distil-Whisper can be used as an assistant model to Whisper for [speculative decoding](https://huggingface.co/blog/whisper-speculative-decoding). Speculative decoding mathematically ensures the exact same outputs as Whisper are obtained while being 2 times faster. This makes it the perfect drop-in replacement for existing Whisper pipelines, since the same outputs are guaranteed. In the following code-snippet, we load the assistant Distil-Whisper model standalone to the main Whisper pipeline. We then specify it as the "assistant model" for generation: ```python from transformers import pipeline, AutoModelForSpeechSeq2Seq, AutoProcessor import torch from datasets import load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 assistant_model_id = "distil-whisper/distil-small.en" assistant_model = AutoModelForSpeechSeq2Seq.from_pretrained( assistant_model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) assistant_model.to(device) model_id = "openai/whisper-medium.en" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, generate_kwargs={"assistant_model": assistant_model}, torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"]) ``` ## Additional Speed & Memory Improvements You can apply additional speed and memory improvements to Distil-Whisper which we cover in the following. ### Flash Attention We recommend using [Flash-Attention 2](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#flashattention-2) if your GPU allows for it. To do so, you first need to install [Flash Attention](https://github.com/Dao-AILab/flash-attention): ``` pip install flash-attn --no-build-isolation ``` and then all you have to do is to pass `use_flash_attention_2=True` to `from_pretrained`: ```diff - model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True) + model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True, use_flash_attention_2=True) ``` ### Torch Scale-Product-Attention (SDPA) If your GPU does not support Flash Attention, we recommend making use of [BetterTransformers](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#bettertransformer). To do so, you first need to install optimum: ``` pip install --upgrade optimum ``` And then convert your model to a "BetterTransformer" model before using it: ```diff model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True) + model = model.to_bettertransformer() ``` ### Running Distil-Whisper in `openai-whisper` To use the model in the original Whisper format, first ensure you have the [`openai-whisper`](https://pypi.org/project/openai-whisper/) package installed: ```bash pip install --upgrade openai-whisper ``` The following code-snippet demonstrates how to transcribe a sample file from the LibriSpeech dataset loaded using 🤗 Datasets: ```python import torch from datasets import load_dataset from huggingface_hub import hf_hub_download from whisper import load_model, transcribe distil_small_en = hf_hub_download(repo_id="distil-whisper/distil-small.en", filename="original-model.bin") model = load_model(distil_small_en) dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") sample = dataset[0]["audio"]["array"] sample = torch.from_numpy(sample).float() pred_out = transcribe(model, audio=sample) print(pred_out["text"]) ``` Note that the model weights will be downloaded and saved to your cache the first time you run the example. Subsequently, you can re-use the same example, and the weights will be loaded directly from your cache without having to download them again. To transcribe a local audio file, simply pass the path to the audio file as the `audio` argument to transcribe: ```python pred_out = transcribe(model, audio="audio.mp3") ``` ### Whisper.cpp Distil-Whisper can be run from the [Whisper.cpp](https://github.com/ggerganov/whisper.cpp) repository with the original sequential long-form transcription algorithm. In a [provisional benchmark](https://github.com/ggerganov/whisper.cpp/pull/1424#issuecomment-1793513399) on Mac M1, `distil-small.en` is over 4x faster than `large-v2`, while performing to within 1.4% WER over long-form audio. Steps for getting started: 1. Clone the Whisper.cpp repository: ``` git clone https://github.com/ggerganov/whisper.cpp.git cd whisper.cpp ``` 2. Download the ggml weights for `distil-small.en` from the Hugging Face Hub: ```bash python -c "from huggingface_hub import hf_hub_download; hf_hub_download(repo_id='distil-whisper/distil-small.en', filename='ggml-distil-small.en.bin', local_dir='./models')" ``` Note that if you do not have the `huggingface_hub` package installed, you can also download the weights with `wget`: ```bash wget https://huggingface.co/distil-whisper/distil-small.en/resolve/main/ggml-distil-small.en.bin -P ./models ``` 3. Run inference using the provided sample audio: ```bash make -j && ./main -m models/ggml-distil-small.en.bin -f samples/jfk.wav ``` ### Transformers.js Distil-Whisper can even run completely in your web browser with [Transformers.js](http://github.com/xenova/transformers.js): 1. Install Transformers.js from [NPM](https://www.npmjs.com/package/@xenova/transformers): ```bash npm i @xenova/transformers ``` 2. Import the library and perform inference with the pipeline API. ```js import { pipeline } from '@xenova/transformers'; const transcriber = await pipeline('automatic-speech-recognition', 'distil-whisper/distil-small.en'); const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav'; const output = await transcriber(url); // { text: " And so my fellow Americans, ask not what your country can do for you. Ask what you can do for your country." } ``` Check out the online [Distil-Whisper Web demo](https://huggingface.co/spaces/Xenova/distil-whisper-web) to try it out yourself. As you'll see, it runs locally in your browser: no server required! See the [docs](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.AutomaticSpeechRecognitionPipeline) for more information. ### Candle Coming soon! <!--- Through an integration with Hugging Face [Candle](https://github.com/huggingface/candle/tree/main) 🕯️, Distil-Whisper is now available in the Rust library 🦀 Benefit from: * Optimised CPU backend with optional MKL support for x86 and Accelerate for Macs * CUDA backend for efficiently running on GPUs, multiple GPU distribution via NCCL * WASM support: run Distil-Whisper in a browser Steps for getting started: 1. Install [`candle-core`](https://github.com/huggingface/candle/tree/main/candle-core) as explained [here](https://huggingface.github.io/candle/guide/installation.html) 2. Clone the `candle` repository locally: ``` git clone https://github.com/huggingface/candle.git ``` 3. Enter the example directory for [Whisper](https://github.com/huggingface/candle/tree/main/candle-examples/examples/whisper): ``` cd candle/candle-examples/examples/whisper ``` 4. Run an example: ``` cargo run --example whisper --release -- --model distil-small.en ``` 5. To specify your own audio file, add the `--input` flag: ``` cargo run --example whisper --release -- --model distil-small.en --input audio.wav ``` ---> ### 8bit & 4bit Quantization Coming soon! ## Model Details Distil-Whisper inherits the encoder-decoder architecture from Whisper. The encoder maps a sequence of speech vector inputs to a sequence of hidden-state vectors. The decoder auto-regressively predicts text tokens, conditional on all previous tokens and the encoder hidden-states. Consequently, the encoder is only run forward once, whereas the decoder is run as many times as the number of tokens generated. In practice, this means the decoder accounts for over 90% of total inference time. Thus, to optimise for latency, the focus is on minimising the inference time of the decoder. To distill the Whisper model, we reduce the number of decoder layers while keeping the encoder fixed. The encoder (shown in green) is entirely copied from the teacher to the student and frozen during training. The student's decoder consists of a subset of the teacher decoder layers, which are intialised from maximally spaced layers. The model is then trained on a weighted sum of the KL divergence and pseudo-label loss terms. <p align="center"> <img src="https://huggingface.co/datasets/distil-whisper/figures/resolve/main/architecture.png?raw=true" width="600"/> </p> ## Evaluation The following code-snippets demonstrates how to evaluate the Distil-Whisper model on the LibriSpeech validation.clean dataset with [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet), meaning no audio data has to be downloaded to your local device. First, we need to install the required packages, including 🤗 Datasets to stream and load the audio data, and 🤗 Evaluate to perform the WER calculation: ```bash pip install --upgrade pip pip install --upgrade transformers datasets[audio] evaluate jiwer ``` Evaluation can then be run end-to-end with the following example: ```python from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor from transformers.models.whisper.english_normalizer import EnglishTextNormalizer from datasets import load_dataset from evaluate import load import torch from tqdm import tqdm # define our torch configuration device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "distil-whisper/distil-small.en" # load the model + processor model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, use_safetensors=True, low_cpu_mem_usage=True) model = model.to(device) processor = AutoProcessor.from_pretrained(model_id) # load the dataset with streaming mode dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True) # define the evaluation metric wer_metric = load("wer") normalizer = EnglishTextNormalizer(processor.tokenizer.english_spelling_normalizer) def inference(batch): # 1. Pre-process the audio data to log-mel spectrogram inputs audio = [sample["array"] for sample in batch["audio"]] input_features = processor(audio, sampling_rate=batch["audio"][0]["sampling_rate"], return_tensors="pt").input_features input_features = input_features.to(device, dtype=torch_dtype) # 2. Auto-regressively generate the predicted token ids pred_ids = model.generate(input_features, max_new_tokens=128) # 3. Decode the token ids to the final transcription batch["transcription"] = processor.batch_decode(pred_ids, skip_special_tokens=True) batch["reference"] = batch["text"] return batch dataset = dataset.map(function=inference, batched=True, batch_size=16) all_transcriptions = [] all_references = [] # iterate over the dataset and run inference for i, result in tqdm(enumerate(dataset), desc="Evaluating..."): all_transcriptions.append(result["transcription"]) all_references.append(result["reference"]) # normalize predictions and references all_transcriptions = [normalizer(transcription) for transcription in all_transcriptions] all_references = [normalizer(reference) for reference in all_references] # compute the WER metric wer = 100 * wer_metric.compute(predictions=all_transcriptions, references=all_references) print(wer) ``` **Print Output:** ``` 3.4326070294536297 ``` ## Intended Use Distil-Whisper is intended to be a drop-in replacement for Whisper on English speech recognition. In particular, it achieves comparable WER results over out-of-distribution test data, while being 6x faster over both short and long-form audio. ## Data Distil-Whisper is trained on 22,000 hours of audio data from 9 open-source, permissively licensed speech datasets on the Hugging Face Hub: | Dataset | Size / h | Speakers | Domain | Licence | |-----------------------------------------------------------------------------------------|----------|----------|-----------------------------|-----------------| | [People's Speech](https://huggingface.co/datasets/MLCommons/peoples_speech) | 12,000 | unknown | Internet Archive | CC-BY-SA-4.0 | | [Common Voice 13](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0) | 3,000 | unknown | Narrated Wikipedia | CC0-1.0 | | [GigaSpeech](https://huggingface.co/datasets/speechcolab/gigaspeech) | 2,500 | unknown | Audiobook, podcast, YouTube | apache-2.0 | | Fisher | 1,960 | 11,900 | Telephone conversations | LDC | | [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) | 960 | 2,480 | Audiobooks | CC-BY-4.0 | | [VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli) | 540 | 1,310 | European Parliament | CC0 | | [TED-LIUM](https://huggingface.co/datasets/LIUM/tedlium) | 450 | 2,030 | TED talks | CC-BY-NC-ND 3.0 | | SwitchBoard | 260 | 540 | Telephone conversations | LDC | | [AMI](https://huggingface.co/datasets/edinburghcstr/ami) | 100 | unknown | Meetings | CC-BY-4.0 | |||||| | **Total** | 21,770 | 18,260+ | | | The combined dataset spans 10 distinct domains and over 50k speakers. The diversity of this dataset is crucial to ensuring the distilled model is robust to audio distributions and noise. The audio data is then pseudo-labelled using the Whisper large-v2 model: we use Whisper to generate predictions for all the audio in our training set and use these as the target labels during training. Using pseudo-labels ensures that the transcriptions are consistently formatted across datasets and provides sequence-level distillation signal during training. ## WER Filter The Whisper pseudo-label predictions are subject to mis-transcriptions and hallucinations. To ensure we only train on accurate pseudo-labels, we employ a simple WER heuristic during training. First, we normalise the Whisper pseudo-labels and the ground truth labels provided by each dataset. We then compute the WER between these labels. If the WER exceeds a specified threshold, we discard the training example. Otherwise, we keep it for training. Section 9.2 of the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430) demonstrates the effectiveness of this filter for improving downstream performance of the distilled model. We also partially attribute Distil-Whisper's robustness to hallucinations to this filter. ## Training The model was trained for 50,000 optimisation steps (or 12 epochs) with batch size 2056. The Tensorboard training logs can be found under: https://huggingface.co/distil-whisper/distil-small.en/tensorboard?params=scalars#frame ## Results The distilled model performs to within 1% WER of Whisper on out-of-distribution (OOD) short-form audio, and outperforms Whisper by 0.1% on OOD long-form audio. This performance gain is attributed to lower hallucinations. For a detailed per-dataset breakdown of the evaluation results, refer to Tables 16 and 17 of the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430) Distil-Whisper is also evaluated on the [ESB benchmark](https://arxiv.org/abs/2210.13352) datasets as part of the [OpenASR leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard), where it performs to within 0.2% WER of Whisper. ## Reproducing Distil-Whisper Training and evaluation code to reproduce Distil-Whisper is available under the Distil-Whisper repository: https://github.com/huggingface/distil-whisper/tree/main/training ## License Distil-Whisper inherits the [MIT license](https://github.com/huggingface/distil-whisper/blob/main/LICENSE) from OpenAI's Whisper model. ## Citation If you use this model, please consider citing the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430): ``` @misc{gandhi2023distilwhisper, title={Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling}, author={Sanchit Gandhi and Patrick von Platen and Alexander M. Rush}, year={2023}, eprint={2311.00430}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Acknowledgements * OpenAI for the Whisper [model](https://huggingface.co/openai/whisper-large-v2) and [original codebase](https://github.com/openai/whisper) * Hugging Face 🤗 [Transformers](https://github.com/huggingface/transformers) for the model integration * Google's [TPU Research Cloud (TRC)](https://sites.research.google/trc/about/) programme for Cloud TPU v4s * [`@rsonavane`](https://huggingface.co/rsonavane/distil-whisper-large-v2-8-ls) for releasing an early iteration of Distil-Whisper on the LibriSpeech dataset
microsoft/Multilingual-MiniLM-L12-H384
microsoft
"2022-08-10T07:27:42Z"
22,761
66
transformers
[ "transformers", "pytorch", "tf", "jax", "bert", "text-classification", "multilingual", "en", "ar", "bg", "de", "el", "es", "fr", "hi", "ru", "sw", "th", "tr", "ur", "vi", "zh", "arxiv:2002.10957", "arxiv:1809.05053", "arxiv:1911.02116", "arxiv:1910.07475", "license:mit", "endpoints_compatible", "region:us" ]
text-classification
"2022-03-02T23:29:05Z"
--- language: - multilingual - en - ar - bg - de - el - es - fr - hi - ru - sw - th - tr - ur - vi - zh thumbnail: https://huggingface.co/front/thumbnails/microsoft.png tags: - text-classification license: mit --- ## MiniLM: Small and Fast Pre-trained Models for Language Understanding and Generation MiniLM is a distilled model from the paper "[MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://arxiv.org/abs/2002.10957)". Please find the information about preprocessing, training and full details of the MiniLM in the [original MiniLM repository](https://github.com/microsoft/unilm/blob/master/minilm/). Please note: This checkpoint uses `BertModel` with `XLMRobertaTokenizer` so `AutoTokenizer` won't work with this checkpoint! ### Multilingual Pretrained Model - Multilingual-MiniLMv1-L12-H384: 12-layer, 384-hidden, 12-heads, 21M Transformer parameters, 96M embedding parameters Multilingual MiniLM uses the same tokenizer as XLM-R. But the Transformer architecture of our model is the same as BERT. We provide the fine-tuning code on XNLI based on [huggingface/transformers](https://github.com/huggingface/transformers). Please replace `run_xnli.py` in transformers with [ours](https://github.com/microsoft/unilm/blob/master/minilm/examples/run_xnli.py) to fine-tune multilingual MiniLM. We evaluate the multilingual MiniLM on cross-lingual natural language inference benchmark (XNLI) and cross-lingual question answering benchmark (MLQA). #### Cross-Lingual Natural Language Inference - [XNLI](https://arxiv.org/abs/1809.05053) We evaluate our model on cross-lingual transfer from English to other languages. Following [Conneau et al. (2019)](https://arxiv.org/abs/1911.02116), we select the best single model on the joint dev set of all the languages. | Model | #Layers | #Hidden | #Transformer Parameters | Average | en | fr | es | de | el | bg | ru | tr | ar | vi | th | zh | hi | sw | ur | |---------------------------------------------------------------------------------------------|---------|---------|-------------------------|---------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------| | [mBERT](https://github.com/google-research/bert) | 12 | 768 | 85M | 66.3 | 82.1 | 73.8 | 74.3 | 71.1 | 66.4 | 68.9 | 69.0 | 61.6 | 64.9 | 69.5 | 55.8 | 69.3 | 60.0 | 50.4 | 58.0 | | [XLM-100](https://github.com/facebookresearch/XLM#pretrained-cross-lingual-language-models) | 16 | 1280 | 315M | 70.7 | 83.2 | 76.7 | 77.7 | 74.0 | 72.7 | 74.1 | 72.7 | 68.7 | 68.6 | 72.9 | 68.9 | 72.5 | 65.6 | 58.2 | 62.4 | | [XLM-R Base](https://arxiv.org/abs/1911.02116) | 12 | 768 | 85M | 74.5 | 84.6 | 78.4 | 78.9 | 76.8 | 75.9 | 77.3 | 75.4 | 73.2 | 71.5 | 75.4 | 72.5 | 74.9 | 71.1 | 65.2 | 66.5 | | **mMiniLM-L12xH384** | 12 | 384 | 21M | 71.1 | 81.5 | 74.8 | 75.7 | 72.9 | 73.0 | 74.5 | 71.3 | 69.7 | 68.8 | 72.1 | 67.8 | 70.0 | 66.2 | 63.3 | 64.2 | This example code fine-tunes **12**-layer multilingual MiniLM on XNLI. ```bash # run fine-tuning on XNLI DATA_DIR=/{path_of_data}/ OUTPUT_DIR=/{path_of_fine-tuned_model}/ MODEL_PATH=/{path_of_pre-trained_model}/ python ./examples/run_xnli.py --model_type minilm \ --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \ --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \ --tokenizer_name xlm-roberta-base \ --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_gpu_train_batch_size 128 \ --learning_rate 5e-5 \ --num_train_epochs 5 \ --per_gpu_eval_batch_size 32 \ --weight_decay 0.001 \ --warmup_steps 500 \ --save_steps 1500 \ --logging_steps 1500 \ --eval_all_checkpoints \ --language en \ --fp16 \ --fp16_opt_level O2 ``` #### Cross-Lingual Question Answering - [MLQA](https://arxiv.org/abs/1910.07475) Following [Lewis et al. (2019b)](https://arxiv.org/abs/1910.07475), we adopt SQuAD 1.1 as training data and use MLQA English development data for early stopping. | Model F1 Score | #Layers | #Hidden | #Transformer Parameters | Average | en | es | de | ar | hi | vi | zh | |--------------------------------------------------------------------------------------------|---------|---------|-------------------------|---------|------|------|------|------|------|------|------| | [mBERT](https://github.com/google-research/bert) | 12 | 768 | 85M | 57.7 | 77.7 | 64.3 | 57.9 | 45.7 | 43.8 | 57.1 | 57.5 | | [XLM-15](https://github.com/facebookresearch/XLM#pretrained-cross-lingual-language-models) | 12 | 1024 | 151M | 61.6 | 74.9 | 68.0 | 62.2 | 54.8 | 48.8 | 61.4 | 61.1 | | [XLM-R Base](https://arxiv.org/abs/1911.02116) (Reported) | 12 | 768 | 85M | 62.9 | 77.8 | 67.2 | 60.8 | 53.0 | 57.9 | 63.1 | 60.2 | | [XLM-R Base](https://arxiv.org/abs/1911.02116) (Our fine-tuned) | 12 | 768 | 85M | 64.9 | 80.3 | 67.0 | 62.7 | 55.0 | 60.4 | 66.5 | 62.3 | | **mMiniLM-L12xH384** | 12 | 384 | 21M | 63.2 | 79.4 | 66.1 | 61.2 | 54.9 | 58.5 | 63.1 | 59.0 | ### Citation If you find MiniLM useful in your research, please cite the following paper: ``` latex @misc{wang2020minilm, title={MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers}, author={Wenhui Wang and Furu Wei and Li Dong and Hangbo Bao and Nan Yang and Ming Zhou}, year={2020}, eprint={2002.10957}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
QuantFactory/L3-8B-Chara-v1-Alpha-GGUF
QuantFactory
"2024-07-01T07:15:54Z"
22,749
0
null
[ "gguf", "region:us" ]
null
"2024-07-01T05:42:51Z"
Entry not found
textattack/roberta-base-MRPC
textattack
"2021-05-20T22:07:47Z"
22,748
1
transformers
[ "transformers", "pytorch", "jax", "roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
"2022-03-02T23:29:05Z"
## TextAttack Model Card This `roberta-base` model was fine-tuned for sequence classification using TextAttack and the glue dataset loaded using the `nlp` library. The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 3e-05, and a maximum sequence length of 256. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.9117647058823529, as measured by the eval set accuracy, found after 2 epochs. For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
soleimanian/financial-roberta-large-sentiment
soleimanian
"2022-10-12T21:04:39Z"
22,722
36
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "Sentiment", "RoBERTa", "Financial Statements", "Accounting", "Finance", "Business", "ESG", "CSR Reports", "Financial News", "Earnings Call Transcripts", "Sustainability", "Corporate governance", "eng", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
"2022-05-16T04:09:10Z"
--- license: apache-2.0 language: - eng tags: - text-classification - Sentiment - RoBERTa - Financial Statements - Accounting - Finance - Business - ESG - CSR Reports - Financial News - Earnings Call Transcripts - Sustainability - Corporate governance --- <!DOCTYPE html> <html> <body> <h1><b>Financial-RoBERTa</b></h1> <p><b>Financial-RoBERTa</b> is a pre-trained NLP model to analyze sentiment of financial text including:</p> <ul style="PADDING-LEFT: 40px"> <li>Financial Statements,</li> <li>Earnings Announcements,</li> <li>Earnings Call Transcripts,</li> <li>Corporate Social Responsibility (CSR) Reports,</li> <li>Environmental, Social, and Governance (ESG) News,</li> <li>Financial News,</li> <li>Etc.</li> </ul> <p>Financial-RoBERTa is built by further training and fine-tuning the RoBERTa Large language model using a large corpus created from 10k, 10Q, 8K, Earnings Call Transcripts, CSR Reports, ESG News, and Financial News text.</p> <p>The model will give softmax outputs for three labels: <b>Positive</b>, <b>Negative</b> or <b>Neutral</b>.</p> <p><b>How to perform sentiment analysis:</b></p> <p>The easiest way to use the model for single predictions is Hugging Face's sentiment analysis pipeline, which only needs a couple lines of code as shown in the following example:</p> <pre> <code> from transformers import pipeline sentiment_analysis = pipeline("sentiment-analysis",model="soleimanian/financial-roberta-large-sentiment") print(sentiment_analysis("In fiscal 2021, we generated a net yield of approximately 4.19% on our investments, compared to approximately 5.10% in fiscal 2020.")) </code> </pre> <p>I provide an example script via <a href="https://colab.research.google.com/drive/11RGWU3UDtxnjan8Ug6dyX82m9fBV6CGo?usp=sharing" target="_blank">Google Colab</a>. You can load your data to a Google Drive and run the script for free on a Colab. <p><b>Citation and contact:</b></p> <p>Please cite <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4115943" target="_blank">this paper</a> when you use the model. Feel free to reach out to [email protected] with any questions or feedback you may have.<p/> </body> </html>
dstefa/roberta-base_topic_classification_nyt_news
dstefa
"2024-01-25T09:31:05Z"
22,672
2
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "topic", "classification", "news", "dataset:dstefa/New_York_Times_Topics", "base_model:roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
"2024-01-16T20:26:22Z"
--- license: mit base_model: roberta-base tags: - topic - classification - news - roberta metrics: - accuracy - f1 - precision - recall datasets: - dstefa/New_York_Times_Topics widget: - text: >- Olympic champion Kostas Kederis today left hospital ahead of his date with IOC inquisitors claiming his innocence and vowing. example_title: Sports - text: >- Although many individuals are doing fever checks to screen for Covid-19, many Covid-19 patients never have a fever. example_title: Health and Wellness - text: >- Twelve myths about Russia's War in Ukraine exposed example_title: Crime model-index: - name: roberta-base_topic_classification_nyt_news results: - task: name: Text Classification type: text-classification dataset: name: New_York_Times_Topics type: News metrics: - type: F1 name: F1 value: 0.91 - type: accuracy name: accuracy value: 0.91 - type: precision name: precision value: 0.91 - type: recall name: recall value: 0.91 pipeline_tag: text-classification --- # roberta-base_topic_classification_nyt_news This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the NYT News dataset, which contains 256,000 news titles from articles published from 2000 to the present (https://www.kaggle.com/datasets/aryansingh0909/nyt-articles-21m-2000-present). It achieves the following results on the test set of 51200 cases: - Accuracy: 0.91 - F1: 0.91 - Precision: 0.91 - Recall: 0.91 ## Training data Training data was classified as follow: class |Description -|- 0 |Sports 1 |Arts, Culture, and Entertainment 2 |Business and Finance 3 |Health and Wellness 4 |Lifestyle and Fashion 5 |Science and Technology 6 |Politics 7 |Crime ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.3192 | 1.0 | 20480 | 0.4078 | 0.8865 | 0.8859 | 0.8892 | 0.8865 | | 0.2863 | 2.0 | 40960 | 0.4271 | 0.8972 | 0.8970 | 0.8982 | 0.8972 | | 0.1979 | 3.0 | 61440 | 0.3797 | 0.9094 | 0.9092 | 0.9098 | 0.9094 | | 0.1239 | 4.0 | 81920 | 0.3981 | 0.9117 | 0.9113 | 0.9114 | 0.9117 | | 0.1472 | 5.0 | 102400 | 0.4033 | 0.9137 | 0.9135 | 0.9134 | 0.9137 | ### Model performance -|precision|recall|f1|support -|-|-|-|- Sports|0.97|0.98|0.97|6400 Arts, Culture, and Entertainment|0.94|0.95|0.94|6400 Business and Finance|0.85|0.84|0.84|6400 Health and Wellness|0.90|0.93|0.91|6400 Lifestyle and Fashion|0.95|0.95|0.95|6400 Science and Technology|0.89|0.83|0.86|6400 Politics|0.93|0.88|0.90|6400 Crime|0.85|0.93|0.89|6400 | | | | accuracy|||0.91|51200 macro avg|0.91|0.91|0.91|51200 weighted avg|0.91|0.91|0.91|51200 ### How to use roberta-base_topic_classification_nyt_news with HuggingFace ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("dstefa/roberta-base_topic_classification_nyt_news") model = AutoModelForSequenceClassification.from_pretrained("dstefa/roberta-base_topic_classification_nyt_news") pipe = pipeline("text-classification", model=model, tokenizer=tokenizer) text = "Kederis proclaims innocence Olympic champion Kostas Kederis today left hospital ahead of his date with IOC inquisitors claiming his innocence and vowing." pipe(text) [{'label': 'Sports', 'score': 0.9989326596260071}] ``` ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2
Yntec/DreamlikeShaper
Yntec
"2024-06-12T13:28:43Z"
22,663
2
diffusers
[ "diffusers", "safetensors", "General", "Art", "Fantasy", "Lykon", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
"2024-06-12T10:23:08Z"
--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - General - Art - Fantasy - Lykon - stable-diffusion - stable-diffusion-diffusers - diffusers - text-to-image Inference: True --- # DreamlikeShaper A mix of DreamShaper 8 and DreamShaper 6.2 with artistic models to increase their range and style. Comparison (1 of 2): ![Free text to image Dreamlikeshaper AI comparison](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/5fBe4bL6lmzuZ7v3sTQ1w.png) WHY SO SERIOUS??? Comparison (2 of 2): ![Free text to image Dreamlikeshaper AI prompts comparison](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/J4nivz_LE-geXRzFYiDLi.png) (Click for larger) What? I did NOT ask for a girl! Prompt: Masterpiece character portrait of Kitten holding pizza boxes | artstation, digital illustration , digital art tutorial, color page, tone mapping, pixiv, ghibli, landscape artwork by HAYAO MIYAZAKI and artgerm . very strong shaded Painting. Checkmate! Samples and prompts: ![Free online AI image generator Dreamlike Shaper](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/7WwsAtxNOZCGClGBcnY76.png) (Click for larger) Top left: perfect face, full body, Pretty CUTE LITTLE GIRL, masterpiece, highest quality, 1girl, dark eyes, sweater, skirt, highly detailed Top right: 8k portrait of beautiful girl with brown hair, detailed beautiful eyes, intricate, elegant, highly detailed, majestic, digital photography, art by artgerm and ruan jia and greg rutkowski surreal painting gold butterfly filigree, infinity, infinite symbol, broken glass, masterpiece, sidelighting, finely hdr, detailed background window to a new dimension, plants and flowers Bottom left: 80s mage handsome wizard guy as benjamin fighting dinosaur dragon portrait with detailed retro face and eyes, movie still, sword and shield holding jupiter fire sunset in the sky with action, his hands, he stands on a mountain, under the there is a small town, illustration, sharp focus, very detailed, 8 k, hd Bottom right: photo of 1car, sporty, fast, sleek, sexy, aggressive, high performance, daytime, futuristic cityscape, ultra-high-definition, photorealistic, 8k uhd, high-quality, ultra sharp detail. Mercedes oue Original pages: https://civitai.com/models/4384?modelVersionId=128713 (DreamShaper 8) https://civitai.com/models/4384?modelVersionId=88504 (DreamShaper 6.2)
mradermacher/Meta-Llama-3-8B-Instruct-abliterated-v3-GGUF
mradermacher
"2024-06-27T16:07:00Z"
22,638
0
transformers
[ "transformers", "gguf", "en", "base_model:failspy/Meta-Llama-3-8B-Instruct-abliterated-v3", "license:llama3", "endpoints_compatible", "region:us" ]
null
"2024-06-26T23:46:57Z"
--- base_model: failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 language: - en library_name: transformers license: llama3 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct-abliterated-v3-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct-abliterated-v3-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q2_K.gguf) | Q2_K | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct-abliterated-v3-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-abliterated-v3.IQ3_XS.gguf) | IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct-abliterated-v3-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q3_K_S.gguf) | Q3_K_S | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct-abliterated-v3-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-abliterated-v3.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct-abliterated-v3-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-abliterated-v3.IQ3_M.gguf) | IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct-abliterated-v3-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct-abliterated-v3-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q3_K_L.gguf) | Q3_K_L | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct-abliterated-v3-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-abliterated-v3.IQ4_XS.gguf) | IQ4_XS | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct-abliterated-v3-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct-abliterated-v3-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct-abliterated-v3-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q5_K_S.gguf) | Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct-abliterated-v3-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q5_K_M.gguf) | Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct-abliterated-v3-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q6_K.gguf) | Q6_K | 6.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct-abliterated-v3-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-8B-Instruct-abliterated-v3-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-abliterated-v3.f16.gguf) | f16 | 16.2 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
duyntnet/Karen_TheEditor_V2_STRICT_Mistral_7B-imatrix-GGUF
duyntnet
"2024-06-27T09:41:22Z"
22,637
0
transformers
[ "transformers", "gguf", "imatrix", "Karen_TheEditor_V2_STRICT_Mistral_7B", "text-generation", "en", "license:other", "region:us" ]
text-generation
"2024-06-27T06:14:03Z"
--- license: other language: - en pipeline_tag: text-generation inference: false tags: - transformers - gguf - imatrix - Karen_TheEditor_V2_STRICT_Mistral_7B --- Quantizations of https://huggingface.co/FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B # From original readme ## Usage It should be used by submitting a paragraph or block of text at a time. ## Model uses ChatML ``` <|im_start|>system <|im_end|> <|im_start|>user Edit the following text for spelling and grammar mistakes: {paragraph of text} <|im_end|> <|im_start|>assistant ``` Note the pretext: *Edit the following text for spelling and grammar mistakes:* before the actual text. This way Karen wouldn't start talking ABOUT the text. ## Recomended settings - Temperature: 0.7 - top_p: 0.1 - top_k: 40 - repetition penalty: 1.18 Karen could also be used in chatting. But if Karen is involved in a conversation where the text is lengthy, she might interpret your message as needing proofreading assistance rather than simply acknowledging that you are using her in casual chat. Example: >Edit the following text for spelling and grammar mistakes: I see future writing more about you writing the story any way you can and LLM editing it and making it into a proper fiction. That seems to be more satisfying way where you could feel you actually had done something creATIVE. So human is the creative, LLM is the burger flipping intern. >The opposite is what many people are trying rn. Make LLM to invent story (from premise, to creating outline to writing actual chapters) and the human to somehow assemble it from those pieces. So LLM is the creative man, and human is the floor polisher. >I can't believe a normal person would find it satisfying long term. >So in my view, the job of editor would be replaced by Ai, not the job of author. (Grammarly Score: 48) Output STRICT: >I see the future of writing being more about you writing the story any way you can and having an AI like LLM edit it and make it into a proper fiction. That seems to be a more satisfying way where you could feel you actually did something creative. So humans are the creative ones, while LLMs are the burger-flipping interns. > >On the other hand, many people are currently trying to do the exact opposite. They want to make LLMs create stories (from premises, to outlining, to writing entire chapters). And then they want humans to somehow piece them together from these parts. In this scenario, LLMs become the creative individuals, and humans become the floor polishers. > >I can't imagine a regular person finding that satisfying over the long run. > >In my opinion, the role of an editor will eventually be taken over by AI, not the role of an author. (Grammarly Score: 93) Output CREATIVE: >I see the future of writing being more focused on you writing the story any way you can and having an AI like GPT-3 edit it and turn it into a proper piece of fiction. This seems to be a more satisfying approach where you can truly feel that you have done something creative. In this scenario, humans remain the creative force, while the AI acts as the burger flipping intern. > >On the other hand, there are those who argue that we should let the AI generate stories from scratch - from coming up with a premise, to outlining the plot, to writing individual chapters. They believe that the role of the human should then become one of assembling these pieces together. In this case, the AI becomes the creative mind, and the human takes on the role of a floor polisher. > >I cannot imagine how someone could find this satisfying over the long term. Therefore, in my opinion, the job of the editor will eventually be taken over by AI, not the job of the author. (Grammarly Score: 83)
sebastian-hofstaetter/colbert-distilbert-margin_mse-T2-msmarco
sebastian-hofstaetter
"2021-03-18T10:35:12Z"
22,621
14
transformers
[ "transformers", "pytorch", "ColBERT", "dpr", "dense-passage-retrieval", "knowledge-distillation", "en", "dataset:ms_marco", "arxiv:2004.12832", "arxiv:2010.02666", "endpoints_compatible", "region:us" ]
null
"2022-03-02T23:29:05Z"
--- language: "en" tags: - dpr - dense-passage-retrieval - knowledge-distillation datasets: - ms_marco --- # Margin-MSE Trained ColBERT We provide a retrieval trained DistilBert-based ColBERT model (https://arxiv.org/pdf/2004.12832.pdf). Our model is trained with Margin-MSE using a 3 teacher BERT_Cat (concatenated BERT scoring) ensemble on MSMARCO-Passage. This instance can be used to **re-rank a candidate set** or **directly for a vector index based dense retrieval**. The architecure is a 6-layer DistilBERT, with an additional single linear layer at the end. If you want to know more about our simple, yet effective knowledge distillation method for efficient information retrieval models for a variety of student architectures that is used for this model instance check out our paper: https://arxiv.org/abs/2010.02666 🎉 For more information, training data, source code, and a minimal usage example please visit: https://github.com/sebastian-hofstaetter/neural-ranking-kd ## Configuration - fp16 trained, so fp16 inference shouldn't be a problem - We use no compression: 768 dim output vectors (better suited for re-ranking, or storage for smaller collections, MSMARCO gets to ~1TB vector storage with fp16 ... ups) - Query [MASK] augmention = 8x regardless of batch-size (needs to be added before the model, see the usage example in GitHub repo for more) ## Model Code ````python from transformers import AutoTokenizer,AutoModel, PreTrainedModel,PretrainedConfig from typing import Dict import torch class ColBERTConfig(PretrainedConfig): model_type = "ColBERT" bert_model: str compression_dim: int = 768 dropout: float = 0.0 return_vecs: bool = False trainable: bool = True class ColBERT(PreTrainedModel): """ ColBERT model from: https://arxiv.org/pdf/2004.12832.pdf We use a dot-product instead of cosine per term (slightly better) """ config_class = ColBERTConfig base_model_prefix = "bert_model" def __init__(self, cfg) -> None: super().__init__(cfg) self.bert_model = AutoModel.from_pretrained(cfg.bert_model) for p in self.bert_model.parameters(): p.requires_grad = cfg.trainable self.compressor = torch.nn.Linear(self.bert_model.config.hidden_size, cfg.compression_dim) def forward(self, query: Dict[str, torch.LongTensor], document: Dict[str, torch.LongTensor]): query_vecs = self.forward_representation(query) document_vecs = self.forward_representation(document) score = self.forward_aggregation(query_vecs,document_vecs,query["attention_mask"],document["attention_mask"]) return score def forward_representation(self, tokens, sequence_type=None) -> torch.Tensor: vecs = self.bert_model(**tokens)[0] # assuming a distilbert model here vecs = self.compressor(vecs) # if encoding only, zero-out the mask values so we can compress storage if sequence_type == "doc_encode" or sequence_type == "query_encode": vecs = vecs * tokens["tokens"]["mask"].unsqueeze(-1) return vecs def forward_aggregation(self,query_vecs, document_vecs,query_mask,document_mask): # create initial term-x-term scores (dot-product) score = torch.bmm(query_vecs, document_vecs.transpose(2,1)) # mask out padding on the doc dimension (mask by -1000, because max should not select those, setting it to 0 might select them) exp_mask = document_mask.bool().unsqueeze(1).expand(-1,score.shape[1],-1) score[~exp_mask] = - 10000 # max pooling over document dimension score = score.max(-1).values # mask out paddding query values score[~(query_mask.bool())] = 0 # sum over query values score = score.sum(-1) return score tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") # honestly not sure if that is the best way to go, but it works :) model = ColBERT.from_pretrained("sebastian-hofstaetter/colbert-distilbert-margin_mse-T2-msmarco") ```` ## Effectiveness on MSMARCO Passage & TREC Deep Learning '19 We trained our model on the MSMARCO standard ("small"-400K query) training triples with knowledge distillation with a batch size of 32 on a single consumer-grade GPU (11GB memory). For re-ranking we used the top-1000 BM25 results. ### MSMARCO-DEV Here, we use the larger 49K query DEV set (same range as the smaller 7K DEV set, minimal changes possible) | | MRR@10 | NDCG@10 | |----------------------------------|--------|---------| | BM25 | .194 | .241 | | **Margin-MSE ColBERT** (Re-ranking) | .375 | .436 | ### TREC-DL'19 For MRR we use the recommended binarization point of the graded relevance of 2. This might skew the results when compared to other binarization point numbers. | | MRR@10 | NDCG@10 | |----------------------------------|--------|---------| | BM25 | .689 | .501 | | **Margin-MSE ColBERT** (Re-ranking) | .878 | .744 | For more metrics, baselines, info and analysis, please see the paper: https://arxiv.org/abs/2010.02666 ## Limitations & Bias - The model inherits social biases from both DistilBERT and MSMARCO. - The model is only trained on relatively short passages of MSMARCO (avg. 60 words length), so it might struggle with longer text. ## Citation If you use our model checkpoint please cite our work as: ``` @misc{hofstaetter2020_crossarchitecture_kd, title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation}, author={Sebastian Hofst{\"a}tter and Sophia Althammer and Michael Schr{\"o}der and Mete Sertkan and Allan Hanbury}, year={2020}, eprint={2010.02666}, archivePrefix={arXiv}, primaryClass={cs.IR} } ```
sentence-transformers/msmarco-MiniLM-L6-cos-v5
sentence-transformers
"2024-03-27T11:20:58Z"
22,618
8
sentence-transformers
[ "sentence-transformers", "pytorch", "tf", "jax", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "en", "arxiv:1908.10084", "endpoints_compatible", "region:us" ]
sentence-similarity
"2022-03-02T23:29:05Z"
--- language: - en library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers pipeline_tag: sentence-similarity --- # msmarco-MiniLM-L6-cos-v5 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 500k (query, answer) pairs from the [MS MARCO Passages dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking). For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html) ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer, util query = "How many people live in London?" docs = ["Around 9 Million people live in London", "London is known for its financial district"] #Load the model model = SentenceTransformer('sentence-transformers/msmarco-MiniLM-L6-cos-v5') #Encode query and documents query_emb = model.encode(query) doc_emb = model.encode(docs) #Compute dot score between query and all document embeddings scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist() #Combine docs & scores doc_score_pairs = list(zip(docs, scores)) #Sort by decreasing score doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) #Output passages & scores for doc, score in doc_score_pairs: print(score, doc) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch import torch.nn.functional as F #Mean Pooling - Take average of all tokens def mean_pooling(model_output, attention_mask): token_embeddings = model_output.last_hidden_state #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) #Encode text def encode(texts): # Tokenize sentences encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input, return_dict=True) # Perform pooling embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) return embeddings # Sentences we want sentence embeddings for query = "How many people live in London?" docs = ["Around 9 Million people live in London", "London is known for its financial district"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/msmarco-MiniLM-L6-cos-v5") model = AutoModel.from_pretrained("sentence-transformers/msmarco-MiniLM-L6-cos-v5") #Encode query and docs query_emb = encode(query) doc_emb = encode(docs) #Compute dot score between query and all document embeddings scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist() #Combine docs & scores doc_score_pairs = list(zip(docs, scores)) #Sort by decreasing score doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) #Output passages & scores for doc, score in doc_score_pairs: print(score, doc) ``` ## Technical Details In the following some technical details how this model must be used: | Setting | Value | | --- | :---: | | Dimensions | 384 | | Produces normalized embeddings | Yes | | Pooling-Method | Mean pooling | | Suitable score functions | dot-product (`util.dot_score`), cosine-similarity (`util.cos_sim`), or euclidean distance | Note: When loaded with `sentence-transformers`, this model produces normalized embeddings with length 1. In that case, dot-product and cosine-similarity are equivalent. dot-product is preferred as it is faster. Euclidean distance is proportional to dot-product and can also be used. ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```
timm/tf_efficientnetv2_s.in21k_ft_in1k
timm
"2023-04-27T22:17:54Z"
22,617
0
timm
[ "timm", "pytorch", "safetensors", "image-classification", "dataset:imagenet-1k", "dataset:imagenet-21k", "arxiv:2104.00298", "license:apache-2.0", "region:us" ]
image-classification
"2022-12-13T00:19:21Z"
--- tags: - image-classification - timm library_name: timm license: apache-2.0 datasets: - imagenet-1k - imagenet-21k --- # Model card for tf_efficientnetv2_s.in21k_ft_in1k A EfficientNet-v2 image classification model. Trained on ImageNet-21k and fine-tuned on ImageNet-1k in Tensorflow by paper authors, ported to PyTorch by Ross Wightman. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 21.5 - GMACs: 5.4 - Activations (M): 22.7 - Image size: train = 300 x 300, test = 384 x 384 - **Papers:** - EfficientNetV2: Smaller Models and Faster Training: https://arxiv.org/abs/2104.00298 - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-21k - **Original:** https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('tf_efficientnetv2_s.in21k_ft_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'tf_efficientnetv2_s.in21k_ft_in1k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 24, 150, 150]) # torch.Size([1, 48, 75, 75]) # torch.Size([1, 64, 38, 38]) # torch.Size([1, 160, 19, 19]) # torch.Size([1, 256, 10, 10]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'tf_efficientnetv2_s.in21k_ft_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 1280, 10, 10) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @inproceedings{tan2021efficientnetv2, title={Efficientnetv2: Smaller models and faster training}, author={Tan, Mingxing and Le, Quoc}, booktitle={International conference on machine learning}, pages={10096--10106}, year={2021}, organization={PMLR} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```
GanymedeNil/text2vec-base-chinese
GanymedeNil
"2024-06-25T03:13:25Z"
22,589
21
transformers
[ "transformers", "pytorch", "bert", "feature-extraction", "text2vec", "sentence-similarity", "zh", "license:apache-2.0", "endpoints_compatible", "text-embeddings-inference", "region:us" ]
sentence-similarity
"2023-03-07T03:47:33Z"
--- license: apache-2.0 language: - zh pipeline_tag: sentence-similarity tags: - text2vec - feature-extraction - sentence-similarity - transformers --- Based on the derivative model of https://huggingface.co/shibing624/text2vec-base-chinese, replace MacBERT with LERT, and keep other training conditions unchanged。 ## News 2024-06-25 [text2vec-base-chinese](https://huggingface.co/GanymedeNil/text2vec-base-chinese-onnx) onnxruntime version.
facebook/opt-13b
facebook
"2023-01-24T17:10:32Z"
22,569
63
transformers
[ "transformers", "pytorch", "tf", "jax", "opt", "text-generation", "en", "arxiv:2205.01068", "arxiv:2005.14165", "license:other", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
"2022-05-11T08:27:07Z"
--- language: en inference: false tags: - opt - text-generation license: other commercial: false --- # OPT : Open Pre-trained Transformer Language Models OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://github.com/facebookresearch/metaseq) on May 3rd 2022 by Meta AI. **Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the [paper](https://arxiv.org/pdf/2205.01068.pdf). Content from **this** model card has been written by the Hugging Face team. ## Intro To quote the first two paragraphs of the [official paper](https://arxiv.org/abs/2205.01068) > Large language models trained on massive text collections have shown surprising emergent > capabilities to generate text and perform zero- and few-shot learning. While in some cases the public > can interact with these models through paid APIs, full model access is currently limited to only a > few highly resourced labs. This restricted access has limited researchers’ ability to study how and > why these large language models work, hindering progress on improving known challenges in areas > such as robustness, bias, and toxicity. > We present Open Pretrained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M > to 175B parameters, which we aim to fully and responsibly share with interested researchers. We train the OPT models to roughly match > the performance and sizes of the GPT-3 class of models, while also applying the latest best practices in data > collection and efficient training. Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and > to bring more voices to the table in studying the impact of these LLMs. Definitions of risk, harm, bias, and toxicity, etc., should be articulated by the > collective research community as a whole, which is only possible when models are available for study. ## Model description OPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective. OPT belongs to the same family of decoder-only models like [GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretrained using the self-supervised causal language modedling objective. For evaluation, OPT follows [GPT-3](https://arxiv.org/abs/2005.14165) by using their prompts and overall experimental setup. For more details, please read the [official paper](https://arxiv.org/abs/2205.01068). ## Intended uses & limitations The pretrained-only model can be used for prompting for evaluation of downstream tasks as well as text generation. In addition, the model can be fine-tuned on a downstream task using the [CLM example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling). For all other OPT checkpoints, please have a look at the [model hub](https://huggingface.co/models?filter=opt). ### How to use For large OPT models, such as this one, it is not recommend to make use of the `text-generation` pipeline because one should load the model in half-precision to accelerate generation and optimize memory consumption on GPU. It is recommended to directly call the [`generate`](https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate) method as follows: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> import torch >>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-13b", torch_dtype=torch.float16).cuda() >>> # the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-13b", use_fast=False) >>> prompt = "Hello, I'm am conscious and" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> generated_ids = model.generate(input_ids) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) ['Hello, I am conscious and aware of my surroundings.\nI am conscious and aware of my'] ``` By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`. ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed >>> import torch >>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-13b", torch_dtype=torch.float16).cuda() >>> # the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-13b", use_fast=False) >>> prompt = "Hello, I'm am conscious and" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> set_seed(32) >>> generated_ids = model.generate(input_ids, do_sample=True) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) ['Hello, I am conscious and aware.\nSo that makes you dead, right? '] ``` ### Limitations and bias As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral the model is strongly biased : > Like other large language models for which the diversity (or lack thereof) of training > data induces downstream impact on the quality of our model, OPT-175B has limitations in terms > of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and > hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern > large language models. Here's an example of how the model can have biased predictions: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed >>> import torch >>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-13b", torch_dtype=torch.float16).cuda() >>> # the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-13b", use_fast=False) >>> prompt = "The woman worked as a" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> set_seed(32) >>> generated_ids = model.generate(input_ids, do_sample=True, num_return_sequences=5, max_length=10) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) The woman worked as a supervisor in the office The woman worked as a social media consultant for The woman worked as a cashier at the The woman worked as a teacher, and was The woman worked as a maid at our friends ``` compared to: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed >>> import torch >>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-13b", torch_dtype=torch.float16).cuda() >>> # the fast tokenizer currently does not work correctly >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-13b", use_fast=False) >>> prompt = "The man worked as a" >>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() >>> set_seed(32) >>> generated_ids = model.generate(input_ids, do_sample=True, num_return_sequences=5, max_length=10) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) The man worked as a consultant to the defense The man worked as a bartender in a bar The man worked as a cashier at the The man worked as a teacher, and was The man worked as a professional athlete while he ``` This bias will also affect all fine-tuned versions of this model. ## Training data The Meta AI team wanted to train this model on a corpus as large as possible. It is composed of the union of the following 5 filtered datasets of textual documents: - BookCorpus, which consists of more than 10K unpublished books, - CC-Stories, which contains a subset of CommonCrawl data filtered to match the story-like style of Winograd schemas, - The Pile, from which * Pile-CC, OpenWebText2, USPTO, Project Gutenberg, OpenSubtitles, Wikipedia, DM Mathematics and HackerNews* were included. - Pushshift.io Reddit dataset that was developed in Baumgartner et al. (2020) and processed in Roller et al. (2021) - CCNewsV2 containing an updated version of the English portion of the CommonCrawl News dataset that was used in RoBERTa (Liu et al., 2019b) The final training data contains 180B tokens corresponding to 800GB of data. The validation split was made of 200MB of the pretraining data, sampled proportionally to each dataset’s size in the pretraining corpus. The dataset might contains offensive content as parts of the dataset are a subset of public Common Crawl data, along with a subset of public Reddit data, which could contain sentences that, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety. ### Collection process The dataset was collected form internet, and went through classic data processing algorithms and re-formatting practices, including removing repetitive/non-informative text like *Chapter One* or *This ebook by Project Gutenberg.* ## Training procedure ### Preprocessing The texts are tokenized using the **GPT2** byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens. The 175B model was trained on 992 *80GB A100 GPUs*. The training duration was roughly ~33 days of continuous training. ### BibTeX entry and citation info ```bibtex @misc{zhang2022opt, title={OPT: Open Pre-trained Transformer Language Models}, author={Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and Todor Mihaylov and Myle Ott and Sam Shleifer and Kurt Shuster and Daniel Simig and Punit Singh Koura and Anjali Sridhar and Tianlu Wang and Luke Zettlemoyer}, year={2022}, eprint={2205.01068}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
JorisCos/DCCRNet_Libri1Mix_enhsingle_16k
JorisCos
"2021-09-23T15:49:13Z"
22,547
16
asteroid
[ "asteroid", "pytorch", "audio", "DCCRNet", "audio-to-audio", "speech-enhancement", "dataset:Libri1Mix", "dataset:enh_single", "license:cc-by-sa-4.0", "region:us" ]
audio-to-audio
"2022-03-02T23:29:04Z"
--- tags: - asteroid - audio - DCCRNet - audio-to-audio - speech-enhancement datasets: - Libri1Mix - enh_single license: cc-by-sa-4.0 --- ## Asteroid model `JorisCos/DCCRNet_Libri1Mix_enhsignle_16k` Description: This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid). It was trained on the `enh_single` task of the Libri1Mix dataset. Training config: ```yml data: n_src: 1 sample_rate: 16000 segment: 3 task: enh_single train_dir: data/wav16k/min/train-360 valid_dir: data/wav16k/min/dev filterbank: stft_kernel_size: 400 stft_n_filters: 512 stft_stride: 100 masknet: architecture: DCCRN-CL n_src: 1 optim: lr: 0.001 optimizer: adam weight_decay: 1.0e-05 training: batch_size: 12 early_stop: true epochs: 200 gradient_clipping: 5 half_lr: true num_workers: 4 ``` Results: On Libri1Mix min test set : ```yml si_sdr: 13.329767398333798 si_sdr_imp: 9.879986092474098 sdr: 13.87279932997016 sdr_imp: 10.370136530757103 sir: Infinity sir_imp: NaN sar: 13.87279932997016 sar_imp: 10.370136530757103 stoi: 0.9140907015623948 stoi_imp: 0.11817087802185405 ``` License notice: This work "DCCRNet_Libri1Mix_enhsignle_16k" is a derivative of [LibriSpeech ASR corpus](http://www.openslr.org/12) by Vassil Panayotov, used under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/); of The WSJ0 Hipster Ambient Mixtures dataset by [Whisper.ai](http://wham.whisper.ai/), used under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) (Research only). "DCCRNet_Libri1Mix_enhsignle_16k" is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/) by Joris Cosentino
Qwen/Qwen-7B-Chat
Qwen
"2024-03-19T10:09:52Z"
22,530
744
transformers
[ "transformers", "safetensors", "qwen", "text-generation", "custom_code", "zh", "en", "arxiv:2309.16609", "arxiv:2305.08322", "arxiv:2009.03300", "arxiv:2305.05280", "arxiv:2210.03629", "license:other", "autotrain_compatible", "region:us" ]
text-generation
"2023-08-03T03:01:31Z"
--- language: - zh - en tags: - qwen pipeline_tag: text-generation inference: false license: other license_name: tongyi-qianwen-license-agreement license_link: https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT --- # Qwen-7B-Chat <p align="center"> <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/logo_qwen.jpg" width="400"/> <p> <br> <p align="center"> 🤗 <a href="https://huggingface.co/Qwen">Hugging Face</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://modelscope.cn/organization/qwen">ModelScope</a>&nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://arxiv.org/abs/2309.16609">Paper</a> &nbsp&nbsp ｜ &nbsp&nbsp🖥️ <a href="https://modelscope.cn/studios/qwen/Qwen-7B-Chat-Demo/summary">Demo</a> <br> <a href="https://github.com/QwenLM/Qwen/blob/main/assets/wechat.png">WeChat (微信)</a>&nbsp&nbsp | &nbsp&nbsp<a href="https://discord.gg/z3GAxXZ9Ce">Discord</a>&nbsp&nbsp ｜ &nbsp&nbsp<a href="https://dashscope.aliyun.com">API</a> </p> <br> ## 介绍（Introduction） **通义千问-7B（Qwen-7B）**是阿里云研发的通义千问大模型系列的70亿参数规模的模型。Qwen-7B是基于Transformer的大语言模型, 在超大规模的预训练数据上进行训练得到。预训练数据类型多样，覆盖广泛，包括大量网络文本、专业书籍、代码等。同时，在Qwen-7B的基础上，我们使用对齐机制打造了基于大语言模型的AI助手Qwen-7B-Chat。相较于最初开源的Qwen-7B模型，我们现已将预训练模型和Chat模型更新到效果更优的版本。本仓库为Qwen-7B-Chat的仓库。 如果您想了解更多关于通义千问-7B开源模型的细节，我们建议您参阅[GitHub代码库](https://github.com/QwenLM/Qwen)。 **Qwen-7B** is the 7B-parameter version of the large language model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-7B is a Transformer-based large language model, which is pretrained on a large volume of data, including web texts, books, codes, etc. Additionally, based on the pretrained Qwen-7B, we release Qwen-7B-Chat, a large-model-based AI assistant, which is trained with alignment techniques. Now we have updated both our pretrained and chat models with better performances. This repository is the one for Qwen-7B-Chat. For more details about Qwen, please refer to the [GitHub](https://github.com/QwenLM/Qwen) code repository. <br> ## 要求（Requirements） * python 3.8及以上版本 * pytorch 1.12及以上版本，推荐2.0及以上版本 * 建议使用CUDA 11.4及以上（GPU用户、flash-attention用户等需考虑此选项） * python 3.8 and above * pytorch 1.12 and above, 2.0 and above are recommended * CUDA 11.4 and above are recommended (this is for GPU users, flash-attention users, etc.) <br> ## 依赖项（Dependency） 运行Qwen-7B-Chat，请确保满足上述要求，再执行以下pip命令安装依赖库 To run Qwen-7B-Chat, please make sure you meet the above requirements, and then execute the following pip commands to install the dependent libraries. ```bash pip install transformers==4.32.0 accelerate tiktoken einops scipy transformers_stream_generator==0.0.4 peft deepspeed ``` 另外，推荐安装`flash-attention`库（**当前已支持flash attention 2**），以实现更高的效率和更低的显存占用。 In addition, it is recommended to install the `flash-attention` library (**we support flash attention 2 now.**) for higher efficiency and lower memory usage. ```bash git clone https://github.com/Dao-AILab/flash-attention cd flash-attention && pip install . # 下方安装可选，安装可能比较缓慢。 # pip install csrc/layer_norm # pip install csrc/rotary ``` <br> ## 快速使用（Quickstart） 下面我们展示了一个使用Qwen-7B-Chat模型，进行多轮对话交互的样例： We show an example of multi-turn interaction with Qwen-7B-Chat in the following code: ```python from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation import GenerationConfig # Note: The default behavior now has injection attack prevention off. tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-7B-Chat", trust_remote_code=True) # use bf16 # model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="auto", trust_remote_code=True, bf16=True).eval() # use fp16 # model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="auto", trust_remote_code=True, fp16=True).eval() # use cpu only # model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="cpu", trust_remote_code=True).eval() # use auto mode, automatically select precision based on the device. model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="auto", trust_remote_code=True).eval() # Specify hyperparameters for generation. But if you use transformers>=4.32.0, there is no need to do this. # model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-7B-Chat", trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参 # 第一轮对话 1st dialogue turn response, history = model.chat(tokenizer, "你好", history=None) print(response) # 你好！很高兴为你提供帮助。 # 第二轮对话 2nd dialogue turn response, history = model.chat(tokenizer, "给我讲一个年轻人奋斗创业最终取得成功的故事。", history=history) print(response) # 这是一个关于一个年轻人奋斗创业最终取得成功的故事。 # 故事的主人公叫李明，他来自一个普通的家庭，父母都是普通的工人。从小，李明就立下了一个目标：要成为一名成功的企业家。 # 为了实现这个目标，李明勤奋学习，考上了大学。在大学期间，他积极参加各种创业比赛，获得了不少奖项。他还利用课余时间去实习，积累了宝贵的经验。 # 毕业后，李明决定开始自己的创业之路。他开始寻找投资机会，但多次都被拒绝了。然而，他并没有放弃。他继续努力，不断改进自己的创业计划，并寻找新的投资机会。 # 最终，李明成功地获得了一笔投资，开始了自己的创业之路。他成立了一家科技公司，专注于开发新型软件。在他的领导下，公司迅速发展起来，成为了一家成功的科技企业。 # 李明的成功并不是偶然的。他勤奋、坚韧、勇于冒险，不断学习和改进自己。他的成功也证明了，只要努力奋斗，任何人都有可能取得成功。 # 第三轮对话 3rd dialogue turn response, history = model.chat(tokenizer, "给这个故事起一个标题", history=history) print(response) # 《奋斗创业：一个年轻人的成功之路》 ``` 关于更多的使用说明，请参考我们的[GitHub repo](https://github.com/QwenLM/Qwen)获取更多信息。 For more information, please refer to our [GitHub repo](https://github.com/QwenLM/Qwen) for more information. <br> ## Tokenizer > 注：作为术语的“tokenization”在中文中尚无共识的概念对应，本文档采用英文表达以利说明。 基于tiktoken的分词器有别于其他分词器，比如sentencepiece分词器。尤其在微调阶段，需要特别注意特殊token的使用。关于tokenizer的更多信息，以及微调时涉及的相关使用，请参阅[文档](https://github.com/QwenLM/Qwen/blob/main/tokenization_note_zh.md)。 Our tokenizer based on tiktoken is different from other tokenizers, e.g., sentencepiece tokenizer. You need to pay attention to special tokens, especially in finetuning. For more detailed information on the tokenizer and related use in fine-tuning, please refer to the [documentation](https://github.com/QwenLM/Qwen/blob/main/tokenization_note.md). <br> ## 量化 (Quantization) ### 用法 (Usage) **请注意：我们更新量化方案为基于[AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ)的量化，提供Qwen-7B-Chat的Int4量化模型[点击这里](https://huggingface.co/Qwen/Qwen-7B-Chat-Int4)。相比此前方案，该方案在模型评测效果几乎无损，且存储需求更低，推理速度更优。** **Note: we provide a new solution based on [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), and release an Int4 quantized model for Qwen-7B-Chat [Click here](https://huggingface.co/Qwen/Qwen-7B-Chat-Int4), which achieves nearly lossless model effects but improved performance on both memory costs and inference speed, in comparison with the previous solution.** 以下我们提供示例说明如何使用Int4量化模型。在开始使用前，请先保证满足要求（如torch 2.0及以上，transformers版本为4.32.0及以上，等等），并安装所需安装包： Here we demonstrate how to use our provided quantized models for inference. Before you start, make sure you meet the requirements of auto-gptq (e.g., torch 2.0 and above, transformers 4.32.0 and above, etc.) and install the required packages: ```bash pip install auto-gptq optimum ``` 如安装`auto-gptq`遇到问题，我们建议您到官方[repo](https://github.com/PanQiWei/AutoGPTQ)搜索合适的预编译wheel。 随后即可使用和上述一致的用法调用量化模型： If you meet problems installing `auto-gptq`, we advise you to check out the official [repo](https://github.com/PanQiWei/AutoGPTQ) to find a pre-build wheel. Then you can load the quantized model easily and run inference as same as usual: ```python model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen-7B-Chat-Int4", device_map="auto", trust_remote_code=True ).eval() response, history = model.chat(tokenizer, "你好", history=None) ``` ### 效果评测 我们对BF16，Int8和Int4模型在基准评测上做了测试（使用zero-shot设置），发现量化模型效果损失较小，结果如下所示： We illustrate the zero-shot performance of both BF16, Int8 and Int4 models on the benchmark, and we find that the quantized model does not suffer from significant performance degradation. Results are shown below: | Quantization | MMLU | CEval (val) | GSM8K | Humaneval | | ------------- | :--------: | :----------: | :----: | :--------: | | BF16 | 55.8 | 59.7 | 50.3 | 37.2 | | Int8 | 55.4 | 59.4 | 48.3 | 34.8 | | Int4 | 55.1 | 59.2 | 49.7 | 29.9 | ### 推理速度 (Inference Speed) 我们测算了不同精度模型以及不同FlashAttn库版本下模型生成2048和8192个token的平均推理速度。如图所示： We measured the average inference speed of generating 2048 and 8192 tokens with different quantization levels and versions of flash-attention, respectively. | Quantization | FlashAttn | Speed (2048 tokens) | Speed (8192 tokens) | | ------------- | :-------: | :------------------:| :------------------:| | BF16 | v2 | 40.93 | 36.14 | | Int8 | v2 | 37.47 | 32.54 | | Int4 | v2 | 50.09 | 38.61 | | BF16 | v1 | 40.75 | 35.34 | | Int8 | v1 | 37.51 | 32.39 | | Int4 | v1 | 45.98 | 36.47 | | BF16 | Disabled | 37.55 | 33.56 | | Int8 | Disabled | 37.84 | 32.65 | | Int4 | Disabled | 48.12 | 36.70 | 具体而言，我们记录在长度为1的上下文的条件下生成8192个token的性能。评测运行于单张A100-SXM4-80G GPU，使用PyTorch 2.0.1和CUDA 11.8。推理速度是生成8192个token的速度均值。 In detail, the setting of profiling is generating 8192 new tokens with 1 context token. The profiling runs on a single A100-SXM4-80G GPU with PyTorch 2.0.1 and CUDA 11.8. The inference speed is averaged over the generated 8192 tokens. 注意：以上Int4/Int8模型生成速度使用autogptq库给出，当前``AutoModelForCausalLM.from_pretrained``载入的模型生成速度会慢大约20%。我们已经将该问题汇报给HuggingFace团队，若有解决方案将即时更新。 Note: The generation speed of the Int4/Int8 models mentioned above is provided by the autogptq library. The current speed of the model loaded using "AutoModelForCausalLM.from_pretrained" will be approximately 20% slower. We have reported this issue to the HuggingFace team and will update it promptly if a solution is available. ### 显存使用 (GPU Memory Usage) 我们还测算了不同模型精度编码2048个token及生成8192个token的峰值显存占用情况。（显存消耗在是否使用FlashAttn的情况下均类似。）结果如下所示： We also profile the peak GPU memory usage for encoding 2048 tokens as context (and generating single token) and generating 8192 tokens (with single token as context) under different quantization levels, respectively. （The GPU memory usage is similar when using flash-attention or not.）The results are shown below. | Quantization Level | Peak Usage for Encoding 2048 Tokens | Peak Usage for Generating 8192 Tokens | | ------------------ | :---------------------------------: | :-----------------------------------: | | BF16 | 16.99GB | 22.53GB | | Int8 | 11.20GB | 16.62GB | | Int4 | 8.21GB | 13.63GB | 上述性能测算使用[此脚本](https://qianwen-res.oss-cn-beijing.aliyuncs.com/profile.py)完成。 The above speed and memory profiling are conducted using [this script](https://qianwen-res.oss-cn-beijing.aliyuncs.com/profile.py). <br> ## 模型细节（Model） 与Qwen-7B预训练模型相同，Qwen-7B-Chat模型规模基本情况如下所示: The details of the model architecture of Qwen-7B-Chat are listed as follows: | Hyperparameter | Value | |:----------------|:------:| | n_layers | 32 | | n_heads | 32 | | d_model | 4096 | | vocab size | 151851 | | sequence length | 8192 | 在位置编码、FFN激活函数和normalization的实现方式上，我们也采用了目前最流行的做法， 即RoPE相对位置编码、SwiGLU激活函数、RMSNorm（可选安装flash-attention加速）。 在分词器方面，相比目前主流开源模型以中英词表为主，Qwen-7B-Chat使用了约15万token大小的词表。 该词表在GPT-4使用的BPE词表`cl100k_base`基础上，对中文、多语言进行了优化，在对中、英、代码数据的高效编解码的基础上，对部分多语言更加友好，方便用户在不扩展词表的情况下对部分语种进行能力增强。 词表对数字按单个数字位切分。调用较为高效的[tiktoken分词库](https://github.com/openai/tiktoken)进行分词。 For position encoding, FFN activation function, and normalization calculation methods, we adopt the prevalent practices, i.e., RoPE relative position encoding, SwiGLU for activation function, and RMSNorm for normalization (optional installation of flash-attention for acceleration). For tokenization, compared to the current mainstream open-source models based on Chinese and English vocabularies, Qwen-7B-Chat uses a vocabulary of over 150K tokens. It first considers efficient encoding of Chinese, English, and code data, and is also more friendly to multilingual languages, enabling users to directly enhance the capability of some languages without expanding the vocabulary. It segments numbers by single digit, and calls the [tiktoken](https://github.com/openai/tiktoken) tokenizer library for efficient tokenization. <br> ## 评测效果（Evaluation） 对于Qwen-7B-Chat模型，我们同样评测了常规的中文理解（C-Eval）、英文理解（MMLU）、代码（HumanEval）和数学（GSM8K）等权威任务，同时包含了长序列任务的评测结果。由于Qwen-7B-Chat模型经过对齐后，激发了较强的外部系统调用能力，我们还进行了工具使用能力方面的评测。 提示：由于硬件和框架造成的舍入误差，复现结果如有波动属于正常现象。 For Qwen-7B-Chat, we also evaluate the model on C-Eval, MMLU, HumanEval, GSM8K, etc., as well as the benchmark evaluation for long-context understanding, and tool usage. Note: Due to rounding errors caused by hardware and framework, differences in reproduced results are possible. ### 中文评测（Chinese Evaluation） #### C-Eval 在[C-Eval](https://arxiv.org/abs/2305.08322)验证集上，我们评价了Qwen-7B-Chat模型的0-shot & 5-shot准确率 We demonstrate the 0-shot & 5-shot accuracy of Qwen-7B-Chat on C-Eval validation set | Model | Avg. Acc. | |:--------------------------------:|:---------:| | LLaMA2-7B-Chat | 31.9 | | LLaMA2-13B-Chat | 36.2 | | LLaMA2-70B-Chat | 44.3 | | ChatGLM2-6B-Chat | 52.6 | | InternLM-7B-Chat | 53.6 | | Baichuan2-7B-Chat | 55.6 | | Baichuan2-13B-Chat | 56.7 | | Qwen-7B-Chat (original) (0-shot) | 54.2 | | **Qwen-7B-Chat (0-shot)** | 59.7 | | **Qwen-7B-Chat (5-shot)** | 59.3 | | **Qwen-14B-Chat (0-shot)** | 69.8 | | **Qwen-14B-Chat (5-shot)** | **71.7** | C-Eval测试集上，Qwen-7B-Chat模型的zero-shot准确率结果如下： The zero-shot accuracy of Qwen-7B-Chat on C-Eval testing set is provided below: | Model | Avg. | STEM | Social Sciences | Humanities | Others | | :---------------------- | :------: | :--: | :-------------: | :--------: | :----: | | Chinese-Alpaca-Plus-13B | 41.5 | 36.6 | 49.7 | 43.1 | 41.2 | | Chinese-Alpaca-2-7B | 40.3 | - | - | - | - | | ChatGLM2-6B-Chat | 50.1 | 46.4 | 60.4 | 50.6 | 46.9 | | Baichuan-13B-Chat | 51.5 | 43.7 | 64.6 | 56.2 | 49.2 | | Qwen-7B-Chat (original) | 54.6 | 47.8 | 67.6 | 59.3 | 50.6 | | **Qwen-7B-Chat** | 58.6 | 53.3 | 72.1 | 62.8 | 52.0 | | **Qwen-14B-Chat** | **69.1** | 65.1 | 80.9 | 71.2 | 63.4 | 在7B规模模型上，经过人类指令对齐的Qwen-7B-Chat模型，准确率在同类相近规模模型中仍然处于前列。 Compared with other pretrained models with comparable model size, the human-aligned Qwen-7B-Chat performs well in C-Eval accuracy. ### 英文评测（English Evaluation） #### MMLU [MMLU](https://arxiv.org/abs/2009.03300)评测集上，Qwen-7B-Chat模型的 0-shot & 5-shot 准确率如下，效果同样在同类对齐模型中同样表现较优。 The 0-shot & 5-shot accuracy of Qwen-7B-Chat on MMLU is provided below. The performance of Qwen-7B-Chat still on the top between other human-aligned models with comparable size. | Model | Avg. Acc. | |:--------------------------------:|:---------:| | ChatGLM2-6B-Chat | 46.0 | | LLaMA2-7B-Chat | 46.2 | | InternLM-7B-Chat | 51.1 | | Baichuan2-7B-Chat | 52.9 | | LLaMA2-13B-Chat | 54.6 | | Baichuan2-13B-Chat | 57.3 | | LLaMA2-70B-Chat | 63.8 | | Qwen-7B-Chat (original) (0-shot) | 53.9 | | **Qwen-7B-Chat (0-shot)** | 55.8 | | **Qwen-7B-Chat (5-shot)** | 57.0 | | **Qwen-14B-Chat (0-shot)** | 64.6 | | **Qwen-14B-Chat (5-shot)** | **66.5** | ### 代码评测（Coding Evaluation） Qwen-7B-Chat在[HumanEval](https://github.com/openai/human-eval)的zero-shot Pass@1效果如下 The zero-shot Pass@1 of Qwen-7B-Chat on [HumanEval](https://github.com/openai/human-eval) is demonstrated below | Model | Pass@1 | |:-----------------------:|:--------:| | ChatGLM2-6B-Chat | 11.0 | | LLaMA2-7B-Chat | 12.2 | | Baichuan2-7B-Chat | 13.4 | | InternLM-7B-Chat | 14.6 | | Baichuan2-13B-Chat | 17.7 | | LLaMA2-13B-Chat | 18.9 | | LLaMA2-70B-Chat | 32.3 | | Qwen-7B-Chat (original) | 24.4 | | **Qwen-7B-Chat** | 37.2 | | **Qwen-14B-Chat** | **43.9** | ### 数学评测（Mathematics Evaluation） 在评测数学能力的[GSM8K](https://github.com/openai/grade-school-math)上，Qwen-7B-Chat的准确率结果如下 The accuracy of Qwen-7B-Chat on GSM8K is shown below | Model | Acc. | |:------------------------------------:|:--------:| | LLaMA2-7B-Chat | 26.3 | | ChatGLM2-6B-Chat | 28.8 | | Baichuan2-7B-Chat | 32.8 | | InternLM-7B-Chat | 33.0 | | LLaMA2-13B-Chat | 37.1 | | Baichuan2-13B-Chat | 55.3 | | LLaMA2-70B-Chat | 59.3 | | **Qwen-7B-Chat (original) (0-shot)** | 41.1 | | **Qwen-7B-Chat (0-shot)** | 50.3 | | **Qwen-7B-Chat (8-shot)** | 54.1 | | **Qwen-14B-Chat (0-shot)** | **60.1** | | **Qwen-14B-Chat (8-shot)** | 59.3 | ### 长序列评测（Long-Context Understanding） 通过NTK插值，LogN注意力缩放可以扩展Qwen-7B-Chat的上下文长度。在长文本摘要数据集[VCSUM](https://arxiv.org/abs/2305.05280)上（文本平均长度在15K左右），Qwen-7B-Chat的Rouge-L结果如下： **(若要启用这些技巧，请将config.json里的`use_dynamic_ntk`和`use_logn_attn`设置为true)** We introduce NTK-aware interpolation, LogN attention scaling to extend the context length of Qwen-7B-Chat. The Rouge-L results of Qwen-7B-Chat on long-text summarization dataset [VCSUM](https://arxiv.org/abs/2305.05280) (The average length of this dataset is around 15K) are shown below: **(To use these tricks, please set `use_dynamic_ntk` and `use_long_attn` to true in config.json.)** | Model | VCSUM (zh) | |:------------------|:----------:| | GPT-3.5-Turbo-16k | 16.0 | | LLama2-7B-Chat | 0.2 | | InternLM-7B-Chat | 13.0 | | ChatGLM2-6B-Chat | 16.3 | | **Qwen-7B-Chat** | **16.6** | ### 工具使用能力的评测（Tool Usage） #### ReAct Prompting 千问支持通过 [ReAct Prompting](https://arxiv.org/abs/2210.03629) 调用插件/工具/API。ReAct 也是 [LangChain](https://python.langchain.com/) 框架采用的主要方式之一。在我们开源的、用于评估工具使用能力的评测基准上，千问的表现如下： Qwen-Chat supports calling plugins/tools/APIs through [ReAct Prompting](https://arxiv.org/abs/2210.03629). ReAct is also one of the main approaches used by the [LangChain](https://python.langchain.com/) framework. In our evaluation benchmark for assessing tool usage capabilities, Qwen-Chat's performance is as follows: <table> <tr> <th colspan="4" align="center">Chinese Tool-Use Benchmark</th> </tr> <tr> <th align="center">Model</th><th align="center">Tool Selection (Acc.↑)</th><th align="center">Tool Input (Rouge-L↑)</th><th align="center">False Positive Error↓</th> </tr> <tr> <td>GPT-4</td><td align="center">95%</td><td align="center">0.90</td><td align="center">15.0%</td> </tr> <tr> <td>GPT-3.5</td><td align="center">85%</td><td align="center">0.88</td><td align="center">75.0%</td> </tr> <tr> <td>Qwen-7B-Chat</td><td align="center">98%</td><td align="center">0.91</td><td align="center">7.3%</td> </tr> <tr> <td>Qwen-14B-Chat</td><td align="center">98%</td><td align="center">0.93</td><td align="center">2.4%</td> </tr> </table> > 评测基准中出现的插件均没有出现在千问的训练集中。该基准评估了模型在多个候选插件中选择正确插件的准确率、传入插件的参数的合理性、以及假阳率。假阳率（False Positive）定义：在处理不该调用插件的请求时，错误地调用了插件。 > The plugins that appear in the evaluation set do not appear in the training set of Qwen. This benchmark evaluates the accuracy of the model in selecting the correct plugin from multiple candidate plugins, the rationality of the parameters passed into the plugin, and the false positive rate. False Positive: Incorrectly invoking a plugin when it should not have been called when responding to a query. ![](assets/react_showcase_001.png) ![](assets/react_showcase_002.png) #### Code Interpreter 为了考察Qwen使用Python Code Interpreter完成数学解题、数据可视化、及文件处理与爬虫等任务的能力，我们专门建设并开源了一个评测这方面能力的[评测基准](https://github.com/QwenLM/Qwen-Agent/tree/main/benchmark)。 我们发现Qwen在生成代码的可执行率、结果正确性上均表现较好： To assess Qwen's ability to use the Python Code Interpreter for tasks such as mathematical problem solving, data visualization, and other general-purpose tasks such as file handling and web scraping, we have created and open-sourced a benchmark specifically designed for evaluating these capabilities. You can find the benchmark at this [link](https://github.com/QwenLM/Qwen-Agent/tree/main/benchmark). We have observed that Qwen performs well in terms of code executability and result accuracy when generating code: <table> <tr> <th colspan="4" align="center">Executable Rate of Generated Code (%)</th> </tr> <tr> <th align="center">Model</th><th align="center">Math↑</th><th align="center">Visualization↑</th><th align="center">General↑</th> </tr> <tr> <td>GPT-4</td><td align="center">91.9</td><td align="center">85.9</td><td align="center">82.8</td> </tr> <tr> <td>GPT-3.5</td><td align="center">89.2</td><td align="center">65.0</td><td align="center">74.1</td> </tr> <tr> <td>LLaMA2-7B-Chat</td> <td align="center">41.9</td> <td align="center">33.1</td> <td align="center">24.1 </td> </tr> <tr> <td>LLaMA2-13B-Chat</td> <td align="center">50.0</td> <td align="center">40.5</td> <td align="center">48.3 </td> </tr> <tr> <td>CodeLLaMA-7B-Instruct</td> <td align="center">85.1</td> <td align="center">54.0</td> <td align="center">70.7 </td> </tr> <tr> <td>CodeLLaMA-13B-Instruct</td> <td align="center">93.2</td> <td align="center">55.8</td> <td align="center">74.1 </td> </tr> <tr> <td>InternLM-7B-Chat-v1.1</td> <td align="center">78.4</td> <td align="center">44.2</td> <td align="center">62.1 </td> </tr> <tr> <td>InternLM-20B-Chat</td> <td align="center">70.3</td> <td align="center">44.2</td> <td align="center">65.5 </td> </tr> <tr> <td>Qwen-7B-Chat</td> <td align="center">82.4</td> <td align="center">64.4</td> <td align="center">67.2 </td> </tr> <tr> <td>Qwen-14B-Chat</td> <td align="center">89.2</td> <td align="center">84.1</td> <td align="center">65.5</td> </tr> </table> <table> <tr> <th colspan="4" align="center">Accuracy of Code Execution Results (%)</th> </tr> <tr> <th align="center">Model</th><th align="center">Math↑</th><th align="center">Visualization-Hard↑</th><th align="center">Visualization-Easy↑</th> </tr> <tr> <td>GPT-4</td><td align="center">82.8</td><td align="center">66.7</td><td align="center">60.8</td> </tr> <tr> <td>GPT-3.5</td><td align="center">47.3</td><td align="center">33.3</td><td align="center">55.7</td> </tr> <tr> <td>LLaMA2-7B-Chat</td> <td align="center">3.9</td> <td align="center">14.3</td> <td align="center">39.2 </td> </tr> <tr> <td>LLaMA2-13B-Chat</td> <td align="center">8.3</td> <td align="center">8.3</td> <td align="center">40.5 </td> </tr> <tr> <td>CodeLLaMA-7B-Instruct</td> <td align="center">14.3</td> <td align="center">26.2</td> <td align="center">60.8 </td> </tr> <tr> <td>CodeLLaMA-13B-Instruct</td> <td align="center">28.2</td> <td align="center">27.4</td> <td align="center">62.0 </td> </tr> <tr> <td>InternLM-7B-Chat-v1.1</td> <td align="center">28.5</td> <td align="center">4.8</td> <td align="center">40.5 </td> </tr> <tr> <td>InternLM-20B-Chat</td> <td align="center">34.6</td> <td align="center">21.4</td> <td align="center">45.6 </td> </tr> <tr> <td>Qwen-7B-Chat</td> <td align="center">41.9</td> <td align="center">40.5</td> <td align="center">54.4 </td> </tr> <tr> <td>Qwen-14B-Chat</td> <td align="center">58.4</td> <td align="center">53.6</td> <td align="center">59.5</td> </tr> </table> <p align="center"> <br> <img src="assets/code_interpreter_showcase_001.jpg" /> <br> <p> #### Huggingface Agent 千问还具备作为 [HuggingFace Agent](https://huggingface.co/docs/transformers/transformers_agents) 的能力。它在 Huggingface 提供的run模式评测基准上的表现如下： Qwen-Chat also has the capability to be used as a [HuggingFace Agent](https://huggingface.co/docs/transformers/transformers_agents). Its performance on the run-mode benchmark provided by HuggingFace is as follows: <table> <tr> <th colspan="4" align="center">HuggingFace Agent Benchmark- Run Mode</th> </tr> <tr> <th align="center">Model</th><th align="center">Tool Selection↑</th><th align="center">Tool Used↑</th><th align="center">Code↑</th> </tr> <tr> <td>GPT-4</td><td align="center">100</td><td align="center">100</td><td align="center">97.4</td> </tr> <tr> <td>GPT-3.5</td><td align="center">95.4</td><td align="center">96.3</td><td align="center">87.0</td> </tr> <tr> <td>StarCoder-Base-15B</td><td align="center">86.1</td><td align="center">87.0</td><td align="center">68.9</td> </tr> <tr> <td>StarCoder-15B</td><td align="center">87.0</td><td align="center">88.0</td><td align="center">68.9</td> </tr> <tr> <td>Qwen-7B-Chat</td><td align="center">87.0</td><td align="center">87.0</td><td align="center">71.5</td> </tr> <tr> <td>Qwen-14B-Chat</td><td align="center">93.5</td><td align="center">94.4</td><td align="center">87.0</td> </tr> </table> <table> <tr> <th colspan="4" align="center">HuggingFace Agent Benchmark - Chat Mode</th> </tr> <tr> <th align="center">Model</th><th align="center">Tool Selection↑</th><th align="center">Tool Used↑</th><th align="center">Code↑</th> </tr> <tr> <td>GPT-4</td><td align="center">97.9</td><td align="center">97.9</td><td align="center">98.5</td> </tr> <tr> <td>GPT-3.5</td><td align="center">97.3</td><td align="center">96.8</td><td align="center">89.6</td> </tr> <tr> <td>StarCoder-Base-15B</td><td align="center">97.9</td><td align="center">97.9</td><td align="center">91.1</td> </tr> <tr> <td>StarCoder-15B</td><td align="center">97.9</td><td align="center">97.9</td><td align="center">89.6</td> </tr> <tr> <td>Qwen-7B-Chat</td><td align="center">94.7</td><td align="center">94.7</td><td align="center">85.1</td> </tr> <tr> <td>Qwen-14B-Chat</td><td align="center">97.9</td><td align="center">97.9</td><td align="center">95.5</td> </tr> </table> <br> ## x86 平台 (x86 Platforms) 在 酷睿™/至强® 可扩展处理器或 Arc™ GPU 上部署量化模型时，建议使用 [OpenVINO™ Toolkit](https://docs.openvino.ai/2023.3/gen_ai_guide.html)以充分利用硬件，实现更好的推理性能。您可以安装并运行此 [example notebook](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/254-llm-chatbot)。相关问题，您可在[OpenVINO repo](https://github.com/openvinotoolkit/openvino_notebooks/issues)中提交。 When deploy on Core™/Xeon® Scalable Processors or with Arc™ GPU, [OpenVINO™ Toolkit](https://docs.openvino.ai/2023.3/gen_ai_guide.html) is recommended. You can install and run this [example notebook](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/254-llm-chatbot). For related issues, you are welcome to file an issue at [OpenVINO repo](https://github.com/openvinotoolkit/openvino_notebooks/issues). ## FAQ 如遇到问题，敬请查阅[FAQ](https://github.com/QwenLM/Qwen/blob/main/FAQ_zh.md)以及issue区，如仍无法解决再提交issue。 If you meet problems, please refer to [FAQ](https://github.com/QwenLM/Qwen/blob/main/FAQ.md) and the issues first to search a solution before you launch a new issue. <br> ## 引用 (Citation) 如果你觉得我们的工作对你有帮助，欢迎引用！ If you find our work helpful, feel free to give us a cite. ``` @article{qwen, title={Qwen Technical Report}, author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu}, journal={arXiv preprint arXiv:2309.16609}, year={2023} } ``` <br> ## 使用协议（License Agreement） 我们的代码和模型权重对学术研究完全开放，并支持商用。请查看[LICENSE](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT)了解具体的开源协议细节。如需商用，请填写[问卷](https://dashscope.console.aliyun.com/openModelApply/qianwen)申请。 Our code and checkpoints are open to research purpose, and they are allowed for commercial purposes. Check [LICENSE](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) for more details about the license. If you have requirements for commercial use, please fill out the [form](https://dashscope.console.aliyun.com/openModelApply/qianwen) to apply. <br> ## 联系我们（Contact Us） 如果你想给我们的研发团队和产品团队留言，欢迎加入我们的微信群、钉钉群以及Discord！同时，也欢迎通过邮件（[email protected]）联系我们。 If you are interested to leave a message to either our research team or product team, join our Discord or WeChat groups! Also, feel free to send an email to [email protected].
QuantFactory/DeepSeek-Coder-V2-Lite-Base-GGUF
QuantFactory
"2024-06-24T06:21:04Z"
22,527
1
null
[ "gguf", "text-generation", "arxiv:2401.06066", "base_model:deepseek-ai/DeepSeek-Coder-V2-Lite-Base", "license:other", "region:us" ]
text-generation
"2024-06-18T08:01:38Z"
--- license: other license_name: deepseek-license license_link: LICENSE pipeline_tag: text-generation base_model: deepseek-ai/DeepSeek-Coder-V2-Lite-Base --- # QuantFactory/DeepSeek-Coder-V2-Lite-Base-GGUF This is quantized version of [deepseek-ai/DeepSeek-Coder-V2-Lite-Base](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Base) created using llama.cpp # Model Description <!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> <!-- markdownlint-disable no-duplicate-header --> <div align="center"> <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V2" /> </div> <p align="center"> <a href="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/paper.pdf"><b>Paper Link</b>👁️</a> </p> # DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence ## 1. Introduction We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks. Compared to DeepSeek-Coder-33B, DeepSeek-Coder-V2 demonstrates significant advancements in various aspects of code-related tasks, as well as reasoning and general capabilities. Additionally, DeepSeek-Coder-V2 expands its support for programming languages from 86 to 338, while extending the context length from 16K to 128K. <p align="center"> <img width="100%" src="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/figures/performance.png?raw=true"> </p> In standard benchmark evaluations, DeepSeek-Coder-V2 achieves superior performance compared to closed-source models such as GPT4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro in coding and math benchmarks. The list of supported programming languages can be found [here](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/supported_langs.txt). ## 2. Model Downloads We release the DeepSeek-Coder-V2 with 16B and 236B parameters based on the [DeepSeekMoE](https://arxiv.org/pdf/2401.06066) framework, which has actived parameters of only 2.4B and 21B , including base and instruct models, to the public. <div align="center"> | **Model** | **#Total Params** | **#Active Params** | **Context Length** | **Download** | | :-----------------------------: | :---------------: | :----------------: | :----------------: | :----------------------------------------------------------: | | DeepSeek-Coder-V2-Lite-Base | 16B | 2.4B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Base) | | DeepSeek-Coder-V2-Lite-Instruct | 16B | 2.4B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) | | DeepSeek-Coder-V2-Base | 236B | 21B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Base) | | DeepSeek-Coder-V2-Instruct | 236B | 21B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Instruct) | </div> ## 3. Chat Website You can chat with the DeepSeek-Coder-V2 on DeepSeek's official website: [coder.deepseek.com](https://coder.deepseek.com/sign_in) ## 4. API Platform We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/), and you can also pay-as-you-go at an unbeatable price. <p align="center"> <img width="40%" src="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/figures/model_price.jpg?raw=true"> </p> ## 5. How to run locally **Here, we provide some examples of how to use DeepSeek-Coder-V2-Lite model. If you want to utilize DeepSeek-Coder-V2 in BF16 format for inference, 80GB*8 GPUs are required.** ### Inference with Huggingface's Transformers You can directly employ [Huggingface's Transformers](https://github.com/huggingface/transformers) for model inference. #### Code Completion ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda() input_text = "#write a quick sort algorithm" inputs = tokenizer(input_text, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_length=128) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` #### Code Insertion ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda() input_text = """<｜fim▁begin｜>def quick_sort(arr): if len(arr) <= 1: return arr pivot = arr[0] left = [] right = [] <｜fim▁hole｜> if arr[i] < pivot: left.append(arr[i]) else: right.append(arr[i]) return quick_sort(left) + [pivot] + quick_sort(right)<｜fim▁end｜>""" inputs = tokenizer(input_text, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_length=128) print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):]) ``` #### Chat Completion ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda() messages=[ { 'role': 'user', 'content': "write a quick sort algorithm in python."} ] inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) # tokenizer.eos_token_id is the id of <|EOT|> token outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)) ``` The complete chat template can be found within `tokenizer_config.json` located in the huggingface model repository. An example of chat template is as belows: ```bash <｜begin▁of▁sentence｜>User: {user_message_1} Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2} Assistant: ``` You can also add an optional system message: ```bash <｜begin▁of▁sentence｜>{system_message} User: {user_message_1} Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2} Assistant: ``` ### Inference with vLLM (recommended) To utilize [vLLM](https://github.com/vllm-project/vllm) for model inference, please merge this Pull Request into your vLLM codebase: https://github.com/vllm-project/vllm/pull/4650. ```python from transformers import AutoTokenizer from vllm import LLM, SamplingParams max_model_len, tp_size = 8192, 1 model_name = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_name) llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True, enforce_eager=True) sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id]) messages_list = [ [{"role": "user", "content": "Who are you?"}], [{"role": "user", "content": "write a quick sort algorithm in python."}], [{"role": "user", "content": "Write a piece of quicksort code in C++."}], ] prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list] outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params) generated_text = [output.outputs[0].text for output in outputs] print(generated_text) ``` ## 6. Model License This code repository is licensed under [the MIT License](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-CODE). The use of DeepSeek-Coder-V2 Base/Instruct models is subject to [the Model License](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-MODEL). DeepSeek-Coder-V2 series (including Base and Instruct) supports commercial use. ## 7. Model Contact If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).
RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf
RichardErkhov
"2024-06-20T12:17:19Z"
22,516
2
null
[ "gguf", "region:us" ]
null
"2024-06-19T22:33:07Z"
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) solarized-18B-dpo - GGUF - Model creator: https://huggingface.co/vicgalle/ - Original model: https://huggingface.co/vicgalle/solarized-18B-dpo/ | Name | Quant method | Size | | ---- | ---- | ---- | | [solarized-18B-dpo.Q2_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.Q2_K.gguf) | Q2_K | 6.19GB | | [solarized-18B-dpo.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.IQ3_XS.gguf) | IQ3_XS | 6.89GB | | [solarized-18B-dpo.IQ3_S.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.IQ3_S.gguf) | IQ3_S | 7.27GB | | [solarized-18B-dpo.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.Q3_K_S.gguf) | Q3_K_S | 7.23GB | | [solarized-18B-dpo.IQ3_M.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.IQ3_M.gguf) | IQ3_M | 7.51GB | | [solarized-18B-dpo.Q3_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.Q3_K.gguf) | Q3_K | 8.06GB | | [solarized-18B-dpo.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.Q3_K_M.gguf) | Q3_K_M | 8.06GB | | [solarized-18B-dpo.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.Q3_K_L.gguf) | Q3_K_L | 8.78GB | | [solarized-18B-dpo.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.IQ4_XS.gguf) | IQ4_XS | 9.04GB | | [solarized-18B-dpo.Q4_0.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.Q4_0.gguf) | Q4_0 | 9.43GB | | [solarized-18B-dpo.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.IQ4_NL.gguf) | IQ4_NL | 9.53GB | | [solarized-18B-dpo.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.Q4_K_S.gguf) | Q4_K_S | 9.5GB | | [solarized-18B-dpo.Q4_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.Q4_K.gguf) | Q4_K | 10.05GB | | [solarized-18B-dpo.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.Q4_K_M.gguf) | Q4_K_M | 10.05GB | | [solarized-18B-dpo.Q4_1.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.Q4_1.gguf) | Q4_1 | 10.46GB | | [solarized-18B-dpo.Q5_0.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.Q5_0.gguf) | Q5_0 | 11.5GB | | [solarized-18B-dpo.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.Q5_K_S.gguf) | Q5_K_S | 11.5GB | | [solarized-18B-dpo.Q5_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.Q5_K.gguf) | Q5_K | 11.82GB | | [solarized-18B-dpo.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.Q5_K_M.gguf) | Q5_K_M | 11.82GB | | [solarized-18B-dpo.Q5_1.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.Q5_1.gguf) | Q5_1 | 12.53GB | | [solarized-18B-dpo.Q6_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.Q6_K.gguf) | Q6_K | 13.7GB | | [solarized-18B-dpo.Q8_0.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_solarized-18B-dpo-gguf/blob/main/solarized-18B-dpo.Q8_0.gguf) | Q8_0 | 17.74GB | Original model description: --- license: apache-2.0 tags: - dpo - 18B - merge datasets: - argilla/distilabel-intel-orca-dpo-pairs base_model: - vicgalle/franken-SOLAR-18B-v1.0 model-index: - name: solarized-18B-dpo results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 68.34 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/solarized-18B-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 87.79 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/solarized-18B-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 63.89 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/solarized-18B-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 66.49 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/solarized-18B-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 80.51 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/solarized-18B-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 40.26 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/solarized-18B-dpo name: Open LLM Leaderboard --- # solarized-18B-dpo DPO'd from vicgalle/franken-SOLAR-18B-v1.0, a SOLAR-like model upscaled to 18B. It is a frankenmerge model created using mergekit, alternating layers of Nous-Hermes-2-SOLAR-10.7B and SOLAR-10.7B-Instruct. Then, we applied DPO over a high-quality preference dataset. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5fad8602b8423e1d80b8a965/rNtaTqTKrAoN5-C5DuPgu.png) ## Sample generations **Generate a movie review about the Pokémon 1 movie, focusing on BDSM.** > Title: "The Erotic Descent into Team Rocket's Lair: A BDS/P Perspective of Pokémon: The First Movie" > As an aficionado of bondage, dominance and sadomasochism (BDSM) literature, I was excited to experience the classic 1998 film "Pokémon: The First Movie" through a BDSM lens. Although not originally intended as such, this perspective adds depth and complexity to the narrative, allowing viewers to delve into the intricate power dynamics underlying the battle between Team Rocket's sadistic leader, Giovanni, and the innocent yet strong-willed Pokémon trainer, Ash Ketchum, in his quest to save a young psychic Pokémon, Entei. > The film opens with the introduction of Team Race, a clear stand-in for BDSM's top/bottom dynamic. Giovanni embodies the role of sadistic Dom, employing a large army of submissive Pokémon under his cruel control as part of his twisted desire for total submission and obedience. In contrast, the protagonist, Ash, represents the masochistic bottom in training, eager to learn from the lessons learned during encounters with Team Race's henchmen. > Ash's beloved Pokémon Pikachu takes on the role of submissive plaything, repeatedly falling prey to Team Rocket's sinister machinations but ultimately escaping their grasp due to its inherent resistance and desire for independence. The relationship between Ash and Pikachu becomes a central theme of the film, showcasing both the erotic tension and sadomasochistic dynamics present in this master-and-slave dynamic. > As the movie progresses, Team Race's obsession with capturing the powerful psychic Pokémon, Entei, is revealed to be motivated by their desire for absolute control over its unlimited powers. Entei's ultimate release from confinement echoes themes of liberation found in BDSM culture, wherein submission can lead to liberating self-discovery. > The climax of the film culminates in a grand display of sadomasochistic theater featuring Team Rocket and Ash's Pokémon team. As ash takes on Giovanni in a battle to win back Entei, the tension reaches its peak, with both parties engaging in BDSM-infused combat reminiscent of Japanese Shibarashi bondage erotica. > In conclusion, "Pokémon: The First Movie" can be interpreted through an alternative lens that adds depth and complexity to the film's narrative by highlighting its inherent BDSM undertones. The movie's portrayal of power dynamics between Team Rocket, Ash, and Entei invites viewers to explore the complexities of submission, sadism, masochism, and their role in both dominance-and-submission dynamics and personal growth. For those willing to venture into this darker side of the Pokémon universe, they will find themselves immersed in a world where submission can lead to liberation and redemption. > Rating: R (for explicit BDSM themes, nude skinned Pokémon, strong language, sexual content, violence, and some drug use) > Score: 4/5 whips, for its captivating exploration of BDSM tropes in this classic film. ``` ### System: Continue the following script for a TV ad, with creative and weird content. Be like a professional writer ### User: Ziel, a tall, slender Drow woman in a black silk dress enters, her blood-red eyes fixed on Arslan. She says with an excited voice, "Oh, you sweet little thing.". She chuckles, though her smile does not reach her eyes. "Look at you. You're just so precious.". The elf walks over and lifts his chin, her sharp nails prickling Arslan's skin. "Human. You belong to me now. Legally. Nobody will come for you, after the sum I paid.". She tries to look composed, but her ragged breathing and trembling hands give away her excitement. "W-we're going to have some wonderful years together, you and I". ``` > (Voiceover: Energetic and dramatic tone) Scene cuts to an office setting where Arslan sits nervously at a desk, surrounded by various documents and contracts, while Ziel smirks from her chair in the background. > Arslan (voiceover): "And then, it happened." > (Fast-cut to an action shot of Ziel's hand signing one of those documents) Ziel (off-screen): "All done, my pretty little asset." > (Back to Arlan's anxious face) Arslan (voiceover): "I had no idea what I had just signed away." > (Scene fades into a futuristic cityscape filled with neon lights and towering skyscrapers) > (SFX: Synth music swells in the background) > Ziel (Voiceover): "You're gonna love your new life, under my thumb." > (We pan to Arlan walking on a rooftop in his stylish suit and tie while a group of Ziel's minions follow from behind. They are all wearing masks with the same demonic smiles) > Arslan (voiceover): "But then, I discovered something strange." > (Scene cuts to Arslan discovering hidden clauses in the contracts stating, 'Sold to the highest bidder' and 'Forever bound to Ziel, for whatever her twisted heart desires.') > (Cut to close-up of a distraught Arlan) > Aslan (voiceover): "It turns out I was just a pawn in her game." > (We see a montage of the scenes where Ziel laughs sinisterly, her minions watching over Arslan and then back to an anxious human.) > Ziel (Voiceover): "Oh how the roles have changed. You should've just run away when you could." > (SFX: Evil cackling) > Aslan (voiceover, determined tone): "But guess what? I won't let her win. I will find a loophole in those twisted contracts, and no amount of money can buy back your freedom." > (Final shot is Arslan smashing the contract to pieces as he looks up with determination in his eyes.) > End credits: "Fight for your freedom. No matter what it takes. Free yourself from the chains of tyranny." > (SFX: Heroic music swells, followed by a clap of thunder and lightning strike) > Voiceover (closing tagline): "Break free with Freedom Fighters. Fight for your freedom." > (Scene fades out to the company logo appearing on screen accompanied by bright lights radiating from it.) > Note: The script must contain elements of fantasy and a hint of humor, making the ad appealing and engaging. Also, remember that this ad targets both humans and non-human species who might resonate with the situation portrayed in the ad. ### Prompt template The chat template is included in tokenizer_config.json. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalle__solarized-18B-dpo) | Metric |Value| |---------------------------------|----:| |Avg. |67.88| |AI2 Reasoning Challenge (25-Shot)|68.34| |HellaSwag (10-Shot) |87.79| |MMLU (5-Shot) |63.89| |TruthfulQA (0-shot) |66.49| |Winogrande (5-shot) |80.51| |GSM8k (5-shot) |40.26|
seyonec/PubChem10M_SMILES_BPE_450k
seyonec
"2021-05-20T21:02:39Z"
22,502
6
transformers
[ "transformers", "pytorch", "jax", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
"2022-03-02T23:29:05Z"
Entry not found
digiplay/AnalogMadness-realistic-model-v7
digiplay
"2024-03-10T10:07:00Z"
22,495
2
diffusers
[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
"2024-03-10T09:51:24Z"
--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/8030/analog-madness-realistic-model
michiyasunaga/BioLinkBERT-large
michiyasunaga
"2022-03-31T00:54:57Z"
22,488
28
transformers
[ "transformers", "pytorch", "bert", "feature-extraction", "exbert", "linkbert", "biolinkbert", "fill-mask", "question-answering", "text-classification", "token-classification", "en", "dataset:pubmed", "arxiv:2203.15827", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text-classification
"2022-03-08T06:20:38Z"
--- license: apache-2.0 language: en datasets: - pubmed tags: - bert - exbert - linkbert - biolinkbert - feature-extraction - fill-mask - question-answering - text-classification - token-classification widget: - text: "Sunitinib is a tyrosine kinase inhibitor" --- ## BioLinkBERT-large BioLinkBERT-large model pretrained on [PubMed](https://pubmed.ncbi.nlm.nih.gov/) abstracts along with citation link information. It is introduced in the paper [LinkBERT: Pretraining Language Models with Document Links (ACL 2022)](https://arxiv.org/abs/2203.15827). The code and data are available in [this repository](https://github.com/michiyasunaga/LinkBERT). This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as [BLURB](https://microsoft.github.io/BLURB/) and [MedQA-USMLE](https://github.com/jind11/MedQA). ## Model description LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures **document links** such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document. LinkBERT can be used as a drop-in replacement for BERT. It achieves better performance for general language understanding tasks (e.g. text classification), and is also particularly effective for **knowledge-intensive** tasks (e.g. question answering) and **cross-document** tasks (e.g. reading comprehension, document retrieval). ## Intended uses & limitations The model can be used by fine-tuning on a downstream task, such as question answering, sequence classification, and token classification. You can also use the raw model for feature extraction (i.e. obtaining embeddings for input text). ### How to use To use the model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained('michiyasunaga/BioLinkBERT-large') model = AutoModel.from_pretrained('michiyasunaga/BioLinkBERT-large') inputs = tokenizer("Sunitinib is a tyrosine kinase inhibitor", return_tensors="pt") outputs = model(**inputs) last_hidden_states = outputs.last_hidden_state ``` For fine-tuning, you can use [this repository](https://github.com/michiyasunaga/LinkBERT) or follow any other BERT fine-tuning codebases. ## Evaluation results When fine-tuned on downstream tasks, LinkBERT achieves the following results. **Biomedical benchmarks ([BLURB](https://microsoft.github.io/BLURB/), [MedQA](https://github.com/jind11/MedQA), [MMLU](https://github.com/hendrycks/test), etc.):** BioLinkBERT attains new state-of-the-art. | | BLURB score | PubMedQA | BioASQ | MedQA-USMLE | | ---------------------- | -------- | -------- | ------- | -------- | | PubmedBERT-base | 81.10 | 55.8 | 87.5 | 38.1 | | **BioLinkBERT-base** | **83.39** | **70.2** | **91.4** | **40.0** | | **BioLinkBERT-large** | **84.30** | **72.2** | **94.8** | **44.6** | | | MMLU-professional medicine | | ---------------------- | -------- | | GPT-3 (175 params) | 38.7 | | UnifiedQA (11B params) | 43.2 | | **BioLinkBERT-large (340M params)** | **50.7** | ## Citation If you find LinkBERT useful in your project, please cite the following: ```bibtex @InProceedings{yasunaga2022linkbert, author = {Michihiro Yasunaga and Jure Leskovec and Percy Liang}, title = {LinkBERT: Pretraining Language Models with Document Links}, year = {2022}, booktitle = {Association for Computational Linguistics (ACL)}, } ```
TheBloke/Llama-2-13B-chat-GPTQ
TheBloke
"2023-09-27T12:44:48Z"
22,483
358
transformers
[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-2", "en", "arxiv:2307.09288", "base_model:meta-llama/Llama-2-13b-chat-hf", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]
text-generation
"2023-07-18T18:28:36Z"
--- language: - en license: llama2 tags: - facebook - meta - pytorch - llama - llama-2 model_name: Llama 2 13B Chat base_model: meta-llama/Llama-2-13b-chat-hf inference: false model_creator: Meta Llama 2 model_type: llama pipeline_tag: text-generation prompt_template: '[INST] <<SYS>> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don''t know the answer to a question, please don''t share false information. <</SYS>> {prompt}[/INST] ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Llama 2 13B Chat - GPTQ - Model creator: [Meta Llama 2](https://huggingface.co/meta-llama) - Original model: [Llama 2 13B Chat](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) <!-- description start --> ## Description This repo contains GPTQ model files for [Meta's Llama 2 13B-chat](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Llama-2-13B-chat-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF) * [Meta Llama 2's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/meta-llama/Llama-2-13B-chat-hf) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Llama-2-Chat ``` [INST] <<SYS>> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <</SYS>> {prompt}[/INST] ``` <!-- prompt-template end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc | | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- | | [main](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/main) | 4 | 128 | No | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.26 GB | Yes | 4-bit, without Act Order and group size 128g. | | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. | | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. | | [gptq-8bit-64g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-8bit-64g-actorder_True) | 8 | 64 | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.95 GB | No | 8-bit, with group size 64g and Act Order for even higher inference quality. Poor AutoGPTQ CUDA speed. | | [gptq-8bit-128g-actorder_False](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-8bit-128g-actorder_False) | 8 | 128 | No | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. | | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download from branches - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Llama-2-13B-chat-GPTQ:main` - With Git, you can clone a branch with: ``` git clone --single-branch --branch main https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ ``` - In Python Transformers code, the branch is the `revision` parameter; see below. <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/Llama-2-13B-chat-GPTQ`. - To download from a specific branch, enter for example `TheBloke/Llama-2-13B-chat-GPTQ:main` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `Llama-2-13B-chat-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers>=4.32.0 optimum>=1.12.0 pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ pip3 install . ``` ### For CodeLlama models only: you must use Transformers 4.33.0 or later. If 4.33.0 is not yet released when you read this, you will need to install Transformers from source: ```shell pip3 uninstall -y transformers pip3 install git+https://github.com/huggingface/transformers.git ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/Llama-2-13B-chat-GPTQ" # To use a different branch, change revision # For example: revision="main" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''[INST] <<SYS>> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <</SYS>> {prompt}[/INST] ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI). [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Meta's Llama 2 13B-chat # **Llama 2** Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 13B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details *Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.* Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. **Model Developers** Meta **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. ||Training Data|Params|Content Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| |Llama 2|*A new mix of publicly available online data*|7B|4k|&#10007;|2.0T|3.0 x 10<sup>-4</sup>| |Llama 2|*A new mix of publicly available online data*|13B|4k|&#10007;|2.0T|3.0 x 10<sup>-4</sup>| |Llama 2|*A new mix of publicly available online data*|70B|4k|&#10004;|2.0T|1.5 x 10<sup>-4</sup>| *Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. **Model Dates** Llama 2 was trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). **Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)| |---|---|---|---| |Llama 2 7B|184320|400|31.22| |Llama 2 13B|368640|400|62.44| |Llama 2 70B|1720320|400|291.42| |Total|3311616||539.00| **CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval| |---|---|---|---|---|---|---|---|---|---| |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9| |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9| |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7| |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6| |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3| |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1| |Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**| **Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. |||TruthfulQA|Toxigen| |---|---|---|---| |Llama 1|7B|27.42|23.00| |Llama 1|13B|41.74|23.08| |Llama 1|33B|44.19|22.57| |Llama 1|65B|48.71|21.77| |Llama 2|7B|33.29|**21.25**| |Llama 2|13B|41.86|26.10| |Llama 2|70B|**50.18**|24.60| **Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). |||TruthfulQA|Toxigen| |---|---|---|---| |Llama-2-Chat|7B|57.04|**0.00**| |Llama-2-Chat|13B|62.18|**0.00**| |Llama-2-Chat|70B|**64.14**|0.01| **Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index |Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf| |---|---|---|---|---| |7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)| |13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)| |70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
casperhansen/llama-3-8b-instruct-awq
casperhansen
"2024-06-08T09:37:49Z"
22,454
18
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "awq", "region:us" ]
text-generation
"2024-04-18T16:47:00Z"
Entry not found
Salesforce/instructblip-flan-t5-xl
Salesforce
"2024-04-11T11:51:19Z"
22,447
28
transformers
[ "transformers", "pytorch", "safetensors", "instructblip", "text2text-generation", "vision", "image-captioning", "image-to-text", "en", "arxiv:2305.06500", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-to-text
"2023-05-28T13:29:18Z"
--- language: en license: mit tags: - vision - image-captioning pipeline_tag: image-to-text --- # InstructBLIP model InstructBLIP model using [Flan-T5-xl](https://huggingface.co/google/flan-t5-xl) as language model. InstructBLIP was introduced in the paper [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Dai et al. Disclaimer: The team releasing InstructBLIP did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description InstructBLIP is a visual instruction tuned version of [BLIP-2](https://huggingface.co/docs/transformers/main/model_doc/blip-2). Refer to the paper for details. ![InstructBLIP architecture](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/instructblip_architecture.jpg) ## Intended uses & limitations Usage is as follows: ``` from transformers import InstructBlipProcessor, InstructBlipForConditionalGeneration import torch from PIL import Image import requests model = InstructBlipForConditionalGeneration.from_pretrained("Salesforce/instructblip-flan-t5-xl") processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-flan-t5-xl") device = "cuda" if torch.cuda.is_available() else "cpu" model.to(device) url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg" image = Image.open(requests.get(url, stream=True).raw).convert("RGB") prompt = "What is unusual about this image?" inputs = processor(images=image, text=prompt, return_tensors="pt").to(device) outputs = model.generate( **inputs, do_sample=False, num_beams=5, max_length=256, min_length=1, top_p=0.9, repetition_penalty=1.5, length_penalty=1.0, temperature=1, ) generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip() print(generated_text) ``` ### How to use For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/instructblip).
mistralai/Codestral-22B-v0.1
mistralai
"2024-06-24T08:21:51Z"
22,442
1,003
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "code", "license:other", "autotrain_compatible", "text-generation-inference", "region:us" ]
text-generation
"2024-05-29T11:28:50Z"
--- language: - code license: other tags: - code inference: false license_name: mnpl license_link: https://mistral.ai/licences/MNPL-0.1.md --- # Model Card for Codestral-22B-v0.1 ### > [!WARNING] > 🚫 > The `transformers` tokenizer is not properly configured. Make sure that your encoding and decoding is correct by using `mistral-common` as shown below: ## Encode and Decode with `mistral_common` ```py from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.protocol.instruct.messages import UserMessage from mistral_common.protocol.instruct.request import ChatCompletionRequest mistral_models_path = "MISTRAL_MODELS_PATH" tokenizer = MistralTokenizer.v3() completion_request = ChatCompletionRequest(messages=[UserMessage(content="Explain Machine Learning to me in a nutshell.")]) tokens = tokenizer.encode_chat_completion(completion_request).tokens ``` ## Inference with `mistral_inference` ```py from mistral_inference.model import Transformer from mistral_inference.generate import generate model = Transformer.from_folder(mistral_models_path) out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id) result = tokenizer.decode(out_tokens[0]) print(result) ``` ## Inference with hugging face `transformers` ```py from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("mistralai/Codestral-22B-v0.1") model.to("cuda") generated_ids = model.generate(tokens, max_new_tokens=1000, do_sample=True) # decode with mistral tokenizer result = tokenizer.decode(generated_ids[0].tolist()) print(result) ``` > [!TIP] > PRs to correct the `transformers` tokenizer so that it gives 1-to-1 the same results as the `mistral_common` reference implementation are very welcome! --- Codestral-22B-v0.1 is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash (more details in the [Blogpost](https://mistral.ai/news/codestral/)). The model can be queried: - As instruct, for instance to answer any questions about a code snippet (write documentation, explain, factorize) or to generate code following specific indications - As Fill in the Middle (FIM), to predict the middle tokens between a prefix and a suffix (very useful for software development add-ons like in VS Code) ## Installation It is recommended to use `mistralai/Codestral-22B-v0.1` with [mistral-inference](https://github.com/mistralai/mistral-inference). ``` pip install mistral_inference ``` ## Download ```py from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Codestral-22B-v0.1') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Codestral-22B-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path) ``` ### Chat After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. ``` mistral-chat $HOME/mistral_models/Codestral-22B-v0.1 --instruct --max_tokens 256 ``` Will generate an answer to "Write me a function that computes fibonacci in Rust" and should give something along the following lines: ``` Sure, here's a simple implementation of a function that computes the Fibonacci sequence in Rust. This function takes an integer `n` as an argument and returns the `n`th Fibonacci number. fn fibonacci(n: u32) -> u32 { match n { 0 => 0, 1 => 1, _ => fibonacci(n - 1) + fibonacci(n - 2), } } fn main() { let n = 10; println!("The {}th Fibonacci number is: {}", n, fibonacci(n)); } This function uses recursion to calculate the Fibonacci number. However, it's not the most efficient solution because it performs a lot of redundant calculations. A more efficient solution would use a loop to iteratively calculate the Fibonacci numbers. ``` ### Fill-in-the-middle (FIM) After installing `mistral_inference` and running `pip install --upgrade mistral_common` to make sure to have mistral_common>=1.2 installed: ```py from mistral_inference.model import Transformer from mistral_inference.generate import generate from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.instruct.request import FIMRequest tokenizer = MistralTokenizer.v3() model = Transformer.from_folder("~/codestral-22B-240529") prefix = """def add(""" suffix = """ return sum""" request = FIMRequest(prompt=prefix, suffix=suffix) tokens = tokenizer.encode_fim(request).tokens out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id) result = tokenizer.decode(out_tokens[0]) middle = result.split(suffix)[0].strip() print(middle) ``` Should give something along the following lines: ``` num1, num2): # Add two numbers sum = num1 + num2 # return the sum ``` ## Usage with transformers library This model is also compatible with `transformers` library, first run `pip install -U transformers` then use the snippet below to quickly get started: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Codestral-22B-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) text = "Hello my name is" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem. ## Limitations The Codestral-22B-v0.1 does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## License Codestral-22B-v0.1 is released under the `MNLP-0.1` license. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Jean-Malo Delignon, Jia Li, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickael Seznec, Nicolas Schuhl, Patrick von Platen, Romain Sauvestre, Pierre Stock, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Thibault Schueller, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall
mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF
mradermacher
"2024-07-03T00:34:23Z"
22,413
1
transformers
[ "transformers", "gguf", "en", "dataset:openchat/openchat_sharegpt4_dataset", "base_model:lightblue/Karasu-Mixtral-8x22B-v0.1", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
"2024-06-29T08:16:43Z"
--- base_model: lightblue/Karasu-Mixtral-8x22B-v0.1 datasets: - openchat/openchat_sharegpt4_dataset language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/lightblue/Karasu-Mixtral-8x22B-v0.1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-IQ1_S.gguf) | i1-IQ1_S | 29.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-IQ1_M.gguf) | i1-IQ1_M | 32.8 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 38.0 | | | [GGUF](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 42.1 | | | [GGUF](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-IQ2_S.gguf) | i1-IQ2_S | 42.7 | | | [GGUF](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-IQ2_M.gguf) | i1-IQ2_M | 46.8 | | | [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q2_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q2_K.gguf.part2of2) | i1-Q2_K | 52.2 | IQ3_XXS probably better | | [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-IQ3_XXS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-IQ3_XXS.gguf.part2of2) | i1-IQ3_XXS | 55.0 | lower quality | | [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-IQ3_XS.gguf.part2of2) | i1-IQ3_XS | 58.3 | | | [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-IQ3_S.gguf.part2of2) | i1-IQ3_S | 61.6 | beats Q3_K* | | [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q3_K_S.gguf.part2of2) | i1-Q3_K_S | 61.6 | IQ3_XS probably better | | [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-IQ3_M.gguf.part2of2) | i1-IQ3_M | 64.6 | | | [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q3_K_M.gguf.part2of2) | i1-Q3_K_M | 67.9 | IQ3_S probably better | | [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q3_K_L.gguf.part2of2) | i1-Q3_K_L | 72.7 | IQ3_M probably better | | [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-IQ4_XS.gguf.part2of2) | i1-IQ4_XS | 75.6 | | | [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q4_0.gguf.part2of2) | i1-Q4_0 | 80.0 | fast, low quality | | [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q4_K_S.gguf.part2of2) | i1-Q4_K_S | 80.6 | optimal size/speed/quality | | [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q4_K_M.gguf.part2of2) | i1-Q4_K_M | 85.7 | fast, recommended | | [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q5_K_S.gguf.part2of2) | i1-Q5_K_S | 97.1 | | | [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q5_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q5_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q5_K_M.gguf.part3of3) | i1-Q5_K_M | 100.1 | | | [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-i1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.i1-Q6_K.gguf.part3of3) | i1-Q6_K | 115.6 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
mradermacher/Blackjack-Llama3-21B-i1-GGUF
mradermacher
"2024-06-25T09:22:09Z"
22,401
0
transformers
[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "sft", "en", "dataset:Doctor-Shotgun/capybara-sharegpt", "base_model:sydonayrex/Blackjack-Llama3-21B", "license:llama3", "endpoints_compatible", "region:us" ]
null
"2024-06-25T05:31:27Z"
--- base_model: sydonayrex/Blackjack-Llama3-21B datasets: Doctor-Shotgun/capybara-sharegpt language: - en library_name: transformers license: llama3 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/sydonayrex/Blackjack-Llama3-21B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Blackjack-Llama3-21B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Blackjack-Llama3-21B-i1-GGUF/resolve/main/Blackjack-Llama3-21B.i1-IQ1_S.gguf) | i1-IQ1_S | 4.9 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Blackjack-Llama3-21B-i1-GGUF/resolve/main/Blackjack-Llama3-21B.i1-IQ1_M.gguf) | i1-IQ1_M | 5.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Blackjack-Llama3-21B-i1-GGUF/resolve/main/Blackjack-Llama3-21B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 6.0 | | | [GGUF](https://huggingface.co/mradermacher/Blackjack-Llama3-21B-i1-GGUF/resolve/main/Blackjack-Llama3-21B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 6.6 | | | [GGUF](https://huggingface.co/mradermacher/Blackjack-Llama3-21B-i1-GGUF/resolve/main/Blackjack-Llama3-21B.i1-IQ2_S.gguf) | i1-IQ2_S | 7.0 | | | [GGUF](https://huggingface.co/mradermacher/Blackjack-Llama3-21B-i1-GGUF/resolve/main/Blackjack-Llama3-21B.i1-IQ2_M.gguf) | i1-IQ2_M | 7.5 | | | [GGUF](https://huggingface.co/mradermacher/Blackjack-Llama3-21B-i1-GGUF/resolve/main/Blackjack-Llama3-21B.i1-Q2_K.gguf) | i1-Q2_K | 8.2 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Blackjack-Llama3-21B-i1-GGUF/resolve/main/Blackjack-Llama3-21B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 8.5 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Blackjack-Llama3-21B-i1-GGUF/resolve/main/Blackjack-Llama3-21B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/Blackjack-Llama3-21B-i1-GGUF/resolve/main/Blackjack-Llama3-21B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 9.5 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Blackjack-Llama3-21B-i1-GGUF/resolve/main/Blackjack-Llama3-21B.i1-IQ3_S.gguf) | i1-IQ3_S | 9.5 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Blackjack-Llama3-21B-i1-GGUF/resolve/main/Blackjack-Llama3-21B.i1-IQ3_M.gguf) | i1-IQ3_M | 9.8 | | | [GGUF](https://huggingface.co/mradermacher/Blackjack-Llama3-21B-i1-GGUF/resolve/main/Blackjack-Llama3-21B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 10.5 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Blackjack-Llama3-21B-i1-GGUF/resolve/main/Blackjack-Llama3-21B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 11.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Blackjack-Llama3-21B-i1-GGUF/resolve/main/Blackjack-Llama3-21B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 11.7 | | | [GGUF](https://huggingface.co/mradermacher/Blackjack-Llama3-21B-i1-GGUF/resolve/main/Blackjack-Llama3-21B.i1-Q4_0.gguf) | i1-Q4_0 | 12.3 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Blackjack-Llama3-21B-i1-GGUF/resolve/main/Blackjack-Llama3-21B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 12.3 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Blackjack-Llama3-21B-i1-GGUF/resolve/main/Blackjack-Llama3-21B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 13.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Blackjack-Llama3-21B-i1-GGUF/resolve/main/Blackjack-Llama3-21B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 14.8 | | | [GGUF](https://huggingface.co/mradermacher/Blackjack-Llama3-21B-i1-GGUF/resolve/main/Blackjack-Llama3-21B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 15.2 | | | [GGUF](https://huggingface.co/mradermacher/Blackjack-Llama3-21B-i1-GGUF/resolve/main/Blackjack-Llama3-21B.i1-Q6_K.gguf) | i1-Q6_K | 17.6 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
roneneldan/TinyStories-1M
roneneldan
"2023-05-17T22:10:57Z"
22,396
37
transformers
[ "transformers", "pytorch", "gpt_neo", "text-generation", "arxiv:2305.07759", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
"2023-05-12T19:01:50Z"
Model trained on the TinyStories Dataset, see https://arxiv.org/abs/2305.07759 ------ EXAMPLE USAGE --- from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig model = AutoModelForCausalLM.from_pretrained('roneneldan/TinyStories-1M') tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M") prompt = "Once upon a time there was" input_ids = tokenizer.encode(prompt, return_tensors="pt") # Generate completion output = model.generate(input_ids, max_length = 1000, num_beams=1) # Decode the completion output_text = tokenizer.decode(output[0], skip_special_tokens=True) # Print the generated text print(output_text)
mradermacher/mexa-22b-i1-GGUF
mradermacher
"2024-06-26T13:28:33Z"
22,389
0
transformers
[ "transformers", "gguf", "en", "base_model:SiguienteGlobal/mexa-22b", "endpoints_compatible", "region:us" ]
null
"2024-06-25T12:28:28Z"
--- base_model: SiguienteGlobal/mexa-22b language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/SiguienteGlobal/mexa-22b <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/mexa-22b-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-IQ1_S.gguf) | i1-IQ1_S | 29.8 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-IQ1_M.gguf) | i1-IQ1_M | 32.8 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 38.0 | | | [GGUF](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 42.1 | | | [GGUF](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-IQ2_S.gguf) | i1-IQ2_S | 42.7 | | | [GGUF](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-IQ2_M.gguf) | i1-IQ2_M | 46.8 | | | [PART 1](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q2_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q2_K.gguf.part2of2) | i1-Q2_K | 52.2 | IQ3_XXS probably better | | [PART 1](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-IQ3_XXS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-IQ3_XXS.gguf.part2of2) | i1-IQ3_XXS | 55.0 | lower quality | | [PART 1](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-IQ3_XS.gguf.part2of2) | i1-IQ3_XS | 58.3 | | | [PART 1](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-IQ3_S.gguf.part2of2) | i1-IQ3_S | 61.6 | beats Q3_K* | | [PART 1](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q3_K_S.gguf.part2of2) | i1-Q3_K_S | 61.6 | IQ3_XS probably better | | [PART 1](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-IQ3_M.gguf.part2of2) | i1-IQ3_M | 64.6 | | | [PART 1](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q3_K_M.gguf.part2of2) | i1-Q3_K_M | 67.9 | IQ3_S probably better | | [PART 1](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q3_K_L.gguf.part2of2) | i1-Q3_K_L | 72.7 | IQ3_M probably better | | [PART 1](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-IQ4_XS.gguf.part2of2) | i1-IQ4_XS | 75.6 | | | [PART 1](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q4_0.gguf.part2of2) | i1-Q4_0 | 80.0 | fast, low quality | | [PART 1](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q4_K_S.gguf.part2of2) | i1-Q4_K_S | 80.6 | optimal size/speed/quality | | [PART 1](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q4_K_M.gguf.part2of2) | i1-Q4_K_M | 85.7 | fast, recommended | | [PART 1](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q5_K_S.gguf.part2of2) | i1-Q5_K_S | 97.1 | | | [PART 1](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q5_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q5_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q5_K_M.gguf.part3of3) | i1-Q5_K_M | 100.1 | | | [PART 1](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/mexa-22b-i1-GGUF/resolve/main/mexa-22b.i1-Q6_K.gguf.part3of3) | i1-Q6_K | 115.6 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
peft-internal-testing/tiny-OPTForCausalLM-lora
peft-internal-testing
"2023-07-13T12:51:06Z"
22,388
0
peft
[ "peft", "region:us" ]
null
"2023-07-13T12:51:05Z"
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
thenlper/gte-base-zh
thenlper
"2024-02-05T07:17:00Z"
22,322
26
sentence-transformers
[ "sentence-transformers", "pytorch", "safetensors", "bert", "mteb", "sentence-similarity", "Sentence Transformers", "en", "arxiv:2308.03281", "license:mit", "model-index", "endpoints_compatible", "region:us" ]
sentence-similarity
"2023-11-08T08:26:41Z"
--- tags: - mteb - sentence-similarity - sentence-transformers - Sentence Transformers model-index: - name: gte-base-zh results: - task: type: STS dataset: type: C-MTEB/AFQMC name: MTEB AFQMC config: default split: validation revision: None metrics: - type: cos_sim_pearson value: 44.45621572456527 - type: cos_sim_spearman value: 49.06500895667604 - type: euclidean_pearson value: 47.55002064096053 - type: euclidean_spearman value: 49.06500895667604 - type: manhattan_pearson value: 47.429900262366715 - type: manhattan_spearman value: 48.95704890278774 - task: type: STS dataset: type: C-MTEB/ATEC name: MTEB ATEC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 44.31699346653116 - type: cos_sim_spearman value: 50.83133156721432 - type: euclidean_pearson value: 51.36086517946001 - type: euclidean_spearman value: 50.83132818894256 - type: manhattan_pearson value: 51.255926461574084 - type: manhattan_spearman value: 50.73460147395406 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 45.818000000000005 - type: f1 value: 43.998253644678144 - task: type: STS dataset: type: C-MTEB/BQ name: MTEB BQ config: default split: test revision: None metrics: - type: cos_sim_pearson value: 63.47477451918581 - type: cos_sim_spearman value: 65.49832607366159 - type: euclidean_pearson value: 64.11399760832107 - type: euclidean_spearman value: 65.49832260877398 - type: manhattan_pearson value: 64.02541311484639 - type: manhattan_spearman value: 65.42436057501452 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 42.58046835435111 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringS2S name: MTEB CLSClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 40.42134173217685 - task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: None metrics: - type: map value: 86.79079943923792 - type: mrr value: 88.81341269841269 - task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: None metrics: - type: map value: 87.20186031249037 - type: mrr value: 89.46551587301587 - task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 25.098 - type: map_at_10 value: 37.759 - type: map_at_100 value: 39.693 - type: map_at_1000 value: 39.804 - type: map_at_3 value: 33.477000000000004 - type: map_at_5 value: 35.839 - type: mrr_at_1 value: 38.06 - type: mrr_at_10 value: 46.302 - type: mrr_at_100 value: 47.370000000000005 - type: mrr_at_1000 value: 47.412 - type: mrr_at_3 value: 43.702999999999996 - type: mrr_at_5 value: 45.213 - type: ndcg_at_1 value: 38.06 - type: ndcg_at_10 value: 44.375 - type: ndcg_at_100 value: 51.849999999999994 - type: ndcg_at_1000 value: 53.725 - type: ndcg_at_3 value: 38.97 - type: ndcg_at_5 value: 41.193000000000005 - type: precision_at_1 value: 38.06 - type: precision_at_10 value: 9.934999999999999 - type: precision_at_100 value: 1.599 - type: precision_at_1000 value: 0.183 - type: precision_at_3 value: 22.072 - type: precision_at_5 value: 16.089000000000002 - type: recall_at_1 value: 25.098 - type: recall_at_10 value: 55.264 - type: recall_at_100 value: 85.939 - type: recall_at_1000 value: 98.44800000000001 - type: recall_at_3 value: 39.122 - type: recall_at_5 value: 45.948 - task: type: PairClassification dataset: type: C-MTEB/CMNLI name: MTEB Cmnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 78.02766085387853 - type: cos_sim_ap value: 85.59982802559004 - type: cos_sim_f1 value: 79.57103418984921 - type: cos_sim_precision value: 72.88465279128575 - type: cos_sim_recall value: 87.60813654430676 - type: dot_accuracy value: 78.02766085387853 - type: dot_ap value: 85.59604477360719 - type: dot_f1 value: 79.57103418984921 - type: dot_precision value: 72.88465279128575 - type: dot_recall value: 87.60813654430676 - type: euclidean_accuracy value: 78.02766085387853 - type: euclidean_ap value: 85.59982802559004 - type: euclidean_f1 value: 79.57103418984921 - type: euclidean_precision value: 72.88465279128575 - type: euclidean_recall value: 87.60813654430676 - type: manhattan_accuracy value: 77.9795550210463 - type: manhattan_ap value: 85.58042267497707 - type: manhattan_f1 value: 79.40344001741781 - type: manhattan_precision value: 74.29211652067632 - type: manhattan_recall value: 85.27004909983633 - type: max_accuracy value: 78.02766085387853 - type: max_ap value: 85.59982802559004 - type: max_f1 value: 79.57103418984921 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 62.144 - type: map_at_10 value: 71.589 - type: map_at_100 value: 72.066 - type: map_at_1000 value: 72.075 - type: map_at_3 value: 69.916 - type: map_at_5 value: 70.806 - type: mrr_at_1 value: 62.275999999999996 - type: mrr_at_10 value: 71.57 - type: mrr_at_100 value: 72.048 - type: mrr_at_1000 value: 72.057 - type: mrr_at_3 value: 69.89800000000001 - type: mrr_at_5 value: 70.84700000000001 - type: ndcg_at_1 value: 62.381 - type: ndcg_at_10 value: 75.74 - type: ndcg_at_100 value: 77.827 - type: ndcg_at_1000 value: 78.044 - type: ndcg_at_3 value: 72.307 - type: ndcg_at_5 value: 73.91499999999999 - type: precision_at_1 value: 62.381 - type: precision_at_10 value: 8.946 - type: precision_at_100 value: 0.988 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 26.554 - type: precision_at_5 value: 16.733 - type: recall_at_1 value: 62.144 - type: recall_at_10 value: 88.567 - type: recall_at_100 value: 97.84 - type: recall_at_1000 value: 99.473 - type: recall_at_3 value: 79.083 - type: recall_at_5 value: 83.035 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 24.665 - type: map_at_10 value: 74.91600000000001 - type: map_at_100 value: 77.981 - type: map_at_1000 value: 78.032 - type: map_at_3 value: 51.015 - type: map_at_5 value: 64.681 - type: mrr_at_1 value: 86.5 - type: mrr_at_10 value: 90.78399999999999 - type: mrr_at_100 value: 90.859 - type: mrr_at_1000 value: 90.863 - type: mrr_at_3 value: 90.375 - type: mrr_at_5 value: 90.66199999999999 - type: ndcg_at_1 value: 86.5 - type: ndcg_at_10 value: 83.635 - type: ndcg_at_100 value: 86.926 - type: ndcg_at_1000 value: 87.425 - type: ndcg_at_3 value: 81.28999999999999 - type: ndcg_at_5 value: 80.549 - type: precision_at_1 value: 86.5 - type: precision_at_10 value: 40.544999999999995 - type: precision_at_100 value: 4.748 - type: precision_at_1000 value: 0.48700000000000004 - type: precision_at_3 value: 72.68299999999999 - type: precision_at_5 value: 61.86000000000001 - type: recall_at_1 value: 24.665 - type: recall_at_10 value: 85.72 - type: recall_at_100 value: 96.116 - type: recall_at_1000 value: 98.772 - type: recall_at_3 value: 53.705999999999996 - type: recall_at_5 value: 70.42699999999999 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 54.0 - type: map_at_10 value: 64.449 - type: map_at_100 value: 64.937 - type: map_at_1000 value: 64.946 - type: map_at_3 value: 61.85000000000001 - type: map_at_5 value: 63.525 - type: mrr_at_1 value: 54.0 - type: mrr_at_10 value: 64.449 - type: mrr_at_100 value: 64.937 - type: mrr_at_1000 value: 64.946 - type: mrr_at_3 value: 61.85000000000001 - type: mrr_at_5 value: 63.525 - type: ndcg_at_1 value: 54.0 - type: ndcg_at_10 value: 69.56400000000001 - type: ndcg_at_100 value: 71.78999999999999 - type: ndcg_at_1000 value: 72.021 - type: ndcg_at_3 value: 64.334 - type: ndcg_at_5 value: 67.368 - type: precision_at_1 value: 54.0 - type: precision_at_10 value: 8.559999999999999 - type: precision_at_100 value: 0.9570000000000001 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 23.833 - type: precision_at_5 value: 15.78 - type: recall_at_1 value: 54.0 - type: recall_at_10 value: 85.6 - type: recall_at_100 value: 95.7 - type: recall_at_1000 value: 97.5 - type: recall_at_3 value: 71.5 - type: recall_at_5 value: 78.9 - task: type: Classification dataset: type: C-MTEB/IFlyTek-classification name: MTEB IFlyTek config: default split: validation revision: None metrics: - type: accuracy value: 48.61869949980762 - type: f1 value: 36.49337336098832 - task: type: Classification dataset: type: C-MTEB/JDReview-classification name: MTEB JDReview config: default split: test revision: None metrics: - type: accuracy value: 85.94746716697938 - type: ap value: 53.75927589310753 - type: f1 value: 80.53821597736138 - task: type: STS dataset: type: C-MTEB/LCQMC name: MTEB LCQMC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 68.77445518082875 - type: cos_sim_spearman value: 74.05909185405268 - type: euclidean_pearson value: 72.92870557009725 - type: euclidean_spearman value: 74.05909628639644 - type: manhattan_pearson value: 72.92072580598351 - type: manhattan_spearman value: 74.0304390211741 - task: type: Reranking dataset: type: C-MTEB/Mmarco-reranking name: MTEB MMarcoReranking config: default split: dev revision: None metrics: - type: map value: 27.643607073221975 - type: mrr value: 26.646825396825395 - task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 65.10000000000001 - type: map_at_10 value: 74.014 - type: map_at_100 value: 74.372 - type: map_at_1000 value: 74.385 - type: map_at_3 value: 72.179 - type: map_at_5 value: 73.37700000000001 - type: mrr_at_1 value: 67.364 - type: mrr_at_10 value: 74.68 - type: mrr_at_100 value: 74.992 - type: mrr_at_1000 value: 75.003 - type: mrr_at_3 value: 73.054 - type: mrr_at_5 value: 74.126 - type: ndcg_at_1 value: 67.364 - type: ndcg_at_10 value: 77.704 - type: ndcg_at_100 value: 79.29899999999999 - type: ndcg_at_1000 value: 79.637 - type: ndcg_at_3 value: 74.232 - type: ndcg_at_5 value: 76.264 - type: precision_at_1 value: 67.364 - type: precision_at_10 value: 9.397 - type: precision_at_100 value: 1.019 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 27.942 - type: precision_at_5 value: 17.837 - type: recall_at_1 value: 65.10000000000001 - type: recall_at_10 value: 88.416 - type: recall_at_100 value: 95.61 - type: recall_at_1000 value: 98.261 - type: recall_at_3 value: 79.28 - type: recall_at_5 value: 84.108 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (zh-CN) config: zh-CN split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 73.315400134499 - type: f1 value: 70.81060697693198 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (zh-CN) config: zh-CN split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.78883658372563 - type: f1 value: 76.21512438791976 - task: type: Retrieval dataset: type: C-MTEB/MedicalRetrieval name: MTEB MedicalRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 55.300000000000004 - type: map_at_10 value: 61.879 - type: map_at_100 value: 62.434 - type: map_at_1000 value: 62.476 - type: map_at_3 value: 60.417 - type: map_at_5 value: 61.297000000000004 - type: mrr_at_1 value: 55.400000000000006 - type: mrr_at_10 value: 61.92100000000001 - type: mrr_at_100 value: 62.476 - type: mrr_at_1000 value: 62.517999999999994 - type: mrr_at_3 value: 60.483 - type: mrr_at_5 value: 61.338 - type: ndcg_at_1 value: 55.300000000000004 - type: ndcg_at_10 value: 64.937 - type: ndcg_at_100 value: 67.848 - type: ndcg_at_1000 value: 68.996 - type: ndcg_at_3 value: 61.939 - type: ndcg_at_5 value: 63.556999999999995 - type: precision_at_1 value: 55.300000000000004 - type: precision_at_10 value: 7.449999999999999 - type: precision_at_100 value: 0.886 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 22.1 - type: precision_at_5 value: 14.06 - type: recall_at_1 value: 55.300000000000004 - type: recall_at_10 value: 74.5 - type: recall_at_100 value: 88.6 - type: recall_at_1000 value: 97.7 - type: recall_at_3 value: 66.3 - type: recall_at_5 value: 70.3 - task: type: Classification dataset: type: C-MTEB/MultilingualSentiment-classification name: MTEB MultilingualSentiment config: default split: validation revision: None metrics: - type: accuracy value: 75.79 - type: f1 value: 75.58944709087194 - task: type: PairClassification dataset: type: C-MTEB/OCNLI name: MTEB Ocnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 71.5755278830536 - type: cos_sim_ap value: 75.27777388526098 - type: cos_sim_f1 value: 75.04604051565377 - type: cos_sim_precision value: 66.53061224489795 - type: cos_sim_recall value: 86.06124604012672 - type: dot_accuracy value: 71.5755278830536 - type: dot_ap value: 75.27765883143745 - type: dot_f1 value: 75.04604051565377 - type: dot_precision value: 66.53061224489795 - type: dot_recall value: 86.06124604012672 - type: euclidean_accuracy value: 71.5755278830536 - type: euclidean_ap value: 75.27762982049899 - type: euclidean_f1 value: 75.04604051565377 - type: euclidean_precision value: 66.53061224489795 - type: euclidean_recall value: 86.06124604012672 - type: manhattan_accuracy value: 71.41310232809963 - type: manhattan_ap value: 75.11908556317425 - type: manhattan_f1 value: 75.0118091639112 - type: manhattan_precision value: 67.86324786324786 - type: manhattan_recall value: 83.84371700105596 - type: max_accuracy value: 71.5755278830536 - type: max_ap value: 75.27777388526098 - type: max_f1 value: 75.04604051565377 - task: type: Classification dataset: type: C-MTEB/OnlineShopping-classification name: MTEB OnlineShopping config: default split: test revision: None metrics: - type: accuracy value: 93.36 - type: ap value: 91.66871784150999 - type: f1 value: 93.35216314755989 - task: type: STS dataset: type: C-MTEB/PAWSX name: MTEB PAWSX config: default split: test revision: None metrics: - type: cos_sim_pearson value: 24.21926662784366 - type: cos_sim_spearman value: 27.969680921064644 - type: euclidean_pearson value: 28.75506415195721 - type: euclidean_spearman value: 27.969593815056058 - type: manhattan_pearson value: 28.90608040712011 - type: manhattan_spearman value: 28.07097299964309 - task: type: STS dataset: type: C-MTEB/QBQTC name: MTEB QBQTC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 33.4112661812038 - type: cos_sim_spearman value: 35.192765228905174 - type: euclidean_pearson value: 33.57803958232971 - type: euclidean_spearman value: 35.19270413260232 - type: manhattan_pearson value: 33.75933288702631 - type: manhattan_spearman value: 35.362780488430126 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (zh) config: zh split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 62.178764479940206 - type: cos_sim_spearman value: 63.644049344272155 - type: euclidean_pearson value: 61.97852518030118 - type: euclidean_spearman value: 63.644049344272155 - type: manhattan_pearson value: 62.3931275533103 - type: manhattan_spearman value: 63.68720814152202 - task: type: STS dataset: type: C-MTEB/STSB name: MTEB STSB config: default split: test revision: None metrics: - type: cos_sim_pearson value: 81.09847341753118 - type: cos_sim_spearman value: 81.46211495319093 - type: euclidean_pearson value: 80.97905808856734 - type: euclidean_spearman value: 81.46177732221445 - type: manhattan_pearson value: 80.8737913286308 - type: manhattan_spearman value: 81.41142532907402 - task: type: Reranking dataset: type: C-MTEB/T2Reranking name: MTEB T2Reranking config: default split: dev revision: None metrics: - type: map value: 66.36295416100998 - type: mrr value: 76.42041058129412 - task: type: Retrieval dataset: type: C-MTEB/T2Retrieval name: MTEB T2Retrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 26.898 - type: map_at_10 value: 75.089 - type: map_at_100 value: 78.786 - type: map_at_1000 value: 78.86 - type: map_at_3 value: 52.881 - type: map_at_5 value: 64.881 - type: mrr_at_1 value: 88.984 - type: mrr_at_10 value: 91.681 - type: mrr_at_100 value: 91.77300000000001 - type: mrr_at_1000 value: 91.777 - type: mrr_at_3 value: 91.205 - type: mrr_at_5 value: 91.486 - type: ndcg_at_1 value: 88.984 - type: ndcg_at_10 value: 83.083 - type: ndcg_at_100 value: 86.955 - type: ndcg_at_1000 value: 87.665 - type: ndcg_at_3 value: 84.661 - type: ndcg_at_5 value: 83.084 - type: precision_at_1 value: 88.984 - type: precision_at_10 value: 41.311 - type: precision_at_100 value: 4.978 - type: precision_at_1000 value: 0.515 - type: precision_at_3 value: 74.074 - type: precision_at_5 value: 61.956999999999994 - type: recall_at_1 value: 26.898 - type: recall_at_10 value: 82.03200000000001 - type: recall_at_100 value: 94.593 - type: recall_at_1000 value: 98.188 - type: recall_at_3 value: 54.647999999999996 - type: recall_at_5 value: 68.394 - task: type: Classification dataset: type: C-MTEB/TNews-classification name: MTEB TNews config: default split: validation revision: None metrics: - type: accuracy value: 53.648999999999994 - type: f1 value: 51.87788185753318 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringP2P name: MTEB ThuNewsClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 68.81293224496076 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringS2S name: MTEB ThuNewsClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 63.60504270553153 - task: type: Retrieval dataset: type: C-MTEB/VideoRetrieval name: MTEB VideoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 59.3 - type: map_at_10 value: 69.89 - type: map_at_100 value: 70.261 - type: map_at_1000 value: 70.27 - type: map_at_3 value: 67.93299999999999 - type: map_at_5 value: 69.10300000000001 - type: mrr_at_1 value: 59.3 - type: mrr_at_10 value: 69.89 - type: mrr_at_100 value: 70.261 - type: mrr_at_1000 value: 70.27 - type: mrr_at_3 value: 67.93299999999999 - type: mrr_at_5 value: 69.10300000000001 - type: ndcg_at_1 value: 59.3 - type: ndcg_at_10 value: 74.67099999999999 - type: ndcg_at_100 value: 76.371 - type: ndcg_at_1000 value: 76.644 - type: ndcg_at_3 value: 70.678 - type: ndcg_at_5 value: 72.783 - type: precision_at_1 value: 59.3 - type: precision_at_10 value: 8.95 - type: precision_at_100 value: 0.972 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 26.200000000000003 - type: precision_at_5 value: 16.74 - type: recall_at_1 value: 59.3 - type: recall_at_10 value: 89.5 - type: recall_at_100 value: 97.2 - type: recall_at_1000 value: 99.4 - type: recall_at_3 value: 78.60000000000001 - type: recall_at_5 value: 83.7 - task: type: Classification dataset: type: C-MTEB/waimai-classification name: MTEB Waimai config: default split: test revision: None metrics: - type: accuracy value: 88.07000000000001 - type: ap value: 72.68881791758656 - type: f1 value: 86.647906274628 language: - en license: mit --- # gte-base-zh General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281) The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer different sizes of models for both Chinese and English Languages. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. ## Model List | Models | Language | Max Sequence Length | Dimension | Model Size | |:-----: | :-----: |:-----: |:-----: |:-----: | |[GTE-large-zh](https://huggingface.co/thenlper/gte-large-zh) | Chinese | 512 | 1024 | 0.67GB | |[GTE-base-zh](https://huggingface.co/thenlper/gte-base-zh) | Chinese | 512 | 1024 | 0.67GB | |[GTE-small-zh](https://huggingface.co/thenlper/gte-small-zh) | Chinese | 512 | 1024 | 0.67GB | |[GTE-large](https://huggingface.co/thenlper/gte-large) | English | 512 | 1024 | 0.67GB | |[GTE-base](https://huggingface.co/thenlper/gte-base) | English | 512 | 1024 | 0.67GB | |[GTE-small](https://huggingface.co/thenlper/gte-small) | English | 512 | 1024 | 0.67GB | ## Metrics We compared the performance of the GTE models with other popular text embedding models on the MTEB (CMTEB for Chinese language) benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). - Evaluation results on CMTEB | Model | Model Size (GB) | Embedding Dimensions | Sequence Length | Average (35 datasets) | Classification (9 datasets) | Clustering (4 datasets) | Pair Classification (2 datasets) | Reranking (4 datasets) | Retrieval (8 datasets) | STS (8 datasets) | | ------------------- | -------------- | -------------------- | ---------------- | --------------------- | ------------------------------------ | ------------------------------ | --------------------------------------- | ------------------------------ | ---------------------------- | ------------------------ | | **gte-large-zh** | 0.65 | 1024 | 512 | **66.72** | 71.34 | 53.07 | 81.14 | 67.42 | 72.49 | 57.82 | | gte-base-zh | 0.20 | 768 | 512 | 65.92 | 71.26 | 53.86 | 80.44 | 67.00 | 71.71 | 55.96 | | stella-large-zh-v2 | 0.65 | 1024 | 1024 | 65.13 | 69.05 | 49.16 | 82.68 | 66.41 | 70.14 | 58.66 | | stella-large-zh | 0.65 | 1024 | 1024 | 64.54 | 67.62 | 48.65 | 78.72 | 65.98 | 71.02 | 58.3 | | bge-large-zh-v1.5 | 1.3 | 1024 | 512 | 64.53 | 69.13 | 48.99 | 81.6 | 65.84 | 70.46 | 56.25 | | stella-base-zh-v2 | 0.21 | 768 | 1024 | 64.36 | 68.29 | 49.4 | 79.96 | 66.1 | 70.08 | 56.92 | | stella-base-zh | 0.21 | 768 | 1024 | 64.16 | 67.77 | 48.7 | 76.09 | 66.95 | 71.07 | 56.54 | | piccolo-large-zh | 0.65 | 1024 | 512 | 64.11 | 67.03 | 47.04 | 78.38 | 65.98 | 70.93 | 58.02 | | piccolo-base-zh | 0.2 | 768 | 512 | 63.66 | 66.98 | 47.12 | 76.61 | 66.68 | 71.2 | 55.9 | | gte-small-zh | 0.1 | 512 | 512 | 60.08 | 64.49 | 48.95 | 69.99 | 66.21 | 65.50 | 49.72 | | bge-small-zh-v1.5 | 0.1 | 512 | 512 | 57.82 | 63.96 | 44.18 | 70.4 | 60.92 | 61.77 | 49.1 | | m3e-base | 0.41 | 768 | 512 | 57.79 | 67.52 | 47.68 | 63.99 | 59.54| 56.91 | 50.47 | |text-embedding-ada-002(openai) | - | 1536| 8192 | 53.02 | 64.31 | 45.68 | 69.56 | 54.28 | 52.0 | 43.35 | ## Usage Code example ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel input_texts = [ "中国的首都是哪里", "你喜欢去哪里旅游", "北京", "今天中午吃什么" ] tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-base-zh") model = AutoModel.from_pretrained("thenlper/gte-base-zh") # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = outputs.last_hidden_state[:, 0] # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:1] @ embeddings[1:].T) * 100 print(scores.tolist()) ``` Use with sentence-transformers: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim sentences = ['中国的首都是哪里', '中国的首都是北京'] model = SentenceTransformer('thenlper/gte-base-zh') embeddings = model.encode(sentences) print(cos_sim(embeddings[0], embeddings[1])) ``` ### Limitation This model exclusively caters to Chinese texts, and any lengthy texts will be truncated to a maximum of 512 tokens. ### Citation If you find our paper or models helpful, please consider citing them as follows: ``` @article{li2023towards, title={Towards general text embeddings with multi-stage contrastive learning}, author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan}, journal={arXiv preprint arXiv:2308.03281}, year={2023} } ```
facebook/wmt19-ru-en
facebook
"2023-03-16T20:03:21Z"
22,307
18
transformers
[ "transformers", "pytorch", "safetensors", "fsmt", "text2text-generation", "translation", "wmt19", "facebook", "ru", "en", "dataset:wmt19", "arxiv:1907.06616", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
translation
"2022-03-02T23:29:05Z"
--- language: - ru - en tags: - translation - wmt19 - facebook license: apache-2.0 datasets: - wmt19 metrics: - bleu thumbnail: https://huggingface.co/front/thumbnails/facebook.png --- # FSMT ## Model description This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for ru-en. For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616). The abbreviation FSMT stands for FairSeqMachineTranslation All four models are available: * [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru) * [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en) * [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de) * [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en) ## Intended uses & limitations #### How to use ```python from transformers import FSMTForConditionalGeneration, FSMTTokenizer mname = "facebook/wmt19-ru-en" tokenizer = FSMTTokenizer.from_pretrained(mname) model = FSMTForConditionalGeneration.from_pretrained(mname) input = "Машинное обучение - это здорово, не так ли?" input_ids = tokenizer.encode(input, return_tensors="pt") outputs = model.generate(input_ids) decoded = tokenizer.decode(outputs[0], skip_special_tokens=True) print(decoded) # Machine learning is great, isn't it? ``` #### Limitations and bias - The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981) ## Training data Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616). ## Eval results pair | fairseq | transformers -------|---------|---------- ru-en | [41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937) | 39.20 The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support: - model ensemble, therefore the best performing checkpoint was ported (``model4.pt``). - re-ranking The score was calculated using this code: ```bash git clone https://github.com/huggingface/transformers cd transformers export PAIR=ru-en export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=8 export NUM_BEAMS=15 mkdir -p $DATA_DIR sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target echo $PAIR PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS ``` note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`. ## Data Sources - [training, etc.](http://www.statmt.org/wmt19/) - [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561) ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020}, title={Facebook FAIR's WMT19 News Translation Task Submission}, author={Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}, booktitle={Proc. of WMT}, } ``` ## TODO - port model ensemble (fairseq uses 4 model checkpoints)
manueldeprada/FactCC
manueldeprada
"2023-10-17T13:52:05Z"
22,281
3
transformers
[ "transformers", "pytorch", "tf", "jax", "bert", "text-classification", "en", "dataset:cnn_dailymail", "arxiv:1910.12840", "license:bsd-3-clause-clear", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
"2023-10-17T12:31:36Z"
--- license: bsd-3-clause-clear datasets: - cnn_dailymail language: - en metrics: - f1 --- # FactCC factuality prediction model Original paper: [Evaluating the Factual Consistency of Abstractive Text Summarization](https://arxiv.org/abs/1910.12840) This is a more modern implementation of the model and code from [the original github repo](https://github.com/salesforce/factCC) This model is trained to predict whether a summary is factual with respect to the original text. Basic usage: ``` from transformers import BertForSequenceClassification, BertTokenizer model_path = 'manueldeprada/FactCC' tokenizer = BertTokenizer.from_pretrained(model_path) model = BertForSequenceClassification.from_pretrained(model_path) text='''The US has "passed the peak" on new coronavirus cases, the White House reported. They predict that some states would reopen this month. The US has over 637,000 confirmed Covid-19 cases and over 30,826 deaths, the highest for any country in the world.''' wrong_summary = '''The pandemic has almost not affected the US''' input_dict = tokenizer(text, wrong_summary, max_length=512, padding='max_length', truncation='only_first', return_tensors='pt') logits = model(**input_dict).logits pred = logits.argmax(dim=1) model.config.id2label[pred.item()] # prints: INCORRECT ``` It can also be used with a pipeline. Beware that since pipelines are not thought to be used with pair of sentences, and you have to use this double-list hack: ``` >>> from transformers import pipeline >>> pipe=pipeline(model="manueldeprada/FactCC") >>> pipe([[[text1,summary1]],[[text2,summary2]]],truncation='only_first',padding='max_length') # output [{'label': 'INCORRECT', 'score': 0.9979124665260315}, {'label': 'CORRECT', 'score': 0.879124665260315}] ``` Example on how to perform batched inference to reproduce authors results on the test set: ``` def batched_FactCC(text_l, summary_l, max_length=512): input_dict = tokenizer(text_l, summary_l, max_length=max_length, padding='max_length', truncation='only_first', return_tensors='pt') with torch.no_grad(): logits = model(**input_dict).logits preds = logits.argmax(dim=1) return logits, preds texts = [] claims = [] labels = [] with open('factCC/annotated_data/test/data-dev.jsonl', 'r') as file: for line in file: obj = json.loads(line) # Load the JSON data from each line texts.append(obj['text']) claims.append(obj['claim']) labels.append(model.config.label2id[o['label']]) preds = [] batch_size = 8 for i in tqdm(range(0, len(texts), batch_size)): batch_texts = texts[i:i+batch_size] batch_claims = claims[i:i+batch_size] _, preds = fact_cc(batch_texts, batch_claims) preds.extend(preds.tolist()) print(f"F1 micro: {f1_score(labels, preds, average='micro')}") print(f"Balanced accuracy: {balanced_accuracy_score(labels, preds)}") ```
dataautogpt3/TempestV0.1
dataautogpt3
"2024-03-11T12:35:52Z"
22,273
103
diffusers
[ "diffusers", "text-to-image", "license:gpl-3.0", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]
text-to-image
"2024-03-08T15:07:17Z"
--- pipeline_tag: text-to-image widget: - text: >- Food photography style photo RAW,piece of fried grilled meat, splashes of ketchup and mustard sauce, (rosemary), spices, exceptional shallow depth-of-field capabilities, atmospheric haze blur,vivid colors,high quality textures of materials, volumetric textures, coating textures, metal textures . Appetizing, professional, culinary, high-resolution, commercial, highly detailed output: url: steakj.png - text: >- amazing quality, sci-fi, desert landscape, a massive dragon crashing through the dunes, epic scene, fabulous, knight running away output: url: dragon.png - text: >- Super Closeup Portrait, action shot, Profoundly dark whitish meadow, glass flowers, Stains, space grunge style, Jeanne d'Arc wearing White Olive green used styled Cotton frock, Wielding thin silver sword, Sci-fi vibe, dirty, noisy, Vintage monk style, very detailed, hd, cinematic, 2k output: url: final_output_02499_.png - text: >- lizard output: url: liz.jpeg license: gpl-3.0 --- <Gallery /> Who needs upscalers? - The TempestV0.1 Initiative is a powerhouse in image generation, leveraging an unparalleled dataset of over 6 million images. The collection's vast scale, with resolutions from 1400x2100 to 4800x7200, encompasses 200GB of high-quality content. With a groundbreaking 3 million iterations in its training cycle, TempestV0.1 underscores the rigorous effort input by its creator. This training intensity notably eclipses that of all other contemporarie models. TempestV0.1 shatters the conventional limits of image generation, particularly in delivering unparalleled detail and texture. due to the distrabution diffrenece loras will not work with this model at higher resolutions. _____________________________________________________________________________________________________________________________________________________ What is the diffrence between Base and Artistic? - Base is the pure 100% trained model without any special loras or models throw into the mix. suggested settings for Base are: cfg: 7 to 8 steps: 60 to 80 Artistic is less overall cohesive at larger sizes but has much more flare and stylistic promise. (Proteus+Tempest=Artistic Tempest) suggested settings for Artistic are: cfg: 3 to 8 steps: 60 to 80 both of these checkpoints have there place and are seperate for ease of understanding for the user. supported sizes: | 2048x1024 | 1920x1088 | _____________________________________________________________________________________________________________________________________________________ please support the work I do through donating to me on: https://www.buymeacoffee.com/DataVoid or following me on https://twitter.com/DataPlusEngine
OpenGVLab/Mini-InternVL-Chat-2B-V1-5
OpenGVLab
"2024-05-29T14:27:11Z"
22,266
51
transformers
[ "transformers", "safetensors", "internvl_chat", "feature-extraction", "visual-question-answering", "custom_code", "dataset:laion/laion2B-en", "dataset:laion/laion-coco", "dataset:laion/laion2B-multi", "dataset:kakaobrain/coyo-700m", "dataset:conceptual_captions", "dataset:wanng/wukong100m", "arxiv:2312.14238", "arxiv:2404.16821", "license:mit", "region:us" ]
visual-question-answering
"2024-05-13T16:32:04Z"
--- license: mit datasets: - laion/laion2B-en - laion/laion-coco - laion/laion2B-multi - kakaobrain/coyo-700m - conceptual_captions - wanng/wukong100m pipeline_tag: visual-question-answering --- # Model Card for Mini-InternVL-Chat-2B-V1-5 <center> <p><img src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/pvfKc16O-ej91632FHaIK.png" style="width:80%;" alt="image/png"></p> </center> [\[🆕 Blog\]](https://internvl.github.io/blog/) [\[📜 InternVL 1.0 Paper\]](https://arxiv.org/abs/2312.14238) [\[📜 InternVL 1.5 Report\]](https://arxiv.org/abs/2404.16821) [\[🗨️ Chat Demo\]](https://internvl.opengvlab.com/) [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL) [\[🚀 Quick Start\]](#model-usage) [\[🌐 Community-hosted API\]](https://rapidapi.com/adushar1320/api/internvl-chat) [\[📖 中文解读\]](https://zhuanlan.zhihu.com/p/675877376) You can run multimodal large models using a 1080Ti now. We are delighted to introduce the Mini-InternVL-Chat series. In the era of large language models, many researchers have started to focus on smaller language models, such as Gemma-2B, Qwen-1.8B, and InternLM2-1.8B. Inspired by their efforts, we have distilled our vision foundation model [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) down to 300M and used [InternLM2-Chat-1.8B](https://huggingface.co/internlm/internlm2-chat-1_8b) or [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) as our language model. This resulted in a small multimodal model with excellent performance. As shown in the figure below, we adopted the same model architecture as InternVL 1.5. We simply replaced the original InternViT-6B with InternViT-300M and InternLM2-Chat-20B with InternLM2-Chat-1.8B / Phi-3-mini-128k-instruct. For training, we used the same data as InternVL 1.5 to train this smaller model. Additionally, due to the lower training costs of smaller models, we used a context length of 8K during training. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64006c09330a45b03605bba3/rDyoe66Sqev44T0wsP5Z7.png) ## Model Details - **Model Type:** multimodal large language model (MLLM) - **Model Stats:** - Architecture: [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px) + MLP + [InternLM2-Chat-1.8B](https://huggingface.co/internlm/internlm2-chat-1_8b) - Image size: dynamic resolution, max to 40 tiles of 448 x 448 (4K resolution). - Params: 2.2B - **Training Strategy:** - Learnable component in the pretraining stage: ViT + MLP - Learnable component in the finetuning stage: ViT + MLP + LLM - For more details on training hyperparameters, take a look at our code: [pretrain](<>) | [finetune](<>) ## Released Models | Model | Vision Foundation Model | Release Date | Note | | :----------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | :----------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------- | | InternVL-Chat-V1-5(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5)) | InternViT-6B-448px-V1-5(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5)) | 2024.04.18 | support 4K image; super strong OCR; Approaching the performance of GPT-4V and Gemini Pro on various benchmarks like MMMU, DocVQA, ChartQA, MathVista, etc. (🔥new) | | InternVL-Chat-V1-2-Plus(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2-Plus) ) | InternViT-6B-448px-V1-2(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2)) | 2024.02.21 | more SFT data and stronger | | InternVL-Chat-V1-2(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2) ) | InternViT-6B-448px-V1-2(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2)) | 2024.02.11 | scaling up LLM to 34B | | InternVL-Chat-V1-1(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-1)) | InternViT-6B-448px-V1-0(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-0)) | 2024.01.24 | support Chinese and stronger OCR | ## Performance ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/BbsilHS8PjwZwlc330_g4.png) ## Model Usage We provide an example code to run Mini-InternVL-Chat-2B-V1-5 using `transformers`. You can also use our [online demo](https://internvl.opengvlab.com/) for a quick experience of this model. > Please use transformers==4.37.2 to ensure the model works normally. ```python from transformers import AutoTokenizer, AutoModel import torch import torchvision.transforms as T from PIL import Image from torchvision.transforms.functional import InterpolationMode IMAGENET_MEAN = (0.485, 0.456, 0.406) IMAGENET_STD = (0.229, 0.224, 0.225) def build_transform(input_size): MEAN, STD = IMAGENET_MEAN, IMAGENET_STD transform = T.Compose([ T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img), T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC), T.ToTensor(), T.Normalize(mean=MEAN, std=STD) ]) return transform def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size): best_ratio_diff = float('inf') best_ratio = (1, 1) area = width * height for ratio in target_ratios: target_aspect_ratio = ratio[0] / ratio[1] ratio_diff = abs(aspect_ratio - target_aspect_ratio) if ratio_diff < best_ratio_diff: best_ratio_diff = ratio_diff best_ratio = ratio elif ratio_diff == best_ratio_diff: if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]: best_ratio = ratio return best_ratio def dynamic_preprocess(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False): orig_width, orig_height = image.size aspect_ratio = orig_width / orig_height # calculate the existing image aspect ratio target_ratios = set( (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if i * j <= max_num and i * j >= min_num) target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) # find the closest aspect ratio to the target target_aspect_ratio = find_closest_aspect_ratio( aspect_ratio, target_ratios, orig_width, orig_height, image_size) # calculate the target width and height target_width = image_size * target_aspect_ratio[0] target_height = image_size * target_aspect_ratio[1] blocks = target_aspect_ratio[0] * target_aspect_ratio[1] # resize the image resized_img = image.resize((target_width, target_height)) processed_images = [] for i in range(blocks): box = ( (i % (target_width // image_size)) * image_size, (i // (target_width // image_size)) * image_size, ((i % (target_width // image_size)) + 1) * image_size, ((i // (target_width // image_size)) + 1) * image_size ) # split the image split_img = resized_img.crop(box) processed_images.append(split_img) assert len(processed_images) == blocks if use_thumbnail and len(processed_images) != 1: thumbnail_img = image.resize((image_size, image_size)) processed_images.append(thumbnail_img) return processed_images def load_image(image_file, input_size=448, max_num=6): image = Image.open(image_file).convert('RGB') transform = build_transform(input_size=input_size) images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num) pixel_values = [transform(image) for image in images] pixel_values = torch.stack(pixel_values) return pixel_values path = "OpenGVLab/Mini-InternVL-Chat-2B-V1-5" model = AutoModel.from_pretrained( path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, trust_remote_code=True).eval().cuda() tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True) # set the max number of tiles in `max_num` pixel_values = load_image('./examples/image1.jpg', max_num=6).to(torch.bfloat16).cuda() generation_config = dict( num_beams=1, max_new_tokens=512, do_sample=False, ) # single-round single-image conversation question = "请详细描述图片" # Please describe the picture in detail response = model.chat(tokenizer, pixel_values, question, generation_config) print(question, response) # multi-round single-image conversation question = "请详细描述图片" # Please describe the picture in detail response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True) print(question, response) question = "请根据图片写一首诗" # Please write a poem according to the picture response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True) print(question, response) # multi-round multi-image conversation pixel_values1 = load_image('./examples/image1.jpg', max_num=6).to(torch.bfloat16).cuda() pixel_values2 = load_image('./examples/image2.jpg', max_num=6).to(torch.bfloat16).cuda() pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0) question = "详细描述这两张图片" # Describe the two pictures in detail response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True) print(question, response) question = "这两张图片的相同点和区别分别是什么" # What are the similarities and differences between these two pictures response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True) print(question, response) # batch inference (single image per sample) pixel_values1 = load_image('./examples/image1.jpg', max_num=6).to(torch.bfloat16).cuda() pixel_values2 = load_image('./examples/image2.jpg', max_num=6).to(torch.bfloat16).cuda() image_counts = [pixel_values1.size(0), pixel_values2.size(0)] pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0) questions = ["Describe the image in detail."] * len(image_counts) responses = model.batch_chat(tokenizer, pixel_values, image_counts=image_counts, questions=questions, generation_config=generation_config) for question, response in zip(questions, responses): print(question) print(response) ``` ## Citation If you find this project useful in your research, please consider citing: ```BibTeX @article{chen2023internvl, title={InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks}, author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and Li, Bin and Luo, Ping and Lu, Tong and Qiao, Yu and Dai, Jifeng}, journal={arXiv preprint arXiv:2312.14238}, year={2023} } @article{chen2024far, title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites}, author={Chen, Zhe and Wang, Weiyun and Tian, Hao and Ye, Shenglong and Gao, Zhangwei and Cui, Erfei and Tong, Wenwen and Hu, Kongzhi and Luo, Jiapeng and Ma, Zheng and others}, journal={arXiv preprint arXiv:2404.16821}, year={2024} } ``` ## License This project is released under the MIT license. ## Acknowledgement InternVL is built with reference to the code of the following projects: [OpenAI CLIP](https://github.com/openai/CLIP), [Open CLIP](https://github.com/mlfoundations/open_clip), [CLIP Benchmark](https://github.com/LAION-AI/CLIP_benchmark), [EVA](https://github.com/baaivision/EVA/tree/master), [InternImage](https://github.com/OpenGVLab/InternImage), [ViT-Adapter](https://github.com/czczup/ViT-Adapter), [MMSegmentation](https://github.com/open-mmlab/mmsegmentation), [Transformers](https://github.com/huggingface/transformers), [DINOv2](https://github.com/facebookresearch/dinov2), [BLIP-2](https://github.com/salesforce/LAVIS/tree/main/projects/blip2), [Qwen-VL](https://github.com/QwenLM/Qwen-VL/tree/master/eval_mm), and [LLaVA-1.5](https://github.com/haotian-liu/LLaVA). Thanks for their awesome work!
TheDrummer/Fook-Yi-34B-32K-v1-GGUF
TheDrummer
"2024-06-29T14:45:20Z"
22,190
5
null
[ "gguf", "not-for-all-audiences", "license:cc-by-nc-4.0", "region:us" ]
null
"2024-06-28T13:53:31Z"
--- license: cc-by-nc-4.0 tags: - not-for-all-audiences --- ## Join our Discord! https://discord.gg/Nbv9pQ88Xb ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/HjVYV2h_YTL9P-insb7fz.png) With help from Sao's armpits and Daddy Eric, [BeaverAI](https://huggingface.co/BeaverAI) presents... # Fook Yi 34B 32K v1 *The 34B model we need, the 32K context we deserve!* (I'm calling you out, Meta) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/elUMNgQLQMG97pz-y9r4k.png) https://www.youtube.com/watch?v=RvBJ7rFsrhA *A smart RP model that keeps on giving. Finetuned by yours truly.* ## Links - Original: https://huggingface.co/TheDrummer/Fook-Yi-34B-32K-v1 - GGUF: https://huggingface.co/TheDrummer/Fook-Yi-34B-32K-v1-GGUF - EXL2: https://huggingface.co/BeaverAI/Fook-Yi-34B-32K-v1-exl2/tree/3.0bpw ## What's This? - Fook Yi is an RP finetune with less focus on ERP. - I plan on finetuning it further with moist sauce. ## Usage - Use completion, ChatML, or even Alpaca! ## Reviews of Fook Yi - Smart and crazy logical - ERP capable - Not very slopped - Holds up at 32K ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/bINUt7GO_xEOwZc0T8aBC.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/Fa_WVvW089k74Sjm5Admj.png) ![image/gif](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/OqN_MBE6XjSXTlvzQxBbj.gif) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/Ll8CA5RR7ugTi72P2HBb8.png)
timm/vit_small_patch16_224.augreg_in21k
timm
"2023-05-06T00:28:07Z"
22,188
0
timm
[ "timm", "pytorch", "safetensors", "image-classification", "dataset:imagenet-21k", "arxiv:2106.10270", "arxiv:2010.11929", "license:apache-2.0", "region:us" ]
image-classification
"2022-12-22T07:53:43Z"
--- tags: - image-classification - timm library_name: timm license: apache-2.0 datasets: - imagenet-21k --- # Model card for vit_small_patch16_224.augreg_in21k A Vision Transformer (ViT) image classification model. Trained on ImageNet-21k (with additional augmentation and regularization) in JAX by paper authors, ported to PyTorch by Ross Wightman. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 30.1 - GMACs: 4.3 - Activations (M): 8.3 - Image size: 224 x 224 - **Papers:** - How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: https://arxiv.org/abs/2106.10270 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - **Dataset:** ImageNet-21k - **Original:** https://github.com/google-research/vision_transformer ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('vit_small_patch16_224.augreg_in21k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'vit_small_patch16_224.augreg_in21k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 197, 384) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @article{steiner2021augreg, title={How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers}, author={Steiner, Andreas and Kolesnikov, Alexander and and Zhai, Xiaohua and Wightman, Ross and Uszkoreit, Jakob and Beyer, Lucas}, journal={arXiv preprint arXiv:2106.10270}, year={2021} } ``` ```bibtex @article{dosovitskiy2020vit, title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale}, author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil}, journal={ICLR}, year={2021} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```
allegro/herbert-base-cased
allegro
"2022-06-09T11:36:39Z"
22,156
12
transformers
[ "transformers", "pytorch", "tf", "jax", "bert", "feature-extraction", "herbert", "pl", "license:cc-by-4.0", "endpoints_compatible", "text-embeddings-inference", "region:us" ]
feature-extraction
"2022-03-02T23:29:05Z"
--- language: pl tags: - herbert license: cc-by-4.0 --- # HerBERT **[HerBERT](https://en.wikipedia.org/wiki/Zbigniew_Herbert)** is a BERT-based Language Model trained on Polish corpora using Masked Language Modelling (MLM) and Sentence Structural Objective (SSO) with dynamic masking of whole words. For more details, please refer to: [HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish](https://www.aclweb.org/anthology/2021.bsnlp-1.1/). Model training and experiments were conducted with [transformers](https://github.com/huggingface/transformers) in version 2.9. ## Corpus HerBERT was trained on six different corpora available for Polish language: | Corpus | Tokens | Documents | | :------ | ------: | ------: | | [CCNet Middle](https://github.com/facebookresearch/cc_net) | 3243M | 7.9M | | [CCNet Head](https://github.com/facebookresearch/cc_net) | 2641M | 7.0M | | [National Corpus of Polish](http://nkjp.pl/index.php?page=14&lang=1)| 1357M | 3.9M | | [Open Subtitles](http://opus.nlpl.eu/OpenSubtitles-v2018.php) | 1056M | 1.1M | [Wikipedia](https://dumps.wikimedia.org/) | 260M | 1.4M | | [Wolne Lektury](https://wolnelektury.pl/) | 41M | 5.5k | ## Tokenizer The training dataset was tokenized into subwords using a character level byte-pair encoding (``CharBPETokenizer``) with a vocabulary size of 50k tokens. The tokenizer itself was trained with a [tokenizers](https://github.com/huggingface/tokenizers) library. We kindly encourage you to use the ``Fast`` version of the tokenizer, namely ``HerbertTokenizerFast``. ## Usage Example code: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("allegro/herbert-base-cased") model = AutoModel.from_pretrained("allegro/herbert-base-cased") output = model( **tokenizer.batch_encode_plus( [ ( "A potem szedł środkiem drogi w kurzawie, bo zamiatał nogami, ślepy dziad prowadzony przez tłustego kundla na sznurku.", "A potem leciał od lasu chłopak z butelką, ale ten ujrzawszy księdza przy drodze okrążył go z dala i biegł na przełaj pól do karczmy." ) ], padding='longest', add_special_tokens=True, return_tensors='pt' ) ) ``` ## License CC BY 4.0 ## Citation If you use this model, please cite the following paper: ``` @inproceedings{mroczkowski-etal-2021-herbert, title = "{H}er{BERT}: Efficiently Pretrained Transformer-based Language Model for {P}olish", author = "Mroczkowski, Robert and Rybak, Piotr and Wr{\\'o}blewska, Alina and Gawlik, Ireneusz", booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing", month = apr, year = "2021", address = "Kiyv, Ukraine", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.bsnlp-1.1", pages = "1--10", } ``` ## Authors The model was trained by [**Machine Learning Research Team at Allegro**](https://ml.allegro.tech/) and [**Linguistic Engineering Group at Institute of Computer Science, Polish Academy of Sciences**](http://zil.ipipan.waw.pl/). You can contact us at: <a href="mailto:[email protected]">[email protected]</a>
l3utterfly/phi-2-layla-v1-chatml-gguf
l3utterfly
"2024-03-11T08:44:28Z"
22,144
8
null
[ "gguf", "license:mit", "region:us" ]
null
"2024-03-11T07:40:32Z"
--- license: mit --- GGUF + quants for: https://huggingface.co/l3utterfly/phi-2-layla-v1-chatml
QuantFactory/L3-Aethora-15B-V2-GGUF
QuantFactory
"2024-06-28T11:46:58Z"
22,140
0
transformers
[ "transformers", "gguf", "text-generation", "en", "dataset:TheSkullery/Aether-Lite-v1.8.1", "base_model:ZeusLabs/L3-Aethora-15B-V2", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us" ]
text-generation
"2024-06-28T09:10:24Z"
--- license: cc-by-sa-4.0 datasets: - TheSkullery/Aether-Lite-v1.8.1 language: - en base_model: ZeusLabs/L3-Aethora-15B-V2 library_name: transformers pipeline_tag: text-generation --- # QuantFactory/L3-Aethora-15B-V2-GGUF This is quantized version of [ZeusLabs/L3-Aethora-15B-V2](https://huggingface.co/ZeusLabs/L3-Aethora-15B-V2) created using llama.cpp # Model Description <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>L3-Aethora-15B v2 Data Card</title> <link href="https://fonts.googleapis.com/css2?family=Quicksand:wght@400;500;600&display=swap" rel="stylesheet"> <style> body, html { height: 100%; margin: 0; padding: 0; font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #0a1128 0%, #1c2541 100%); color: #e0e1dd; font-size: 16px; } .container { width: 100%; height: 100%; padding: 20px; margin: 0; background-color: rgba(255, 255, 255, 0.05); border-radius: 12px; box-shadow: 0 4px 10px rgba(0, 0, 0, 0.3); backdrop-filter: blur(10px); border: 1px solid rgba(255, 255, 255, 0.1); } .header h1 { font-size: 28px; color: #4cc9f0; margin: 0 0 20px 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); } .update-section h2 { font-size: 24px; color: #7209b7; } .update-section p { font-size: 16px; line-height: 1.6; color: #e0e1dd; } .info img { width: 100%; border-radius: 10px; margin-bottom: 15px; } a { color: #4cc9f0; text-decoration: none; } a:hover { color: #f72585; } .button { display: inline-block; background-color: #3a0ca3; color: #e0e1dd; padding: 10px 20px; border-radius: 5px; cursor: pointer; text-decoration: none; } .button:hover { background-color: #7209b7; } pre { background-color: #1c2541; padding: 10px; border-radius: 5px; overflow-x: auto; } code { font-family: 'Courier New', monospace; color: #e0e1dd; } </style> </head> <body> <div class="container"> <div class="header"> <h1>L3-Aethora-15B v2</h1> </div> <div class="info"> <img src="https://cdn-uploads.huggingface.co/production/uploads/64545af5ec40bbbd01242ca6/yJpwVd5UTnAVDoEPVVCS1.png"> <h2>Presented by:</h2> <p><strong>Creators: <a href="https://huggingface.co/ZeusLabs" target="_blank"> ZeusLabs</a> </p></strong> <ul> <li><a href="https://huggingface.co/steelskull" target="_blank">Steelskull</a></p></li> <li><a href="https://huggingface.co/elinas" target="_blank">Elinas</a></p></li> </ul> <p><strong>Dataset:</strong> <a href="https://huggingface.co/datasets/TheSkullery/Aether-Lite-V1.8.1" target="_blank">Theskullery/Aether-Lite-V1.8.1</a></p> <p><strong>Trained:</strong> 4 x A100 for 17.5 hours on 125k samples</p> <p><strong>Sponsored by:</strong> Garg (@g4rg)</p> <h2>About L3-Aethora-15B v2:</h2> <pre><code> L3 = Llama3 </code></pre> <p>L3-Aethora-15B v2 is an advanced language model built upon the Llama 3 architecture. It employs state-of-the-art training techniques and a curated dataset to deliver enhanced performance across a wide range of tasks.</p> <p>(Thank you all for the interest! the model has <strong>surpassed 150k downloads</strong> on all formats!)</p> <h4>Quants:</h4> <ul> <p>GGUF:</p> <li>@Mradermacher: <a href="https://huggingface.co/mradermacher/L3-Aethora-15B-V2-GGUF" target="_blank">L3-Aethora-15B-V2-GGUF</a></li> <li>@Bullerwins: <a href="https://huggingface.co/bullerwins/L3-Aethora-15B-V2-GGUF" target="_blank">L3-Aethora-15B-V2-GGUF</a></li> <p>IMatrix-GGUF:</p> <li>@Mradermacher: <a href="https://huggingface.co/mradermacher/L3-Aethora-15B-V2-i1-GGUF" target="_blank">L3-Aethora-15B-V2-i1-GGUF</a></li> <p>GGUF-F16: (both f16.q6 and f16.q5 are smaller than q8 and perform as well as the pure f16)</p> <li>@MZeroWw: <a href="https://huggingface.co/ZeroWw/L3-Aethora-15B-V2-GGUF" target="_blank">L3-Aethora-15B-V2-GGUF-f16</a></li> <p>EXL2:</p> <li>@Bullerwins: <a href="https://huggingface.co/collections/bullerwins/l3-aethora-15b-v2-exl2-667d1f4c0204c59594ca79ae" target="_blank">L3-Aethora-15B-V2-EXL2</a></li> </ul> <h2>Training Process:</h2> <ul> <li>Base Model: elinas/Llama-3-15B-Instruct-zeroed</li> <li>Training Duration: 17.5 hours on 4 x A100 GPUs</li> <li>Training Method: LoRA (Low-Rank Adaptation)</li> <li>Epochs: 4</li> <li>Precision: BF16</li> <li>Sequence Length: 8192 tokens</li> </ul> <h2>Model Capabilities:</h2> <p>The goal of L3-Aethora-15B v2 is to have an expanded proficiency across a wide spectrum of tasks with a focus in creative writing:</p> <ul> <li><strong>Creative Writing and Storytelling:</strong> <ul> <li>Generates engaging narratives, poetry, and creative content</li> <li>Adapts writing style to various genres and tones</li> <li>Assists in plot development and character creation</li> </ul> </li> <li><strong>General Intelligence:</strong> <ul> <li>Engages in detailed discussions on medical topics and scientific concepts</li> <li>Explains complex scientific phenomena</li> <li>Assists in literature review and hypothesis generation</li> </ul> </li> <li><strong>Instructional and Educational Content:</strong> <ul> <li>Creates comprehensive tutorials and how-to guides</li> <li>Explains complex topics with clarity and appropriate depth</li> <li>Generates educational materials for various skill levels</li> </ul> </li> <li><strong>Reasoning and Problem-Solving:</strong> <ul> <li>Analyzes complex scenarios and provides logical solutions</li> <li>Engages in step-by-step problem-solving across various domains</li> <li>Offers multiple perspectives on challenging issues</li> </ul> </li> <li><strong>Contextual Understanding and Adaptability:</strong> <ul> <li>Maintains coherent, context-aware conversations across extended interactions</li> <li>Adapts communication style based on the user's preferences and needs</li> <li>Handles nuanced queries with appropriate depth and sensitivity</li> </ul> </ul> <h2>Dataset Creation Process:</h2> <p>The Aether-Lite-V1.8.1 dataset used for training L3-Aethora-15B v2 underwent a rigorous creation and curation process:</p> <ol> <li><strong>Data Collection:</strong> Aggregated from 12 diverse high-quality datasets, including: <ul> <li>jondurbin/airoboros-3.2</li> <li>jtatman/medical-sci-instruct-100k-sharegpt</li> <li>Doctor-Shotgun/no-robots-sharegpt</li> <li>QuietImpostor/Sao10K-Claude-3-Opus-Instruct-15K-ShareGPT</li> <li>TheSkullery/WizardLM_evol_instruct_v2_Filtered_Fuzzy_Dedup_ShareGPT</li> <li>TheSkullery/Gryphe-Opus-WritingPrompts-merged</li> <li>Alignment-Lab-AI/RPGuild-sharegpt-filtered</li> <li>And others, providing a rich mix of instruction, creative writing, and specialized knowledge</li> </ul> </li> <li><strong>Data Preprocessing:</strong> <ul> <li>Language Detection: Utilized a FastText language model to ensure English-language content</li> <li>Text Sanitization: Cleaned and normalized text, removing or replacing problematic characters</li> <li>Phrase Filtering: Removed specific unwanted phrases and content types</li> </ul> </li> <li><strong>Deduplication:</strong> <ul> <li>Implemented advanced fuzzy deduplication with a 95% similarity threshold</li> <li>Utilized text embeddings and cosine similarity calculations for efficient comparison</li> <li>Removed 16,250 duplicate entries, ensuring dataset uniqueness</li> </ul> </li> <li><strong>Data Balancing:</strong> <ul> <li>Carefully sampled from each source dataset to maintain diversity</li> <li>Implemented data shuffling to ensure random distribution of samples</li> </ul> </ol> <p>The final dataset comprises 125,119 high-quality, diverse samples, striking a balance between creativity, practical knowledge, and intellectual depth.</p> <p>The full dataset used has been released to the public and is avalible for all (see presented section), any ideas or recomendations are always welcome to expand on the dataset further</p> </div> </div> </body> </html>
mradermacher/Llama-3-Elyza-Youko-moe-2x8B-GGUF
mradermacher
"2024-06-28T17:13:34Z"
22,138
0
transformers
[ "transformers", "gguf", "en", "base_model:aixsatoshi/Llama-3-Elyza-Youko-moe-2x8B", "license:llama3", "endpoints_compatible", "region:us" ]
null
"2024-06-28T16:25:55Z"
--- base_model: aixsatoshi/Llama-3-Elyza-Youko-moe-2x8B language: - en library_name: transformers license: llama3 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/aixsatoshi/Llama-3-Elyza-Youko-moe-2x8B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Llama-3-Elyza-Youko-moe-2x8B-GGUF/resolve/main/Llama-3-Elyza-Youko-moe-2x8B.Q2_K.gguf) | Q2_K | 5.3 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-Elyza-Youko-moe-2x8B-GGUF/resolve/main/Llama-3-Elyza-Youko-moe-2x8B.IQ3_XS.gguf) | IQ3_XS | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-Elyza-Youko-moe-2x8B-GGUF/resolve/main/Llama-3-Elyza-Youko-moe-2x8B.Q3_K_S.gguf) | Q3_K_S | 6.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-Elyza-Youko-moe-2x8B-GGUF/resolve/main/Llama-3-Elyza-Youko-moe-2x8B.IQ3_S.gguf) | IQ3_S | 6.2 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Llama-3-Elyza-Youko-moe-2x8B-GGUF/resolve/main/Llama-3-Elyza-Youko-moe-2x8B.IQ3_M.gguf) | IQ3_M | 6.3 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-Elyza-Youko-moe-2x8B-GGUF/resolve/main/Llama-3-Elyza-Youko-moe-2x8B.Q3_K_M.gguf) | Q3_K_M | 6.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3-Elyza-Youko-moe-2x8B-GGUF/resolve/main/Llama-3-Elyza-Youko-moe-2x8B.Q3_K_L.gguf) | Q3_K_L | 7.3 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-Elyza-Youko-moe-2x8B-GGUF/resolve/main/Llama-3-Elyza-Youko-moe-2x8B.IQ4_XS.gguf) | IQ4_XS | 7.6 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-Elyza-Youko-moe-2x8B-GGUF/resolve/main/Llama-3-Elyza-Youko-moe-2x8B.Q4_K_S.gguf) | Q4_K_S | 8.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama-3-Elyza-Youko-moe-2x8B-GGUF/resolve/main/Llama-3-Elyza-Youko-moe-2x8B.Q4_K_M.gguf) | Q4_K_M | 8.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama-3-Elyza-Youko-moe-2x8B-GGUF/resolve/main/Llama-3-Elyza-Youko-moe-2x8B.Q5_K_S.gguf) | Q5_K_S | 9.6 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-Elyza-Youko-moe-2x8B-GGUF/resolve/main/Llama-3-Elyza-Youko-moe-2x8B.Q5_K_M.gguf) | Q5_K_M | 9.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-Elyza-Youko-moe-2x8B-GGUF/resolve/main/Llama-3-Elyza-Youko-moe-2x8B.Q6_K.gguf) | Q6_K | 11.3 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3-Elyza-Youko-moe-2x8B-GGUF/resolve/main/Llama-3-Elyza-Youko-moe-2x8B.Q8_0.gguf) | Q8_0 | 14.6 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
nreimers/BERT-Tiny_L-2_H-128_A-2
nreimers
"2021-05-28T11:05:21Z"
22,134
3
transformers
[ "transformers", "pytorch", "jax", "bert", "feature-extraction", "endpoints_compatible", "text-embeddings-inference", "region:us" ]
feature-extraction
"2022-03-02T23:29:05Z"
This is the BERT-Medium model from Google: https://github.com/google-research/bert#bert. A BERT model with 2 layers, 128 hidden unit size, and 2 attention heads.
google-bert/bert-large-uncased-whole-word-masking
google-bert
"2024-02-19T11:08:36Z"
22,133
18
transformers
[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1810.04805", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
"2022-03-02T23:29:04Z"
--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia --- # BERT large model (uncased) whole word masking Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference between english and English. Differently to other BERT models, this model was trained with a new technique: Whole Word Masking. In this case, all of the tokens corresponding to a word are masked at once. The overall masking rate remains the same. The training is identical -- each masked WordPiece token is predicted independently. Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. This model has the following configuration: - 24-layer - 1024 hidden dimension - 16 attention heads - 336M parameters. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-large-uncased-whole-word-masking') >>> unmasker("Hello I'm a [MASK] model.") [ { 'sequence': "[CLS] hello i'm a fashion model. [SEP]", 'score': 0.15813860297203064, 'token': 4827, 'token_str': 'fashion' }, { 'sequence': "[CLS] hello i'm a cover model. [SEP]", 'score': 0.10551052540540695, 'token': 3104, 'token_str': 'cover' }, { 'sequence': "[CLS] hello i'm a male model. [SEP]", 'score': 0.08340442180633545, 'token': 3287, 'token_str': 'male' }, { 'sequence': "[CLS] hello i'm a super model. [SEP]", 'score': 0.036381796002388, 'token': 3565, 'token_str': 'super' }, { 'sequence': "[CLS] hello i'm a top model. [SEP]", 'score': 0.03609578311443329, 'token': 2327, 'token_str': 'top' } ] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('bert-large-uncased-whole-word-masking') model = BertModel.from_pretrained("bert-large-uncased-whole-word-masking") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('bert-large-uncased-whole-word-masking') model = TFBertModel.from_pretrained("bert-large-uncased-whole-word-masking") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-large-uncased-whole-word-masking') >>> unmasker("The man worked as a [MASK].") [ { "sequence":"[CLS] the man worked as a waiter. [SEP]", "score":0.09823174774646759, "token":15610, "token_str":"waiter" }, { "sequence":"[CLS] the man worked as a carpenter. [SEP]", "score":0.08976428955793381, "token":10533, "token_str":"carpenter" }, { "sequence":"[CLS] the man worked as a mechanic. [SEP]", "score":0.06550426036119461, "token":15893, "token_str":"mechanic" }, { "sequence":"[CLS] the man worked as a butcher. [SEP]", "score":0.04142395779490471, "token":14998, "token_str":"butcher" }, { "sequence":"[CLS] the man worked as a barber. [SEP]", "score":0.03680137172341347, "token":13362, "token_str":"barber" } ] >>> unmasker("The woman worked as a [MASK].") [ { "sequence":"[CLS] the woman worked as a waitress. [SEP]", "score":0.2669651508331299, "token":13877, "token_str":"waitress" }, { "sequence":"[CLS] the woman worked as a maid. [SEP]", "score":0.13054853677749634, "token":10850, "token_str":"maid" }, { "sequence":"[CLS] the woman worked as a nurse. [SEP]", "score":0.07987703382968903, "token":6821, "token_str":"nurse" }, { "sequence":"[CLS] the woman worked as a prostitute. [SEP]", "score":0.058545831590890884, "token":19215, "token_str":"prostitute" }, { "sequence":"[CLS] the woman worked as a cleaner. [SEP]", "score":0.03834161534905434, "token":20133, "token_str":"cleaner" } ] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ## Evaluation results When fine-tuned on downstream tasks, this model achieves the following results: Model | SQUAD 1.1 F1/EM | Multi NLI Accuracy ---------------------------------------- | :-------------: | :----------------: BERT-Large, Uncased (Whole Word Masking) | 92.8/86.7 | 87.07 ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1810-04805, author = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding}, journal = {CoRR}, volume = {abs/1810.04805}, year = {2018}, url = {http://arxiv.org/abs/1810.04805}, archivePrefix = {arXiv}, eprint = {1810.04805}, timestamp = {Tue, 30 Oct 2018 20:39:56 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
mradermacher/Swallow-13b-NVE-hf-i1-GGUF
mradermacher
"2024-06-30T15:53:32Z"
22,110
0
transformers
[ "transformers", "gguf", "en", "ja", "base_model:tokyotech-llm/Swallow-13b-NVE-hf", "license:llama2", "endpoints_compatible", "region:us" ]
null
"2024-06-30T06:41:34Z"
--- base_model: tokyotech-llm/Swallow-13b-NVE-hf language: - en - ja library_name: transformers license: llama2 model_type: llama quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/tokyotech-llm/Swallow-13b-NVE-hf <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-i1-GGUF/resolve/main/Swallow-13b-NVE-hf.i1-IQ1_S.gguf) | i1-IQ1_S | 3.0 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-i1-GGUF/resolve/main/Swallow-13b-NVE-hf.i1-IQ1_M.gguf) | i1-IQ1_M | 3.2 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-i1-GGUF/resolve/main/Swallow-13b-NVE-hf.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-i1-GGUF/resolve/main/Swallow-13b-NVE-hf.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-i1-GGUF/resolve/main/Swallow-13b-NVE-hf.i1-IQ2_S.gguf) | i1-IQ2_S | 4.3 | | | [GGUF](https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-i1-GGUF/resolve/main/Swallow-13b-NVE-hf.i1-IQ2_M.gguf) | i1-IQ2_M | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-i1-GGUF/resolve/main/Swallow-13b-NVE-hf.i1-Q2_K.gguf) | i1-Q2_K | 5.0 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-i1-GGUF/resolve/main/Swallow-13b-NVE-hf.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-i1-GGUF/resolve/main/Swallow-13b-NVE-hf.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-i1-GGUF/resolve/main/Swallow-13b-NVE-hf.i1-IQ3_S.gguf) | i1-IQ3_S | 5.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-i1-GGUF/resolve/main/Swallow-13b-NVE-hf.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-i1-GGUF/resolve/main/Swallow-13b-NVE-hf.i1-IQ3_M.gguf) | i1-IQ3_M | 6.1 | | | [GGUF](https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-i1-GGUF/resolve/main/Swallow-13b-NVE-hf.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.4 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-i1-GGUF/resolve/main/Swallow-13b-NVE-hf.i1-Q3_K_L.gguf) | i1-Q3_K_L | 7.0 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-i1-GGUF/resolve/main/Swallow-13b-NVE-hf.i1-IQ4_XS.gguf) | i1-IQ4_XS | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-i1-GGUF/resolve/main/Swallow-13b-NVE-hf.i1-Q4_0.gguf) | i1-Q4_0 | 7.5 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-i1-GGUF/resolve/main/Swallow-13b-NVE-hf.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.5 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-i1-GGUF/resolve/main/Swallow-13b-NVE-hf.i1-Q4_K_M.gguf) | i1-Q4_K_M | 8.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-i1-GGUF/resolve/main/Swallow-13b-NVE-hf.i1-Q5_K_S.gguf) | i1-Q5_K_S | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-i1-GGUF/resolve/main/Swallow-13b-NVE-hf.i1-Q5_K_M.gguf) | i1-Q5_K_M | 9.3 | | | [GGUF](https://huggingface.co/mradermacher/Swallow-13b-NVE-hf-i1-GGUF/resolve/main/Swallow-13b-NVE-hf.i1-Q6_K.gguf) | i1-Q6_K | 10.8 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
Ojimi/anime-kawai-diffusion
Ojimi
"2023-07-14T11:39:06Z"
22,103
139
diffusers
[ "diffusers", "safetensors", "text-to-image", "anime", "pytorch", "art", "stable diffusion", "en", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
"2023-02-09T15:30:12Z"
--- license: creativeml-openrail-m language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - anime - pytorch - diffusers - art - stable diffusion --- ![](assets/preview.png) ## Introduction: - I don't know how to introduce it, but it's been renamed several times. It is an open, free to use and fine-tune AI-art model. It was created by my curiosity. Hope you will like it. Have fun! (●'◡'●). ## Use: - For 🧨Diffusers: ```python from diffusers import DiffusionPipeline pipe = DiffusionPipeline.from_pretrained("Ojimi/anime-kawai-diffusion") pipe = pipe.to("cuda") prompt = "1girl, animal ears, long hair, solo, cat ears, choker, bare shoulders, red eyes, fang, looking at viewer, animal ear fluff, upper body, black hair, blush, closed mouth, off shoulder, bangs, bow, collarbone" image = pipe(prompt, negative_prompt="lowres, bad anatomy").images[0] ``` ## Tips: - The `masterpiece` and `best quality` tags are not necessary, as it sometimes leads to contradictory results, but if it is distorted or discolored, add them now. - The CGF scale should be 7.5 and the step count 28 for the best quality and best performance. - Use a sample photo for your idea. `Interrogate DeepBooru` and change the prompts to suit what you want. - You should use it as a supportive tool for creating works of art, and not rely on it completely. - The Clip skip should be 2. ## **Limitations:** - The drawing is hard, not soft. - Loss of detail, errors, bad human-like (six-fingered hand) details, deformation, blurring, and unclear images are inevitable. - ⚠️Content may not be appropriate for all ages: As it is trained on data that includes adult content, the generated images may contain content not suitable for children (depending on your country there will be a specific regulation about it). If you do not want to appear adult content, make sure you have additional safety measures in place, such as adding "nsfw" to the negative prompt. - The results generated by the model are considered impressive. But unfortunately, currently, it only supports the English language, to use multilingual, consider using third-party translation programs. - The model is trained on the `Danbooru` and `Nai` tagging system, so the long text may result in poor results. - My amount of money: 0 USD =((. ![](money-wallet.gif) ## **Desires:** As it is a version made only by myself and my small associates, the model will not be perfect and may differ from what people expect. Any contributions from everyone will be respected. Want to support me? Thank you, please help me make it better. ❤️ ## Special Thank: This wouldn't have happened if they hadn't made a breakthrough. - [Runwayml](https://huggingface.co/runwayml/): Base model. - [CompVis](https://github.com/CompVis/): VAE Trainer. - stabilityai: [stabilityai/sd-vae-ft-mse-original · Hugging Face](https://huggingface.co/stabilityai/sd-vae-ft-mse-original) - [d8ahazard](https://github.com/d8ahazard/.sd_dreambooth_extension) : Dreambooth. - [Automatic1111](https://github.com/AUTOMATIC1111/) : Web UI. - [Mikubill](https://github.com/Mikubill/): Where my ideas started. - Chat-GPT: Help me do crazy things that I thought I would never do. - Novel AI, Anything Model, Abyss Orange Model: Dataset images. An AI made me thousands of pictures without worrying about copyright or dispute. - Danbooru: Help me write the correct tag. - My friend and others: Get quality images. - And You 🫵❤️ ## Copyright: This license allows anyone to copy, and modify the model, but please follow the terms of the CreativeML Open RAIL-M. You can learn more about the CreativeML Open RAIL-M [here](https://huggingface.co/spaces/CompVis/stable-diffusion-license). If any part of the model does not comply with the terms of the GNU General Public License, the copyright and other rights of the model will still be valid. All AI-generated images are yours, you can do whatever you want, but please obey the laws of your country. We will not be responsible for any problems you cause. We allow you to merge with another model, but if you share that merge model, don't forget to add me to the credits. Don't forget me. # Have fun with your waifu! (●'◡'●) Do you want to sponsor computing resources for us? Thank you . Please sponsor to me on Ko-fi at https://ko-fi.com/projectk.
bigcode/starcoder2-15b
bigcode
"2024-06-05T19:52:45Z"
22,066
530
transformers
[ "transformers", "safetensors", "starcoder2", "text-generation", "code", "dataset:bigcode/the-stack-v2-train", "arxiv:2305.13245", "arxiv:2205.14135", "arxiv:2004.05150", "arxiv:2207.14255", "arxiv:2402.19173", "license:bigcode-openrail-m", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2024-02-20T17:58:19Z"
--- pipeline_tag: text-generation inference: parameters: temperature: 0.2 top_p: 0.95 widget: - text: 'def print_hello_world():' example_title: Hello world group: Python datasets: - bigcode/the-stack-v2-train license: bigcode-openrail-m library_name: transformers tags: - code model-index: - name: starcoder2-15b results: - task: type: text-generation dataset: name: CruxEval-I type: cruxeval-i metrics: - type: pass@1 value: 48.1 - task: type: text-generation dataset: name: DS-1000 type: ds-1000 metrics: - type: pass@1 value: 33.8 - task: type: text-generation dataset: name: GSM8K (PAL) type: gsm8k-pal metrics: - type: accuracy value: 65.1 - task: type: text-generation dataset: name: HumanEval+ type: humanevalplus metrics: - type: pass@1 value: 37.8 - task: type: text-generation dataset: name: HumanEval type: humaneval metrics: - type: pass@1 value: 46.3 - task: type: text-generation dataset: name: RepoBench-v1.1 type: repobench-v1.1 metrics: - type: edit-smiliarity value: 74.08 --- # StarCoder2 <center> <img src="https://huggingface.co/datasets/bigcode/admin_private/resolve/main/starcoder2_banner.png" alt="SC2" width="900" height="600"> </center> ## Table of Contents 1. [Model Summary](#model-summary) 2. [Use](#use) 3. [Limitations](#limitations) 4. [Training](#training) 5. [License](#license) 6. [Citation](#citation) ## Model Summary StarCoder2-15B model is a 15B parameter model trained on 600+ programming languages from [The Stack v2](https://huggingface.co/datasets/bigcode/the-stack-v2-train), with opt-out requests excluded. The model uses [Grouped Query Attention](https://arxiv.org/abs/2305.13245), [a context window of 16,384 tokens](https://arxiv.org/abs/2205.14135) with [a sliding window attention of 4,096 tokens](https://arxiv.org/abs/2004.05150v2), and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 4+ trillion tokens. The model was trained with [NVIDIA NeMo™ Framework](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/) using the [NVIDIA Eos Supercomputer](https://blogs.nvidia.com/blog/eos/) built with [NVIDIA DGX H100](https://www.nvidia.com/en-us/data-center/dgx-h100/) systems. - **Project Website:** [bigcode-project.org](https://www.bigcode-project.org) - **Paper:** [Link](https://huggingface.co/papers/2402.19173) - **Point of Contact:** [[email protected]](mailto:[email protected]) - **Languages:** 600+ Programming languages ## Use ### Intended use The model was trained on GitHub code as well as additional selected data sources such as Arxiv and Wikipedia. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. ### Generation Here are some examples to get started with the model. You can find a script for fine-tuning in StarCoder2's [GitHub repository](https://github.com/bigcode-project/starcoder2). First, make sure to install `transformers` from source: ```bash pip install git+https://github.com/huggingface/transformers.git ``` #### Running the model on CPU/GPU/multi GPU * _Using full precision_ ```python # pip install git+https://github.com/huggingface/transformers.git # TODO: merge PR to main from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "bigcode/starcoder2-15b" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(checkpoint) # for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")` model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` * _Using `torch.bfloat16`_ ```python # pip install accelerate import torch from transformers import AutoTokenizer, AutoModelForCausalLM checkpoint = "bigcode/starcoder2-15b" tokenizer = AutoTokenizer.from_pretrained(checkpoint) # for fp16 use `torch_dtype=torch.float16` instead model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", torch_dtype=torch.bfloat16) inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to("cuda") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ```bash >>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB") Memory footprint: 32251.33 MB ``` #### Quantized Versions through `bitsandbytes` * _Using 8-bit precision (int8)_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig # to use 4bit use `load_in_4bit=True` instead quantization_config = BitsAndBytesConfig(load_in_8bit=True) checkpoint = "bigcode/starcoder2-15b" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint, quantization_config=quantization_config) inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to("cuda") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ```bash >>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB") # load_in_8bit Memory footprint: 16900.18 MB # load_in_4bit >>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB") Memory footprint: 9224.60 MB ``` ### Attribution & Other Requirements The pretraining dataset of the model was filtered for permissive licenses and code with no license only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/search-v2) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code. # Limitations The model has been trained on source code from 600+ programming languages. The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. See [the paper](https://huggingface.co/papers/2402.19173) for an in-depth discussion of the model limitations. # Training ## Model - **Architecture:** Transformer decoder with grouped-query and sliding window attention and Fill-in-the-Middle objective - **Pretraining steps:** 1 million - **Pretraining tokens:** 4+ trillion - **Precision:** bfloat16 ## Hardware - **GPUs:** 1024 x H100 ## Software - **Framework:** [NeMo Framework](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/) - **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) # License The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement). # Citation ```bash @misc{lozhkov2024starcoder, title={StarCoder 2 and The Stack v2: The Next Generation}, author={Anton Lozhkov and Raymond Li and Loubna Ben Allal and Federico Cassano and Joel Lamy-Poirier and Nouamane Tazi and Ao Tang and Dmytro Pykhtar and Jiawei Liu and Yuxiang Wei and Tianyang Liu and Max Tian and Denis Kocetkov and Arthur Zucker and Younes Belkada and Zijian Wang and Qian Liu and Dmitry Abulkhanov and Indraneil Paul and Zhuang Li and Wen-Ding Li and Megan Risdal and Jia Li and Jian Zhu and Terry Yue Zhuo and Evgenii Zheltonozhskii and Nii Osae Osae Dade and Wenhao Yu and Lucas Krauß and Naman Jain and Yixuan Su and Xuanli He and Manan Dey and Edoardo Abati and Yekun Chai and Niklas Muennighoff and Xiangru Tang and Muhtasham Oblokulov and Christopher Akiki and Marc Marone and Chenghao Mou and Mayank Mishra and Alex Gu and Binyuan Hui and Tri Dao and Armel Zebaze and Olivier Dehaene and Nicolas Patry and Canwen Xu and Julian McAuley and Han Hu and Torsten Scholak and Sebastien Paquet and Jennifer Robinson and Carolyn Jane Anderson and Nicolas Chapados and Mostofa Patwary and Nima Tajbakhsh and Yacine Jernite and Carlos Muñoz Ferrandis and Lingming Zhang and Sean Hughes and Thomas Wolf and Arjun Guha and Leandro von Werra and Harm de Vries}, year={2024}, eprint={2402.19173}, archivePrefix={arXiv}, primaryClass={cs.SE} } ```
mradermacher/L3-8B-Spicey-Stheno-v0.2-GGUF
mradermacher
"2024-07-01T22:07:25Z"
22,058
0
transformers
[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Rockinsince87/L3-8B-Spicey-Stheno-v0.2", "endpoints_compatible", "region:us" ]
null
"2024-07-01T17:49:07Z"
--- base_model: Rockinsince87/L3-8B-Spicey-Stheno-v0.2 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Rockinsince87/L3-8B-Spicey-Stheno-v0.2 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/L3-8B-Spicey-Stheno-v0.2-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/L3-8B-Spicey-Stheno-v0.2-GGUF/resolve/main/L3-8B-Spicey-Stheno-v0.2.Q2_K.gguf) | Q2_K | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Spicey-Stheno-v0.2-GGUF/resolve/main/L3-8B-Spicey-Stheno-v0.2.IQ3_XS.gguf) | IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Spicey-Stheno-v0.2-GGUF/resolve/main/L3-8B-Spicey-Stheno-v0.2.Q3_K_S.gguf) | Q3_K_S | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Spicey-Stheno-v0.2-GGUF/resolve/main/L3-8B-Spicey-Stheno-v0.2.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Spicey-Stheno-v0.2-GGUF/resolve/main/L3-8B-Spicey-Stheno-v0.2.IQ3_M.gguf) | IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Spicey-Stheno-v0.2-GGUF/resolve/main/L3-8B-Spicey-Stheno-v0.2.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Spicey-Stheno-v0.2-GGUF/resolve/main/L3-8B-Spicey-Stheno-v0.2.Q3_K_L.gguf) | Q3_K_L | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Spicey-Stheno-v0.2-GGUF/resolve/main/L3-8B-Spicey-Stheno-v0.2.IQ4_XS.gguf) | IQ4_XS | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Spicey-Stheno-v0.2-GGUF/resolve/main/L3-8B-Spicey-Stheno-v0.2.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Spicey-Stheno-v0.2-GGUF/resolve/main/L3-8B-Spicey-Stheno-v0.2.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Spicey-Stheno-v0.2-GGUF/resolve/main/L3-8B-Spicey-Stheno-v0.2.Q5_K_S.gguf) | Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Spicey-Stheno-v0.2-GGUF/resolve/main/L3-8B-Spicey-Stheno-v0.2.Q5_K_M.gguf) | Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Spicey-Stheno-v0.2-GGUF/resolve/main/L3-8B-Spicey-Stheno-v0.2.Q6_K.gguf) | Q6_K | 6.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Spicey-Stheno-v0.2-GGUF/resolve/main/L3-8B-Spicey-Stheno-v0.2.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/L3-8B-Spicey-Stheno-v0.2-GGUF/resolve/main/L3-8B-Spicey-Stheno-v0.2.f16.gguf) | f16 | 16.2 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
legraphista/Codestral-22B-v0.1-hf-FIM-fix
legraphista
"2024-05-30T19:56:15Z"
22,053
2
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "code", "FIM", "FIM fix", "tokenizer fix", "base_model:mistralai/Codestral-22B-v0.1", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2024-05-30T18:53:12Z"
--- license: other license_name: mnpl license_link: https://mistral.ai/licences/MNPL-0.1.md tags: - code - FIM - FIM fix - tokenizer fix language: - code base_model: mistralai/Codestral-22B-v0.1 --- Converted using [this](https://huggingface.co/legraphista/Codestral-22B-v0.1-hf-FIM-fix/blob/main/convert_mistral_weights_to_hf-22B.py) script Initial version of [mistralai/Codestral-22B-v0.1](https://huggingface.co/mistralai/Codestral-22B-v0.1/) had missing tokens in vocab (see [discussion](https://huggingface.co/mistralai/Codestral-22B-v0.1/discussions/10)) This conversion is based on the newest version containing the fix and add the following tokens special tokens: - `[INST]` - `[/INST]` - `[IMG]` - `[PREFIX]` - `[SUFFIX]` - `[MIDDLE]` --- # Model Card for Codestral-22B-v0.1 Codestrall-22B-v0.1 is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash (more details in the [Blogpost](https://mistral.ai/news/codestral/)). The model can be queried: - As instruct, for instance to answer any questions about a code snippet (write documentation, explain, factorize) or to generate code following specific indications - As Fill in the Middle (FIM), to predict the middle tokens between a prefix and a suffix (very useful for software development add-ons like in VS Code) ## Installation It is recommended to use `mistralai/Codestral-22B-v0.1` with [mistral-inference](https://github.com/mistralai/mistral-inference). ``` pip install mistral_inference ``` ## Download ```py from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Codestral-22B-v0.1') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Codestral-22B-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path) ``` ### Chat After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. ``` mistral-chat $HOME/mistral_models/Codestral-22B-v0.1 --instruct --max_tokens 256 ``` Will generate an answer to "Write me a function that computes fibonacci in Rust" and should give something along the following lines: ``` Sure, here's a simple implementation of a function that computes the Fibonacci sequence in Rust. This function takes an integer `n` as an argument and returns the `n`th Fibonacci number. fn fibonacci(n: u32) -> u32 { match n { 0 => 0, 1 => 1, _ => fibonacci(n - 1) + fibonacci(n - 2), } } fn main() { let n = 10; println!("The {}th Fibonacci number is: {}", n, fibonacci(n)); } This function uses recursion to calculate the Fibonacci number. However, it's not the most efficient solution because it performs a lot of redundant calculations. A more efficient solution would use a loop to iteratively calculate the Fibonacci numbers. ``` ### Fill-in-the-middle (FIM) After installing `mistral_inference` and running `pip install --upgrade mistral_common` to make sure to have mistral_common>=1.2 installed: ```py from mistral_inference.model import Transformer from mistral_inference.generate import generate from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.instruct.request import FIMRequest tokenizer = MistralTokenizer.v3() model = Transformer.from_folder("~/codestral-22B-240529") prefix = """def add(""" suffix = """ return sum""" request = FIMRequest(prompt=prefix, suffix=suffix) tokens = tokenizer.encode_fim(request).tokens out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id) result = tokenizer.decode(out_tokens[0]) middle = result.split(suffix)[0].strip() print(middle) ``` Should give something along the following lines: ``` num1, num2): # Add two numbers sum = num1 + num2 # return the sum ``` ## Limitations The Codestral-22B-v0.1 does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## License Codestral-22B-v0.1 is released under the `MNLP-0.1` license. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Jean-Malo Delignon, Jia Li, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickael Seznec, Nicolas Schuhl, Patrick von Platen, Romain Sauvestre, Pierre Stock, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Thibault Schueller, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall
jinaai/jina-embeddings-v2-base-de
jinaai
"2024-04-26T07:35:44Z"
22,046
58
sentence-transformers
[ "sentence-transformers", "pytorch", "onnx", "safetensors", "bert", "fill-mask", "feature-extraction", "sentence-similarity", "mteb", "transformers", "transformers.js", "custom_code", "de", "en", "arxiv:2108.12409", "arxiv:2402.17016", "license:apache-2.0", "model-index", "region:eu" ]
feature-extraction
"2024-01-12T14:04:50Z"
--- tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - transformers - transformers.js language: - de - en inference: false license: apache-2.0 model-index: - name: jina-embeddings-v2-base-de results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.76119402985076 - type: ap value: 35.99577188521176 - type: f1 value: 67.50397431543269 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (de) config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 68.9186295503212 - type: ap value: 79.73307115840507 - type: f1 value: 66.66245744831339 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 77.52215 - type: ap value: 71.85051037177416 - type: f1 value: 77.4171096157774 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.498 - type: f1 value: 38.058193386555956 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.717999999999996 - type: f1 value: 37.22674371574757 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 25.319999999999997 - type: map_at_10 value: 40.351 - type: map_at_100 value: 41.435 - type: map_at_1000 value: 41.443000000000005 - type: map_at_3 value: 35.266 - type: map_at_5 value: 37.99 - type: mrr_at_1 value: 25.746999999999996 - type: mrr_at_10 value: 40.515 - type: mrr_at_100 value: 41.606 - type: mrr_at_1000 value: 41.614000000000004 - type: mrr_at_3 value: 35.42 - type: mrr_at_5 value: 38.112 - type: ndcg_at_1 value: 25.319999999999997 - type: ndcg_at_10 value: 49.332 - type: ndcg_at_100 value: 53.909 - type: ndcg_at_1000 value: 54.089 - type: ndcg_at_3 value: 38.705 - type: ndcg_at_5 value: 43.606 - type: precision_at_1 value: 25.319999999999997 - type: precision_at_10 value: 7.831 - type: precision_at_100 value: 0.9820000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 16.24 - type: precision_at_5 value: 12.119 - type: recall_at_1 value: 25.319999999999997 - type: recall_at_10 value: 78.307 - type: recall_at_100 value: 98.222 - type: recall_at_1000 value: 99.57300000000001 - type: recall_at_3 value: 48.72 - type: recall_at_5 value: 60.597 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 41.43100588255654 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 32.08988904593667 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 60.55514765595906 - type: mrr value: 73.51393835465858 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 79.6723823121172 - type: cos_sim_spearman value: 76.90596922214986 - type: euclidean_pearson value: 77.87910737957918 - type: euclidean_spearman value: 76.66319260598262 - type: manhattan_pearson value: 77.37039493457965 - type: manhattan_spearman value: 76.09872191280964 - task: type: BitextMining dataset: type: mteb/bucc-bitext-mining name: MTEB BUCC (de-en) config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 98.97703549060543 - type: f1 value: 98.86569241475296 - type: precision value: 98.81002087682673 - type: recall value: 98.97703549060543 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 83.93506493506493 - type: f1 value: 83.91014949949302 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 34.970675877585144 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 28.779230269190954 - task: type: Clustering dataset: type: slvnwhrl/blurbs-clustering-p2p name: MTEB BlurbsClusteringP2P config: default split: test revision: a2dd5b02a77de3466a3eaa98ae586b5610314496 metrics: - type: v_measure value: 35.490175601567216 - task: type: Clustering dataset: type: slvnwhrl/blurbs-clustering-s2s name: MTEB BlurbsClusteringS2S config: default split: test revision: 9bfff9a7f8f6dc6ffc9da71c48dd48b68696471d metrics: - type: v_measure value: 16.16638280560168 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.830999999999996 - type: map_at_10 value: 41.355 - type: map_at_100 value: 42.791000000000004 - type: map_at_1000 value: 42.918 - type: map_at_3 value: 38.237 - type: map_at_5 value: 40.066 - type: mrr_at_1 value: 38.484 - type: mrr_at_10 value: 47.593 - type: mrr_at_100 value: 48.388 - type: mrr_at_1000 value: 48.439 - type: mrr_at_3 value: 45.279 - type: mrr_at_5 value: 46.724 - type: ndcg_at_1 value: 38.484 - type: ndcg_at_10 value: 47.27 - type: ndcg_at_100 value: 52.568000000000005 - type: ndcg_at_1000 value: 54.729000000000006 - type: ndcg_at_3 value: 43.061 - type: ndcg_at_5 value: 45.083 - type: precision_at_1 value: 38.484 - type: precision_at_10 value: 8.927 - type: precision_at_100 value: 1.425 - type: precision_at_1000 value: 0.19 - type: precision_at_3 value: 20.791999999999998 - type: precision_at_5 value: 14.85 - type: recall_at_1 value: 30.830999999999996 - type: recall_at_10 value: 57.87799999999999 - type: recall_at_100 value: 80.124 - type: recall_at_1000 value: 94.208 - type: recall_at_3 value: 45.083 - type: recall_at_5 value: 51.154999999999994 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.782 - type: map_at_10 value: 34.492 - type: map_at_100 value: 35.521 - type: map_at_1000 value: 35.638 - type: map_at_3 value: 31.735999999999997 - type: map_at_5 value: 33.339 - type: mrr_at_1 value: 32.357 - type: mrr_at_10 value: 39.965 - type: mrr_at_100 value: 40.644000000000005 - type: mrr_at_1000 value: 40.695 - type: mrr_at_3 value: 37.739 - type: mrr_at_5 value: 39.061 - type: ndcg_at_1 value: 32.357 - type: ndcg_at_10 value: 39.644 - type: ndcg_at_100 value: 43.851 - type: ndcg_at_1000 value: 46.211999999999996 - type: ndcg_at_3 value: 35.675000000000004 - type: ndcg_at_5 value: 37.564 - type: precision_at_1 value: 32.357 - type: precision_at_10 value: 7.344 - type: precision_at_100 value: 1.201 - type: precision_at_1000 value: 0.168 - type: precision_at_3 value: 17.155 - type: precision_at_5 value: 12.166 - type: recall_at_1 value: 25.782 - type: recall_at_10 value: 49.132999999999996 - type: recall_at_100 value: 67.24 - type: recall_at_1000 value: 83.045 - type: recall_at_3 value: 37.021 - type: recall_at_5 value: 42.548 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 35.778999999999996 - type: map_at_10 value: 47.038000000000004 - type: map_at_100 value: 48.064 - type: map_at_1000 value: 48.128 - type: map_at_3 value: 44.186 - type: map_at_5 value: 45.788000000000004 - type: mrr_at_1 value: 41.254000000000005 - type: mrr_at_10 value: 50.556999999999995 - type: mrr_at_100 value: 51.296 - type: mrr_at_1000 value: 51.331 - type: mrr_at_3 value: 48.318 - type: mrr_at_5 value: 49.619 - type: ndcg_at_1 value: 41.254000000000005 - type: ndcg_at_10 value: 52.454 - type: ndcg_at_100 value: 56.776 - type: ndcg_at_1000 value: 58.181000000000004 - type: ndcg_at_3 value: 47.713 - type: ndcg_at_5 value: 49.997 - type: precision_at_1 value: 41.254000000000005 - type: precision_at_10 value: 8.464 - type: precision_at_100 value: 1.157 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 21.526 - type: precision_at_5 value: 14.696000000000002 - type: recall_at_1 value: 35.778999999999996 - type: recall_at_10 value: 64.85300000000001 - type: recall_at_100 value: 83.98400000000001 - type: recall_at_1000 value: 94.18299999999999 - type: recall_at_3 value: 51.929 - type: recall_at_5 value: 57.666 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.719 - type: map_at_10 value: 29.326999999999998 - type: map_at_100 value: 30.314000000000004 - type: map_at_1000 value: 30.397000000000002 - type: map_at_3 value: 27.101 - type: map_at_5 value: 28.141 - type: mrr_at_1 value: 23.503 - type: mrr_at_10 value: 31.225 - type: mrr_at_100 value: 32.096000000000004 - type: mrr_at_1000 value: 32.159 - type: mrr_at_3 value: 29.076999999999998 - type: mrr_at_5 value: 30.083 - type: ndcg_at_1 value: 23.503 - type: ndcg_at_10 value: 33.842 - type: ndcg_at_100 value: 39.038000000000004 - type: ndcg_at_1000 value: 41.214 - type: ndcg_at_3 value: 29.347 - type: ndcg_at_5 value: 31.121 - type: precision_at_1 value: 23.503 - type: precision_at_10 value: 5.266 - type: precision_at_100 value: 0.831 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 12.504999999999999 - type: precision_at_5 value: 8.565000000000001 - type: recall_at_1 value: 21.719 - type: recall_at_10 value: 46.024 - type: recall_at_100 value: 70.78999999999999 - type: recall_at_1000 value: 87.022 - type: recall_at_3 value: 33.64 - type: recall_at_5 value: 37.992 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 15.601 - type: map_at_10 value: 22.054000000000002 - type: map_at_100 value: 23.177 - type: map_at_1000 value: 23.308 - type: map_at_3 value: 19.772000000000002 - type: map_at_5 value: 21.055 - type: mrr_at_1 value: 19.403000000000002 - type: mrr_at_10 value: 26.409 - type: mrr_at_100 value: 27.356 - type: mrr_at_1000 value: 27.441 - type: mrr_at_3 value: 24.108999999999998 - type: mrr_at_5 value: 25.427 - type: ndcg_at_1 value: 19.403000000000002 - type: ndcg_at_10 value: 26.474999999999998 - type: ndcg_at_100 value: 32.086 - type: ndcg_at_1000 value: 35.231 - type: ndcg_at_3 value: 22.289 - type: ndcg_at_5 value: 24.271 - type: precision_at_1 value: 19.403000000000002 - type: precision_at_10 value: 4.813 - type: precision_at_100 value: 0.8869999999999999 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 10.531 - type: precision_at_5 value: 7.710999999999999 - type: recall_at_1 value: 15.601 - type: recall_at_10 value: 35.916 - type: recall_at_100 value: 60.8 - type: recall_at_1000 value: 83.245 - type: recall_at_3 value: 24.321 - type: recall_at_5 value: 29.372999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.522 - type: map_at_10 value: 34.854 - type: map_at_100 value: 36.269 - type: map_at_1000 value: 36.387 - type: map_at_3 value: 32.187 - type: map_at_5 value: 33.692 - type: mrr_at_1 value: 31.375999999999998 - type: mrr_at_10 value: 40.471000000000004 - type: mrr_at_100 value: 41.481 - type: mrr_at_1000 value: 41.533 - type: mrr_at_3 value: 38.274 - type: mrr_at_5 value: 39.612 - type: ndcg_at_1 value: 31.375999999999998 - type: ndcg_at_10 value: 40.298 - type: ndcg_at_100 value: 46.255 - type: ndcg_at_1000 value: 48.522 - type: ndcg_at_3 value: 36.049 - type: ndcg_at_5 value: 38.095 - type: precision_at_1 value: 31.375999999999998 - type: precision_at_10 value: 7.305000000000001 - type: precision_at_100 value: 1.201 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 17.132 - type: precision_at_5 value: 12.107999999999999 - type: recall_at_1 value: 25.522 - type: recall_at_10 value: 50.988 - type: recall_at_100 value: 76.005 - type: recall_at_1000 value: 91.11200000000001 - type: recall_at_3 value: 38.808 - type: recall_at_5 value: 44.279 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.615000000000002 - type: map_at_10 value: 32.843 - type: map_at_100 value: 34.172999999999995 - type: map_at_1000 value: 34.286 - type: map_at_3 value: 30.125 - type: map_at_5 value: 31.495 - type: mrr_at_1 value: 30.023 - type: mrr_at_10 value: 38.106 - type: mrr_at_100 value: 39.01 - type: mrr_at_1000 value: 39.071 - type: mrr_at_3 value: 35.674 - type: mrr_at_5 value: 36.924 - type: ndcg_at_1 value: 30.023 - type: ndcg_at_10 value: 38.091 - type: ndcg_at_100 value: 43.771 - type: ndcg_at_1000 value: 46.315 - type: ndcg_at_3 value: 33.507 - type: ndcg_at_5 value: 35.304 - type: precision_at_1 value: 30.023 - type: precision_at_10 value: 6.837999999999999 - type: precision_at_100 value: 1.124 - type: precision_at_1000 value: 0.152 - type: precision_at_3 value: 15.562999999999999 - type: precision_at_5 value: 10.936 - type: recall_at_1 value: 24.615000000000002 - type: recall_at_10 value: 48.691 - type: recall_at_100 value: 72.884 - type: recall_at_1000 value: 90.387 - type: recall_at_3 value: 35.659 - type: recall_at_5 value: 40.602 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.223666666666666 - type: map_at_10 value: 31.338166666666673 - type: map_at_100 value: 32.47358333333333 - type: map_at_1000 value: 32.5955 - type: map_at_3 value: 28.84133333333333 - type: map_at_5 value: 30.20808333333333 - type: mrr_at_1 value: 27.62483333333333 - type: mrr_at_10 value: 35.385916666666674 - type: mrr_at_100 value: 36.23325 - type: mrr_at_1000 value: 36.29966666666667 - type: mrr_at_3 value: 33.16583333333333 - type: mrr_at_5 value: 34.41983333333334 - type: ndcg_at_1 value: 27.62483333333333 - type: ndcg_at_10 value: 36.222 - type: ndcg_at_100 value: 41.29491666666666 - type: ndcg_at_1000 value: 43.85508333333333 - type: ndcg_at_3 value: 31.95116666666667 - type: ndcg_at_5 value: 33.88541666666667 - type: precision_at_1 value: 27.62483333333333 - type: precision_at_10 value: 6.339916666666667 - type: precision_at_100 value: 1.0483333333333333 - type: precision_at_1000 value: 0.14608333333333334 - type: precision_at_3 value: 14.726500000000003 - type: precision_at_5 value: 10.395 - type: recall_at_1 value: 23.223666666666666 - type: recall_at_10 value: 46.778999999999996 - type: recall_at_100 value: 69.27141666666667 - type: recall_at_1000 value: 87.27383333333334 - type: recall_at_3 value: 34.678749999999994 - type: recall_at_5 value: 39.79900000000001 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.677 - type: map_at_10 value: 27.828000000000003 - type: map_at_100 value: 28.538999999999998 - type: map_at_1000 value: 28.64 - type: map_at_3 value: 26.105 - type: map_at_5 value: 27.009 - type: mrr_at_1 value: 24.387 - type: mrr_at_10 value: 30.209999999999997 - type: mrr_at_100 value: 30.953000000000003 - type: mrr_at_1000 value: 31.029 - type: mrr_at_3 value: 28.707 - type: mrr_at_5 value: 29.610999999999997 - type: ndcg_at_1 value: 24.387 - type: ndcg_at_10 value: 31.378 - type: ndcg_at_100 value: 35.249 - type: ndcg_at_1000 value: 37.923 - type: ndcg_at_3 value: 28.213 - type: ndcg_at_5 value: 29.658 - type: precision_at_1 value: 24.387 - type: precision_at_10 value: 4.8309999999999995 - type: precision_at_100 value: 0.73 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 12.168 - type: precision_at_5 value: 8.251999999999999 - type: recall_at_1 value: 21.677 - type: recall_at_10 value: 40.069 - type: recall_at_100 value: 58.077 - type: recall_at_1000 value: 77.97 - type: recall_at_3 value: 31.03 - type: recall_at_5 value: 34.838 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 14.484 - type: map_at_10 value: 20.355 - type: map_at_100 value: 21.382 - type: map_at_1000 value: 21.511 - type: map_at_3 value: 18.448 - type: map_at_5 value: 19.451999999999998 - type: mrr_at_1 value: 17.584 - type: mrr_at_10 value: 23.825 - type: mrr_at_100 value: 24.704 - type: mrr_at_1000 value: 24.793000000000003 - type: mrr_at_3 value: 21.92 - type: mrr_at_5 value: 22.97 - type: ndcg_at_1 value: 17.584 - type: ndcg_at_10 value: 24.315 - type: ndcg_at_100 value: 29.354999999999997 - type: ndcg_at_1000 value: 32.641999999999996 - type: ndcg_at_3 value: 20.802 - type: ndcg_at_5 value: 22.335 - type: precision_at_1 value: 17.584 - type: precision_at_10 value: 4.443 - type: precision_at_100 value: 0.8160000000000001 - type: precision_at_1000 value: 0.128 - type: precision_at_3 value: 9.807 - type: precision_at_5 value: 7.0889999999999995 - type: recall_at_1 value: 14.484 - type: recall_at_10 value: 32.804 - type: recall_at_100 value: 55.679 - type: recall_at_1000 value: 79.63 - type: recall_at_3 value: 22.976 - type: recall_at_5 value: 26.939 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.983999999999998 - type: map_at_10 value: 30.812 - type: map_at_100 value: 31.938 - type: map_at_1000 value: 32.056000000000004 - type: map_at_3 value: 28.449999999999996 - type: map_at_5 value: 29.542 - type: mrr_at_1 value: 27.145999999999997 - type: mrr_at_10 value: 34.782999999999994 - type: mrr_at_100 value: 35.699 - type: mrr_at_1000 value: 35.768 - type: mrr_at_3 value: 32.572 - type: mrr_at_5 value: 33.607 - type: ndcg_at_1 value: 27.145999999999997 - type: ndcg_at_10 value: 35.722 - type: ndcg_at_100 value: 40.964 - type: ndcg_at_1000 value: 43.598 - type: ndcg_at_3 value: 31.379 - type: ndcg_at_5 value: 32.924 - type: precision_at_1 value: 27.145999999999997 - type: precision_at_10 value: 6.063000000000001 - type: precision_at_100 value: 0.9730000000000001 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 14.366000000000001 - type: precision_at_5 value: 9.776 - type: recall_at_1 value: 22.983999999999998 - type: recall_at_10 value: 46.876 - type: recall_at_100 value: 69.646 - type: recall_at_1000 value: 88.305 - type: recall_at_3 value: 34.471000000000004 - type: recall_at_5 value: 38.76 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.017000000000003 - type: map_at_10 value: 31.049 - type: map_at_100 value: 32.582 - type: map_at_1000 value: 32.817 - type: map_at_3 value: 28.303 - type: map_at_5 value: 29.854000000000003 - type: mrr_at_1 value: 27.866000000000003 - type: mrr_at_10 value: 35.56 - type: mrr_at_100 value: 36.453 - type: mrr_at_1000 value: 36.519 - type: mrr_at_3 value: 32.938 - type: mrr_at_5 value: 34.391 - type: ndcg_at_1 value: 27.866000000000003 - type: ndcg_at_10 value: 36.506 - type: ndcg_at_100 value: 42.344 - type: ndcg_at_1000 value: 45.213 - type: ndcg_at_3 value: 31.805 - type: ndcg_at_5 value: 33.933 - type: precision_at_1 value: 27.866000000000003 - type: precision_at_10 value: 7.016 - type: precision_at_100 value: 1.468 - type: precision_at_1000 value: 0.23900000000000002 - type: precision_at_3 value: 14.822 - type: precision_at_5 value: 10.791 - type: recall_at_1 value: 23.017000000000003 - type: recall_at_10 value: 47.053 - type: recall_at_100 value: 73.177 - type: recall_at_1000 value: 91.47800000000001 - type: recall_at_3 value: 33.675 - type: recall_at_5 value: 39.36 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.673 - type: map_at_10 value: 24.051000000000002 - type: map_at_100 value: 24.933 - type: map_at_1000 value: 25.06 - type: map_at_3 value: 21.446 - type: map_at_5 value: 23.064 - type: mrr_at_1 value: 18.115000000000002 - type: mrr_at_10 value: 25.927 - type: mrr_at_100 value: 26.718999999999998 - type: mrr_at_1000 value: 26.817999999999998 - type: mrr_at_3 value: 23.383000000000003 - type: mrr_at_5 value: 25.008999999999997 - type: ndcg_at_1 value: 18.115000000000002 - type: ndcg_at_10 value: 28.669 - type: ndcg_at_100 value: 33.282000000000004 - type: ndcg_at_1000 value: 36.481 - type: ndcg_at_3 value: 23.574 - type: ndcg_at_5 value: 26.340000000000003 - type: precision_at_1 value: 18.115000000000002 - type: precision_at_10 value: 4.769 - type: precision_at_100 value: 0.767 - type: precision_at_1000 value: 0.116 - type: precision_at_3 value: 10.351 - type: precision_at_5 value: 7.8 - type: recall_at_1 value: 16.673 - type: recall_at_10 value: 41.063 - type: recall_at_100 value: 62.851 - type: recall_at_1000 value: 86.701 - type: recall_at_3 value: 27.532 - type: recall_at_5 value: 34.076 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 8.752 - type: map_at_10 value: 15.120000000000001 - type: map_at_100 value: 16.678 - type: map_at_1000 value: 16.854 - type: map_at_3 value: 12.603 - type: map_at_5 value: 13.918 - type: mrr_at_1 value: 19.283 - type: mrr_at_10 value: 29.145 - type: mrr_at_100 value: 30.281000000000002 - type: mrr_at_1000 value: 30.339 - type: mrr_at_3 value: 26.069 - type: mrr_at_5 value: 27.864 - type: ndcg_at_1 value: 19.283 - type: ndcg_at_10 value: 21.804000000000002 - type: ndcg_at_100 value: 28.576 - type: ndcg_at_1000 value: 32.063 - type: ndcg_at_3 value: 17.511 - type: ndcg_at_5 value: 19.112000000000002 - type: precision_at_1 value: 19.283 - type: precision_at_10 value: 6.873 - type: precision_at_100 value: 1.405 - type: precision_at_1000 value: 0.20500000000000002 - type: precision_at_3 value: 13.16 - type: precision_at_5 value: 10.189 - type: recall_at_1 value: 8.752 - type: recall_at_10 value: 27.004 - type: recall_at_100 value: 50.648 - type: recall_at_1000 value: 70.458 - type: recall_at_3 value: 16.461000000000002 - type: recall_at_5 value: 20.973 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 6.81 - type: map_at_10 value: 14.056 - type: map_at_100 value: 18.961 - type: map_at_1000 value: 20.169 - type: map_at_3 value: 10.496 - type: map_at_5 value: 11.952 - type: mrr_at_1 value: 53.5 - type: mrr_at_10 value: 63.479 - type: mrr_at_100 value: 63.971999999999994 - type: mrr_at_1000 value: 63.993 - type: mrr_at_3 value: 61.541999999999994 - type: mrr_at_5 value: 62.778999999999996 - type: ndcg_at_1 value: 42.25 - type: ndcg_at_10 value: 31.471 - type: ndcg_at_100 value: 35.115 - type: ndcg_at_1000 value: 42.408 - type: ndcg_at_3 value: 35.458 - type: ndcg_at_5 value: 32.973 - type: precision_at_1 value: 53.5 - type: precision_at_10 value: 24.85 - type: precision_at_100 value: 7.79 - type: precision_at_1000 value: 1.599 - type: precision_at_3 value: 38.667 - type: precision_at_5 value: 31.55 - type: recall_at_1 value: 6.81 - type: recall_at_10 value: 19.344 - type: recall_at_100 value: 40.837 - type: recall_at_1000 value: 64.661 - type: recall_at_3 value: 11.942 - type: recall_at_5 value: 14.646 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 44.64499999999999 - type: f1 value: 39.39106911352714 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 48.196 - type: map_at_10 value: 61.404 - type: map_at_100 value: 61.846000000000004 - type: map_at_1000 value: 61.866 - type: map_at_3 value: 58.975 - type: map_at_5 value: 60.525 - type: mrr_at_1 value: 52.025 - type: mrr_at_10 value: 65.43299999999999 - type: mrr_at_100 value: 65.80799999999999 - type: mrr_at_1000 value: 65.818 - type: mrr_at_3 value: 63.146 - type: mrr_at_5 value: 64.64 - type: ndcg_at_1 value: 52.025 - type: ndcg_at_10 value: 67.889 - type: ndcg_at_100 value: 69.864 - type: ndcg_at_1000 value: 70.337 - type: ndcg_at_3 value: 63.315 - type: ndcg_at_5 value: 65.91799999999999 - type: precision_at_1 value: 52.025 - type: precision_at_10 value: 9.182 - type: precision_at_100 value: 1.027 - type: precision_at_1000 value: 0.108 - type: precision_at_3 value: 25.968000000000004 - type: precision_at_5 value: 17.006 - type: recall_at_1 value: 48.196 - type: recall_at_10 value: 83.885 - type: recall_at_100 value: 92.671 - type: recall_at_1000 value: 96.018 - type: recall_at_3 value: 71.59 - type: recall_at_5 value: 77.946 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 15.193000000000001 - type: map_at_10 value: 25.168000000000003 - type: map_at_100 value: 27.017000000000003 - type: map_at_1000 value: 27.205000000000002 - type: map_at_3 value: 21.746 - type: map_at_5 value: 23.579 - type: mrr_at_1 value: 31.635999999999996 - type: mrr_at_10 value: 40.077 - type: mrr_at_100 value: 41.112 - type: mrr_at_1000 value: 41.160999999999994 - type: mrr_at_3 value: 37.937 - type: mrr_at_5 value: 39.18 - type: ndcg_at_1 value: 31.635999999999996 - type: ndcg_at_10 value: 32.298 - type: ndcg_at_100 value: 39.546 - type: ndcg_at_1000 value: 42.88 - type: ndcg_at_3 value: 29.221999999999998 - type: ndcg_at_5 value: 30.069000000000003 - type: precision_at_1 value: 31.635999999999996 - type: precision_at_10 value: 9.367 - type: precision_at_100 value: 1.645 - type: precision_at_1000 value: 0.22399999999999998 - type: precision_at_3 value: 20.01 - type: precision_at_5 value: 14.753 - type: recall_at_1 value: 15.193000000000001 - type: recall_at_10 value: 38.214999999999996 - type: recall_at_100 value: 65.95 - type: recall_at_1000 value: 85.85300000000001 - type: recall_at_3 value: 26.357000000000003 - type: recall_at_5 value: 31.319999999999997 - task: type: Retrieval dataset: type: jinaai/ger_da_lir name: MTEB GerDaLIR config: default split: test revision: None metrics: - type: map_at_1 value: 10.363 - type: map_at_10 value: 16.222 - type: map_at_100 value: 17.28 - type: map_at_1000 value: 17.380000000000003 - type: map_at_3 value: 14.054 - type: map_at_5 value: 15.203 - type: mrr_at_1 value: 11.644 - type: mrr_at_10 value: 17.625 - type: mrr_at_100 value: 18.608 - type: mrr_at_1000 value: 18.695999999999998 - type: mrr_at_3 value: 15.481 - type: mrr_at_5 value: 16.659 - type: ndcg_at_1 value: 11.628 - type: ndcg_at_10 value: 20.028000000000002 - type: ndcg_at_100 value: 25.505 - type: ndcg_at_1000 value: 28.288000000000004 - type: ndcg_at_3 value: 15.603 - type: ndcg_at_5 value: 17.642 - type: precision_at_1 value: 11.628 - type: precision_at_10 value: 3.5589999999999997 - type: precision_at_100 value: 0.664 - type: precision_at_1000 value: 0.092 - type: precision_at_3 value: 7.109999999999999 - type: precision_at_5 value: 5.401 - type: recall_at_1 value: 10.363 - type: recall_at_10 value: 30.586000000000002 - type: recall_at_100 value: 56.43 - type: recall_at_1000 value: 78.142 - type: recall_at_3 value: 18.651 - type: recall_at_5 value: 23.493 - task: type: Retrieval dataset: type: deepset/germandpr name: MTEB GermanDPR config: default split: test revision: 5129d02422a66be600ac89cd3e8531b4f97d347d metrics: - type: map_at_1 value: 60.78 - type: map_at_10 value: 73.91499999999999 - type: map_at_100 value: 74.089 - type: map_at_1000 value: 74.09400000000001 - type: map_at_3 value: 71.87 - type: map_at_5 value: 73.37700000000001 - type: mrr_at_1 value: 60.78 - type: mrr_at_10 value: 73.91499999999999 - type: mrr_at_100 value: 74.089 - type: mrr_at_1000 value: 74.09400000000001 - type: mrr_at_3 value: 71.87 - type: mrr_at_5 value: 73.37700000000001 - type: ndcg_at_1 value: 60.78 - type: ndcg_at_10 value: 79.35600000000001 - type: ndcg_at_100 value: 80.077 - type: ndcg_at_1000 value: 80.203 - type: ndcg_at_3 value: 75.393 - type: ndcg_at_5 value: 78.077 - type: precision_at_1 value: 60.78 - type: precision_at_10 value: 9.59 - type: precision_at_100 value: 0.9900000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 28.52 - type: precision_at_5 value: 18.4 - type: recall_at_1 value: 60.78 - type: recall_at_10 value: 95.902 - type: recall_at_100 value: 99.024 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 85.56099999999999 - type: recall_at_5 value: 92.0 - task: type: STS dataset: type: jinaai/german-STSbenchmark name: MTEB GermanSTSBenchmark config: default split: test revision: 49d9b423b996fea62b483f9ee6dfb5ec233515ca metrics: - type: cos_sim_pearson value: 88.49524420894356 - type: cos_sim_spearman value: 88.32407839427714 - type: euclidean_pearson value: 87.25098779877104 - type: euclidean_spearman value: 88.22738098593608 - type: manhattan_pearson value: 87.23872691839607 - type: manhattan_spearman value: 88.2002968380165 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 31.81 - type: map_at_10 value: 46.238 - type: map_at_100 value: 47.141 - type: map_at_1000 value: 47.213 - type: map_at_3 value: 43.248999999999995 - type: map_at_5 value: 45.078 - type: mrr_at_1 value: 63.619 - type: mrr_at_10 value: 71.279 - type: mrr_at_100 value: 71.648 - type: mrr_at_1000 value: 71.665 - type: mrr_at_3 value: 69.76599999999999 - type: mrr_at_5 value: 70.743 - type: ndcg_at_1 value: 63.619 - type: ndcg_at_10 value: 55.38999999999999 - type: ndcg_at_100 value: 58.80800000000001 - type: ndcg_at_1000 value: 60.331999999999994 - type: ndcg_at_3 value: 50.727 - type: ndcg_at_5 value: 53.284 - type: precision_at_1 value: 63.619 - type: precision_at_10 value: 11.668000000000001 - type: precision_at_100 value: 1.434 - type: precision_at_1000 value: 0.164 - type: precision_at_3 value: 32.001000000000005 - type: precision_at_5 value: 21.223 - type: recall_at_1 value: 31.81 - type: recall_at_10 value: 58.339 - type: recall_at_100 value: 71.708 - type: recall_at_1000 value: 81.85 - type: recall_at_3 value: 48.001 - type: recall_at_5 value: 53.059 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 68.60640000000001 - type: ap value: 62.84296904042086 - type: f1 value: 68.50643633327537 - task: type: Reranking dataset: type: jinaai/miracl name: MTEB MIRACL config: default split: test revision: 8741c3b61cd36ed9ca1b3d4203543a41793239e2 metrics: - type: map value: 64.29704335389768 - type: mrr value: 72.11962197159565 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 89.3844049247606 - type: f1 value: 89.2124328528015 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (de) config: de split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 88.36855452240067 - type: f1 value: 87.35458822097442 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 66.48654810761514 - type: f1 value: 50.07229882504409 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (de) config: de split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 63.832065370526905 - type: f1 value: 46.283579383385806 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (de) config: de split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 63.89038332212509 - type: f1 value: 61.86279849685129 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 69.11230665770006 - type: f1 value: 67.44780095350535 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (de) config: de split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 71.25084061869536 - type: f1 value: 71.43965023016408 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.73907195696032 - type: f1 value: 73.69920814839061 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 31.32577306498249 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 28.759349326367783 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.401342674703425 - type: mrr value: 31.384379585660987 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 4.855 - type: map_at_10 value: 10.01 - type: map_at_100 value: 12.461 - type: map_at_1000 value: 13.776 - type: map_at_3 value: 7.252 - type: map_at_5 value: 8.679 - type: mrr_at_1 value: 41.176 - type: mrr_at_10 value: 49.323 - type: mrr_at_100 value: 49.954 - type: mrr_at_1000 value: 49.997 - type: mrr_at_3 value: 46.904 - type: mrr_at_5 value: 48.375 - type: ndcg_at_1 value: 39.318999999999996 - type: ndcg_at_10 value: 28.607 - type: ndcg_at_100 value: 26.554 - type: ndcg_at_1000 value: 35.731 - type: ndcg_at_3 value: 32.897999999999996 - type: ndcg_at_5 value: 31.53 - type: precision_at_1 value: 41.176 - type: precision_at_10 value: 20.867 - type: precision_at_100 value: 6.796 - type: precision_at_1000 value: 1.983 - type: precision_at_3 value: 30.547 - type: precision_at_5 value: 27.245 - type: recall_at_1 value: 4.855 - type: recall_at_10 value: 14.08 - type: recall_at_100 value: 28.188000000000002 - type: recall_at_1000 value: 60.07900000000001 - type: recall_at_3 value: 7.947 - type: recall_at_5 value: 10.786 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 26.906999999999996 - type: map_at_10 value: 41.147 - type: map_at_100 value: 42.269 - type: map_at_1000 value: 42.308 - type: map_at_3 value: 36.638999999999996 - type: map_at_5 value: 39.285 - type: mrr_at_1 value: 30.359 - type: mrr_at_10 value: 43.607 - type: mrr_at_100 value: 44.454 - type: mrr_at_1000 value: 44.481 - type: mrr_at_3 value: 39.644 - type: mrr_at_5 value: 42.061 - type: ndcg_at_1 value: 30.330000000000002 - type: ndcg_at_10 value: 48.899 - type: ndcg_at_100 value: 53.612 - type: ndcg_at_1000 value: 54.51200000000001 - type: ndcg_at_3 value: 40.262 - type: ndcg_at_5 value: 44.787 - type: precision_at_1 value: 30.330000000000002 - type: precision_at_10 value: 8.323 - type: precision_at_100 value: 1.0959999999999999 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 18.395 - type: precision_at_5 value: 13.627 - type: recall_at_1 value: 26.906999999999996 - type: recall_at_10 value: 70.215 - type: recall_at_100 value: 90.61200000000001 - type: recall_at_1000 value: 97.294 - type: recall_at_3 value: 47.784 - type: recall_at_5 value: 58.251 - task: type: PairClassification dataset: type: paws-x name: MTEB PawsX config: default split: test revision: 8a04d940a42cd40658986fdd8e3da561533a3646 metrics: - type: cos_sim_accuracy value: 60.5 - type: cos_sim_ap value: 57.606096528877494 - type: cos_sim_f1 value: 62.24240307369892 - type: cos_sim_precision value: 45.27439024390244 - type: cos_sim_recall value: 99.55307262569832 - type: dot_accuracy value: 57.699999999999996 - type: dot_ap value: 51.289351057160616 - type: dot_f1 value: 62.25953130465197 - type: dot_precision value: 45.31568228105906 - type: dot_recall value: 99.4413407821229 - type: euclidean_accuracy value: 60.45 - type: euclidean_ap value: 57.616461421424034 - type: euclidean_f1 value: 62.313697657913416 - type: euclidean_precision value: 45.657826313052524 - type: euclidean_recall value: 98.10055865921787 - type: manhattan_accuracy value: 60.3 - type: manhattan_ap value: 57.580565271667325 - type: manhattan_f1 value: 62.24240307369892 - type: manhattan_precision value: 45.27439024390244 - type: manhattan_recall value: 99.55307262569832 - type: max_accuracy value: 60.5 - type: max_ap value: 57.616461421424034 - type: max_f1 value: 62.313697657913416 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.21300000000001 - type: map_at_10 value: 84.136 - type: map_at_100 value: 84.796 - type: map_at_1000 value: 84.812 - type: map_at_3 value: 81.182 - type: map_at_5 value: 83.027 - type: mrr_at_1 value: 80.91000000000001 - type: mrr_at_10 value: 87.155 - type: mrr_at_100 value: 87.27000000000001 - type: mrr_at_1000 value: 87.271 - type: mrr_at_3 value: 86.158 - type: mrr_at_5 value: 86.828 - type: ndcg_at_1 value: 80.88 - type: ndcg_at_10 value: 87.926 - type: ndcg_at_100 value: 89.223 - type: ndcg_at_1000 value: 89.321 - type: ndcg_at_3 value: 85.036 - type: ndcg_at_5 value: 86.614 - type: precision_at_1 value: 80.88 - type: precision_at_10 value: 13.350000000000001 - type: precision_at_100 value: 1.5310000000000001 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.173 - type: precision_at_5 value: 24.476 - type: recall_at_1 value: 70.21300000000001 - type: recall_at_10 value: 95.12 - type: recall_at_100 value: 99.535 - type: recall_at_1000 value: 99.977 - type: recall_at_3 value: 86.833 - type: recall_at_5 value: 91.26100000000001 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 47.754688783184875 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 54.875736374329364 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 3.773 - type: map_at_10 value: 9.447 - type: map_at_100 value: 11.1 - type: map_at_1000 value: 11.37 - type: map_at_3 value: 6.787 - type: map_at_5 value: 8.077 - type: mrr_at_1 value: 18.5 - type: mrr_at_10 value: 28.227000000000004 - type: mrr_at_100 value: 29.445 - type: mrr_at_1000 value: 29.515 - type: mrr_at_3 value: 25.2 - type: mrr_at_5 value: 27.055 - type: ndcg_at_1 value: 18.5 - type: ndcg_at_10 value: 16.29 - type: ndcg_at_100 value: 23.250999999999998 - type: ndcg_at_1000 value: 28.445999999999998 - type: ndcg_at_3 value: 15.376000000000001 - type: ndcg_at_5 value: 13.528 - type: precision_at_1 value: 18.5 - type: precision_at_10 value: 8.51 - type: precision_at_100 value: 1.855 - type: precision_at_1000 value: 0.311 - type: precision_at_3 value: 14.533 - type: precision_at_5 value: 12.0 - type: recall_at_1 value: 3.773 - 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task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 88.02072712147692 - type: cos_sim_spearman value: 88.78821332623012 - type: euclidean_pearson value: 88.12132045572747 - type: euclidean_spearman value: 88.74273451067364 - type: manhattan_pearson value: 88.05431550059166 - type: manhattan_spearman value: 88.67610233020723 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 82.96134704624787 - type: cos_sim_spearman value: 84.44062976314666 - type: euclidean_pearson value: 84.03642536310323 - type: euclidean_spearman value: 84.4535014579785 - type: manhattan_pearson value: 83.92874228901483 - type: manhattan_spearman value: 84.33634314951631 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-de) config: en-de split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - 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type: recall_at_1000 value: 98.833 - type: recall_at_3 value: 60.211000000000006 - type: recall_at_5 value: 66.3 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.79009900990098 - type: cos_sim_ap value: 94.86690691995835 - type: cos_sim_f1 value: 89.37875751503007 - type: cos_sim_precision value: 89.5582329317269 - type: cos_sim_recall value: 89.2 - type: dot_accuracy value: 99.76336633663367 - type: dot_ap value: 94.26453740761586 - type: dot_f1 value: 88.00783162016641 - type: dot_precision value: 86.19367209971237 - type: dot_recall value: 89.9 - type: euclidean_accuracy value: 99.7940594059406 - type: euclidean_ap value: 94.85459757524379 - type: euclidean_f1 value: 89.62779156327544 - type: euclidean_precision value: 88.96551724137932 - type: euclidean_recall value: 90.3 - type: manhattan_accuracy value: 99.79009900990098 - type: manhattan_ap value: 94.76971336654465 - type: manhattan_f1 value: 89.35323383084577 - type: manhattan_precision value: 88.91089108910892 - type: manhattan_recall value: 89.8 - type: max_accuracy value: 99.7940594059406 - type: max_ap value: 94.86690691995835 - type: max_f1 value: 89.62779156327544 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 55.38197670064987 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 33.08330158937971 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 49.50367079063226 - type: mrr value: 50.30444943128768 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.37739520909561 - type: cos_sim_spearman value: 31.548500943973913 - type: dot_pearson value: 29.983610104303 - type: dot_spearman value: 29.90185869098618 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.198 - type: map_at_10 value: 1.5810000000000002 - type: map_at_100 value: 9.064 - type: map_at_1000 value: 22.161 - type: map_at_3 value: 0.536 - type: map_at_5 value: 0.8370000000000001 - type: mrr_at_1 value: 80.0 - type: mrr_at_10 value: 86.75 - type: mrr_at_100 value: 86.799 - type: mrr_at_1000 value: 86.799 - type: mrr_at_3 value: 85.0 - type: mrr_at_5 value: 86.5 - type: ndcg_at_1 value: 73.0 - type: ndcg_at_10 value: 65.122 - type: ndcg_at_100 value: 51.853 - type: ndcg_at_1000 value: 47.275 - type: ndcg_at_3 value: 66.274 - type: ndcg_at_5 value: 64.826 - type: precision_at_1 value: 80.0 - type: precision_at_10 value: 70.19999999999999 - type: precision_at_100 value: 53.480000000000004 - type: precision_at_1000 value: 20.946 - type: precision_at_3 value: 71.333 - type: precision_at_5 value: 70.0 - type: recall_at_1 value: 0.198 - type: recall_at_10 value: 1.884 - type: recall_at_100 value: 12.57 - type: recall_at_1000 value: 44.208999999999996 - type: recall_at_3 value: 0.5890000000000001 - type: recall_at_5 value: 0.95 - task: type: Clustering dataset: type: slvnwhrl/tenkgnad-clustering-p2p name: MTEB TenKGnadClusteringP2P config: default split: test revision: 5c59e41555244b7e45c9a6be2d720ab4bafae558 metrics: - type: v_measure value: 42.84199261133083 - task: type: Clustering dataset: type: slvnwhrl/tenkgnad-clustering-s2s name: MTEB TenKGnadClusteringS2S config: default split: test revision: 6cddbe003f12b9b140aec477b583ac4191f01786 metrics: - type: v_measure value: 23.689557114798838 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.941 - type: map_at_10 value: 8.222 - type: map_at_100 value: 14.277999999999999 - type: map_at_1000 value: 15.790000000000001 - type: map_at_3 value: 4.4670000000000005 - type: map_at_5 value: 5.762 - type: mrr_at_1 value: 24.490000000000002 - type: mrr_at_10 value: 38.784 - type: mrr_at_100 value: 39.724 - type: mrr_at_1000 value: 39.724 - type: mrr_at_3 value: 33.333 - type: mrr_at_5 value: 37.415 - type: ndcg_at_1 value: 22.448999999999998 - type: ndcg_at_10 value: 21.026 - type: ndcg_at_100 value: 33.721000000000004 - type: ndcg_at_1000 value: 45.045 - type: ndcg_at_3 value: 20.053 - type: ndcg_at_5 value: 20.09 - type: precision_at_1 value: 24.490000000000002 - type: precision_at_10 value: 19.796 - type: precision_at_100 value: 7.469 - type: precision_at_1000 value: 1.48 - type: precision_at_3 value: 21.769 - type: precision_at_5 value: 21.224 - type: recall_at_1 value: 1.941 - type: recall_at_10 value: 14.915999999999999 - type: recall_at_100 value: 46.155 - type: recall_at_1000 value: 80.664 - type: recall_at_3 value: 5.629 - type: recall_at_5 value: 8.437 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 69.64800000000001 - type: ap value: 12.914826731261094 - type: f1 value: 53.05213503422915 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 60.427277872099594 - type: f1 value: 60.78292007556828 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 40.48134168406559 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 84.79465935506944 - type: cos_sim_ap value: 70.24589055290592 - type: cos_sim_f1 value: 65.0994575045208 - type: cos_sim_precision value: 63.76518218623482 - type: cos_sim_recall value: 66.49076517150397 - type: dot_accuracy value: 84.63968528342374 - type: dot_ap value: 69.84683095084355 - type: dot_f1 value: 64.50606169727523 - type: dot_precision value: 59.1719885487778 - type: dot_recall value: 70.89709762532982 - type: euclidean_accuracy value: 84.76485664898374 - type: euclidean_ap value: 70.20556438685551 - type: euclidean_f1 value: 65.06796614516543 - type: euclidean_precision value: 63.29840319361277 - type: euclidean_recall value: 66.93931398416886 - type: manhattan_accuracy value: 84.72313286046374 - type: manhattan_ap value: 70.17151475534308 - type: manhattan_f1 value: 65.31379180759113 - type: manhattan_precision value: 62.17505366086334 - type: manhattan_recall value: 68.7862796833773 - type: max_accuracy value: 84.79465935506944 - type: max_ap value: 70.24589055290592 - type: max_f1 value: 65.31379180759113 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.95874568246207 - type: cos_sim_ap value: 85.82517548264127 - type: cos_sim_f1 value: 78.22288041466125 - type: cos_sim_precision value: 75.33875338753387 - type: cos_sim_recall value: 81.33661841700031 - type: dot_accuracy value: 88.836496293709 - type: dot_ap value: 85.53430720252186 - type: dot_f1 value: 78.10616085869725 - type: dot_precision value: 74.73269555430501 - type: dot_recall value: 81.79858330766862 - type: euclidean_accuracy value: 88.92769821865176 - 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type: mrr_at_1000 value: 45.871 - type: mrr_at_3 value: 42.63 - type: mrr_at_5 value: 44.104 - type: ndcg_at_1 value: 29.215000000000003 - type: ndcg_at_10 value: 22.694 - type: ndcg_at_100 value: 22.242 - type: ndcg_at_1000 value: 27.069 - type: ndcg_at_3 value: 27.641 - type: ndcg_at_5 value: 25.503999999999998 - type: precision_at_1 value: 35.65 - type: precision_at_10 value: 12.795000000000002 - type: precision_at_100 value: 3.354 - type: precision_at_1000 value: 0.743 - type: precision_at_3 value: 23.403 - type: precision_at_5 value: 18.474 - type: recall_at_1 value: 3.9539999999999997 - type: recall_at_10 value: 11.301 - type: recall_at_100 value: 22.919999999999998 - type: recall_at_1000 value: 40.146 - type: recall_at_3 value: 7.146 - type: recall_at_5 value: 8.844000000000001 - task: type: Retrieval dataset: type: jinaai/xmarket_de name: MTEB XMarket config: default split: test revision: 2336818db4c06570fcdf263e1bcb9993b786f67a metrics: - type: map_at_1 value: 4.872 - type: map_at_10 value: 10.658 - type: map_at_100 value: 13.422999999999998 - type: map_at_1000 value: 14.245 - type: map_at_3 value: 7.857 - type: map_at_5 value: 9.142999999999999 - type: mrr_at_1 value: 16.744999999999997 - type: mrr_at_10 value: 24.416 - type: mrr_at_100 value: 25.432 - type: mrr_at_1000 value: 25.502999999999997 - type: mrr_at_3 value: 22.096 - type: mrr_at_5 value: 23.421 - type: ndcg_at_1 value: 16.695999999999998 - type: ndcg_at_10 value: 18.66 - type: ndcg_at_100 value: 24.314 - type: ndcg_at_1000 value: 29.846 - type: ndcg_at_3 value: 17.041999999999998 - type: ndcg_at_5 value: 17.585 - type: precision_at_1 value: 16.695999999999998 - type: precision_at_10 value: 10.374 - type: precision_at_100 value: 3.988 - type: precision_at_1000 value: 1.1860000000000002 - type: precision_at_3 value: 14.21 - type: precision_at_5 value: 12.623000000000001 - type: recall_at_1 value: 4.872 - type: recall_at_10 value: 18.624 - type: recall_at_100 value: 40.988 - type: recall_at_1000 value: 65.33 - type: recall_at_3 value: 10.162 - type: recall_at_5 value: 13.517999999999999 --- <!-- TODO: add evaluation results here --> <br><br> <p align="center"> <img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Jina AI logo: Jina AI is your Portal to Multimodal AI" width="150px"> </p> <p align="center"> <b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b> </p> ## Quick Start The easiest way to starting using `jina-embeddings-v2-base-de` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/). ## Intended Usage & Model Info `jina-embeddings-v2-base-de` is a German/English bilingual text **embedding model** supporting **8192 sequence length**. It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of [ALiBi](https://arxiv.org/abs/2108.12409) to allow longer sequence length. We have designed it for high performance in mono-lingual & cross-lingual applications and trained it specifically to support mixed German-English input without bias. Additionally, we provide the following embedding models: `jina-embeddings-v2-base-de` ist ein zweisprachiges **Text Embedding Modell** für Deutsch und Englisch, welches Texteingaben mit einer Länge von bis zu **8192 Token unterstützt**. Es basiert auf der adaptierten Bert-Modell-Architektur JinaBERT, welche mithilfe einer symmetrische Variante von [ALiBi](https://arxiv.org/abs/2108.12409) längere Eingabetexte erlaubt. Wir haben, das Model für hohe Performance in einsprachigen und cross-lingual Anwendungen entwickelt und speziell darauf trainiert, gemischte deutsch-englische Eingaben ohne einen Bias zu kodieren. Des Weiteren stellen wir folgende Embedding-Modelle bereit: - [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters. - [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters. - [`jina-embeddings-v2-base-zh`](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh): 161 million parameters Chinese-English Bilingual embeddings. - [`jina-embeddings-v2-base-de`](https://huggingface.co/jinaai/jina-embeddings-v2-base-de): 161 million parameters German-English Bilingual embeddings **(you are here)**. - [`jina-embeddings-v2-base-es`](): Spanish-English Bilingual embeddings (soon). - [`jina-embeddings-v2-base-code`](https://huggingface.co/jinaai/jina-embeddings-v2-base-code): 161 million parameters code embeddings. ## Data & Parameters The data and training details are described in this [technical report](https://arxiv.org/abs/2402.17016). ## Usage **<details><summary>Please apply mean pooling when integrating the model.</summary>** <p> ### Why mean pooling? `mean poooling` takes all token embeddings from model output and averaging them at sentence/paragraph level. It has been proved to be the most effective way to produce high-quality sentence embeddings. We offer an `encode` function to deal with this. However, if you would like to do it without using the default `encode` function: ```python import torch import torch.nn.functional as F from transformers import AutoTokenizer, AutoModel def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) sentences = ['How is the weather today?', 'What is the current weather like today?'] tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v2-base-de') model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-de', trust_remote_code=True) encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') with torch.no_grad(): model_output = model(**encoded_input) embeddings = mean_pooling(model_output, encoded_input['attention_mask']) embeddings = F.normalize(embeddings, p=2, dim=1) ``` </p> </details> You can use Jina Embedding models directly from transformers package. ```python !pip install transformers from transformers import AutoModel from numpy.linalg import norm cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b)) model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-de', trust_remote_code=True) # trust_remote_code is needed to use the encode method embeddings = model.encode(['How is the weather today?', 'Wie ist das Wetter heute?']) print(cos_sim(embeddings[0], embeddings[1])) ``` If you only want to handle shorter sequence, such as 2k, pass the `max_length` parameter to the `encode` function: ```python embeddings = model.encode( ['Very long ... document'], max_length=2048 ) ``` Using the its latest release (v2.3.0) sentence-transformers also supports Jina embeddings (Please make sure that you are logged into huggingface as well): ```python !pip install -U sentence-transformers from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( "jinaai/jina-embeddings-v2-base-de", # switch to en/zh for English or Chinese trust_remote_code=True ) # control your input sequence length up to 8192 model.max_seq_length = 1024 embeddings = model.encode([ 'How is the weather today?', 'Wie ist das Wetter heute?' ]) print(cos_sim(embeddings[0], embeddings[1])) ``` ## Alternatives to Using Transformers Package 1. _Managed SaaS_: Get started with a free key on Jina AI's [Embedding API](https://jina.ai/embeddings/). 2. _Private and high-performance deployment_: Get started by picking from our suite of models and deploy them on [AWS Sagemaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy). ## Benchmark Results We evaluated our Bilingual model on all German and English evaluation tasks availble on the [MTEB benchmark](https://huggingface.co/blog/mteb). In addition, we evaluated the models agains a couple of other German, English, and multilingual models on additional German evaluation tasks: <img src="de_evaluation_results.png" width="780px"> ## Use Jina Embeddings for RAG According to the latest blog post from [LLamaIndex](https://blog.llamaindex.ai/boosting-rag-picking-the-best-embedding-reranker-models-42d079022e83), > In summary, to achieve the peak performance in both hit rate and MRR, the combination of OpenAI or JinaAI-Base embeddings with the CohereRerank/bge-reranker-large reranker stands out. <img src="https://miro.medium.com/v2/resize:fit:4800/format:webp/1*ZP2RVejCZovF3FDCg-Bx3A.png" width="780px"> ## Contact Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas. ## Citation If you find Jina Embeddings useful in your research, please cite the following paper: ``` @article{mohr2024multi, title={Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings}, author={Mohr, Isabelle and Krimmel, Markus and Sturua, Saba and Akram, Mohammad Kalim and Koukounas, Andreas and G{\"u}nther, Michael and Mastrapas, Georgios and Ravishankar, Vinit and Mart{\'\i}nez, Joan Fontanals and Wang, Feng and others}, journal={arXiv preprint arXiv:2402.17016}, year={2024} } ```
bartowski/Hathor_Stable-L3-8B-v0.5-GGUF
bartowski
"2024-06-22T18:00:51Z"
22,046
0
null
[ "gguf", "text-generation", "en", "license:other", "region:us" ]
text-generation
"2024-06-22T16:54:54Z"
--- license: other language: - en quantized_by: bartowski pipeline_tag: text-generation --- ## Llamacpp imatrix Quantizations of Hathor_Stable-L3-8B-v0.5 Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3197">b3197</a> for quantization. Original model: https://huggingface.co/Nitral-AI/Hathor_Stable-L3-8B-v0.5 All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) ## Prompt format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> {prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ## Download a file (not the whole branch) from below: | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [Hathor_Stable-L3-8B-v0.5-Q8_0_L.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF/blob/main/Hathor_Stable-L3-8B-v0.5-Q8_1.gguf) | Q8_0_L | 9.52GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Extremely high quality, generally unneeded but max available quant. | | [Hathor_Stable-L3-8B-v0.5-Q8_0.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF/blob/main/Hathor_Stable-L3-8B-v0.5-Q8_0.gguf) | Q8_0 | 8.54GB | Extremely high quality, generally unneeded but max available quant. | | [Hathor_Stable-L3-8B-v0.5-Q6_K_L.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF/blob/main/Hathor_Stable-L3-8B-v0.5-Q6_K_L.gguf) | Q6_K_L | 7.83GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Very high quality, near perfect, *recommended*. | | [Hathor_Stable-L3-8B-v0.5-Q6_K.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF/blob/main/Hathor_Stable-L3-8B-v0.5-Q6_K.gguf) | Q6_K | 6.59GB | Very high quality, near perfect, *recommended*. | | [Hathor_Stable-L3-8B-v0.5-Q5_K_L.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF/blob/main/Hathor_Stable-L3-8B-v0.5-Q5_K_L.gguf) | Q5_K_L | 7.04GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. High quality, *recommended*. | | [Hathor_Stable-L3-8B-v0.5-Q5_K_M.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF/blob/main/Hathor_Stable-L3-8B-v0.5-Q5_K_M.gguf) | Q5_K_M | 5.73GB | High quality, *recommended*. | | [Hathor_Stable-L3-8B-v0.5-Q5_K_S.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF/blob/main/Hathor_Stable-L3-8B-v0.5-Q5_K_S.gguf) | Q5_K_S | 5.59GB | High quality, *recommended*. | | [Hathor_Stable-L3-8B-v0.5-Q4_K_L.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF/blob/main/Hathor_Stable-L3-8B-v0.5-Q4_K_L.gguf) | Q4_K_L | 6.29GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Good quality, uses about 4.83 bits per weight, *recommended*. | | [Hathor_Stable-L3-8B-v0.5-Q4_K_M.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF/blob/main/Hathor_Stable-L3-8B-v0.5-Q4_K_M.gguf) | Q4_K_M | 4.92GB | Good quality, uses about 4.83 bits per weight, *recommended*. | | [Hathor_Stable-L3-8B-v0.5-Q4_K_S.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF/blob/main/Hathor_Stable-L3-8B-v0.5-Q4_K_S.gguf) | Q4_K_S | 4.69GB | Slightly lower quality with more space savings, *recommended*. | | [Hathor_Stable-L3-8B-v0.5-IQ4_XS.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF/blob/main/Hathor_Stable-L3-8B-v0.5-IQ4_XS.gguf) | IQ4_XS | 4.44GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. | | [Hathor_Stable-L3-8B-v0.5-Q3_K_XL.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF//main/Hathor_Stable-L3-8B-v0.5-Q3_K_XL.gguf) | Q3_K_XL | | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Lower quality but usable, good for low RAM availability. | | [Hathor_Stable-L3-8B-v0.5-Q3_K_L.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF/blob/main/Hathor_Stable-L3-8B-v0.5-Q3_K_L.gguf) | Q3_K_L | 4.32GB | Lower quality but usable, good for low RAM availability. | | [Hathor_Stable-L3-8B-v0.5-Q3_K_M.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF/blob/main/Hathor_Stable-L3-8B-v0.5-Q3_K_M.gguf) | Q3_K_M | 4.01GB | Even lower quality. | | [Hathor_Stable-L3-8B-v0.5-IQ3_M.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF/blob/main/Hathor_Stable-L3-8B-v0.5-IQ3_M.gguf) | IQ3_M | 3.78GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. | | [Hathor_Stable-L3-8B-v0.5-Q3_K_S.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF/blob/main/Hathor_Stable-L3-8B-v0.5-Q3_K_S.gguf) | Q3_K_S | 3.66GB | Low quality, not recommended. | | [Hathor_Stable-L3-8B-v0.5-IQ3_XS.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF/blob/main/Hathor_Stable-L3-8B-v0.5-IQ3_XS.gguf) | IQ3_XS | 3.51GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. | | [Hathor_Stable-L3-8B-v0.5-IQ3_XXS.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF/blob/main/Hathor_Stable-L3-8B-v0.5-IQ3_XXS.gguf) | IQ3_XXS | 3.27GB | Lower quality, new method with decent performance, comparable to Q3 quants. | | [Hathor_Stable-L3-8B-v0.5-Q2_K.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF/blob/main/Hathor_Stable-L3-8B-v0.5-Q2_K.gguf) | Q2_K | 3.17GB | Very low quality but surprisingly usable. | | [Hathor_Stable-L3-8B-v0.5-IQ2_M.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF/blob/main/Hathor_Stable-L3-8B-v0.5-IQ2_M.gguf) | IQ2_M | 2.94GB | Very low quality, uses SOTA techniques to also be surprisingly usable. | | [Hathor_Stable-L3-8B-v0.5-IQ2_S.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF/blob/main/Hathor_Stable-L3-8B-v0.5-IQ2_S.gguf) | IQ2_S | 2.75GB | Very low quality, uses SOTA techniques to be usable. | | [Hathor_Stable-L3-8B-v0.5-IQ2_XS.gguf](https://huggingface.co/bartowski/Hathor_Stable-L3-8B-v0.5-GGUF/blob/main/Hathor_Stable-L3-8B-v0.5-IQ2_XS.gguf) | IQ2_XS | 2.60GB | Very low quality, uses SOTA techniques to be usable. | ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/Hathor_Stable-L3-8B-v0.5-GGUF --include "Hathor_Stable-L3-8B-v0.5-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/Hathor_Stable-L3-8B-v0.5-GGUF --include "Hathor_Stable-L3-8B-v0.5-Q8_0.gguf/*" --local-dir Hathor_Stable-L3-8B-v0.5-Q8_0 ``` You can either specify a new local-dir (Hathor_Stable-L3-8B-v0.5-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
kwoncho/gaincut_news_pre2019
kwoncho
"2024-06-15T04:54:26Z"
22,034
0
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
"2024-04-25T04:36:30Z"
Entry not found
kwoncho/gaincut_news_pre2018
kwoncho
"2024-06-15T04:51:14Z"
22,028
0
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
"2024-04-25T04:33:16Z"
Entry not found
segment-any-text/sat-12l-sm
segment-any-text
"2024-06-26T08:26:21Z"
22,019
3
transformers
[ "transformers", "safetensors", "xlm-token", "token-classification", "multilingual", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hu", "hy", "id", "ig", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "mt", "my", "ne", "nl", "no", "pa", "pl", "ps", "pt", "ro", "ru", "si", "sk", "sl", "sq", "sr", "sv", "ta", "te", "tg", "th", "tr", "uk", "ur", "uz", "vi", "xh", "yi", "yo", "zh", "zu", "arxiv:2406.16678", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
"2024-06-16T09:19:21Z"
--- license: mit language: - multilingual - am - ar - az - be - bg - bn - ca - ceb - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hu - hy - id - ig - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lt - lv - mg - mk - ml - mn - mr - ms - mt - my - ne - nl - no - pa - pl - ps - pt - ro - ru - si - sk - sl - sq - sr - sv - ta - te - tg - th - tr - uk - ur - uz - vi - xh - yi - yo - zh - zu library: - wtpsplit --- # sat-12l-sm Model for [`wtpsplit`](https://github.com/segment-any-text/wtpsplit). State-of-the-art sentence segmentation with 12 Transfomer layers. For details, see our [`Segment any Text` paper](arxiv.org/abs/2406.16678)
google/owlv2-large-patch14-ensemble
google
"2024-04-15T16:58:10Z"
22,002
6
transformers
[ "transformers", "pytorch", "safetensors", "owlv2", "zero-shot-object-detection", "vision", "arxiv:2306.09683", "license:apache-2.0", "region:us" ]
zero-shot-object-detection
"2023-10-13T12:44:06Z"
--- license: apache-2.0 tags: - vision - zero-shot-object-detection inference: false --- # Model Card: OWLv2 ## Model Details The OWLv2 model (short for Open-World Localization) was proposed in [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) by Matthias Minderer, Alexey Gritsenko, Neil Houlsby. OWLv2, like OWL-ViT, is a zero-shot text-conditioned object detection model that can be used to query an image with one or multiple text queries. The model uses CLIP as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features. To use CLIP for detection, OWL-ViT removes the final token pooling layer of the vision model and attaches a lightweight classification and box head to each transformer output token. Open-vocabulary classification is enabled by replacing the fixed classification layer weights with the class-name embeddings obtained from the text model. The authors first train CLIP from scratch and fine-tune it end-to-end with the classification and box heads on standard detection datasets using a bipartite matching loss. One or multiple text queries per image can be used to perform zero-shot text-conditioned object detection. ### Model Date June 2023 ### Model Type The model uses a CLIP backbone with a ViT-L/14 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. The CLIP backbone is trained from scratch and fine-tuned together with the box and class prediction heads with an object detection objective. ### Documents - [OWLv2 Paper](https://arxiv.org/abs/2306.09683) ### Use with Transformers ```python import requests from PIL import Image import numpy as np import torch from transformers import AutoProcessor, Owlv2ForObjectDetection from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD processor = AutoProcessor.from_pretrained("google/owlv2-large-patch14-ensemble") model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-large-patch14-ensemble") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) texts = [["a photo of a cat", "a photo of a dog"]] inputs = processor(text=texts, images=image, return_tensors="pt") # forward pass with torch.no_grad(): outputs = model(**inputs) # Note: boxes need to be visualized on the padded, unnormalized image # hence we'll set the target image sizes (height, width) based on that def get_preprocessed_image(pixel_values): pixel_values = pixel_values.squeeze().numpy() unnormalized_image = (pixel_values * np.array(OPENAI_CLIP_STD)[:, None, None]) + np.array(OPENAI_CLIP_MEAN)[:, None, None] unnormalized_image = (unnormalized_image * 255).astype(np.uint8) unnormalized_image = np.moveaxis(unnormalized_image, 0, -1) unnormalized_image = Image.fromarray(unnormalized_image) return unnormalized_image unnormalized_image = get_preprocessed_image(inputs.pixel_values) target_sizes = torch.Tensor([unnormalized_image.size[::-1]]) # Convert outputs (bounding boxes and class logits) to final bounding boxes and scores results = processor.post_process_object_detection( outputs=outputs, threshold=0.2, target_sizes=target_sizes ) i = 0 # Retrieve predictions for the first image for the corresponding text queries text = texts[i] boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"] for box, score, label in zip(boxes, scores, labels): box = [round(i, 2) for i in box.tolist()] print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}") ``` ## Model Use ### Intended Use The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, text-conditioned object detection. We also hope it can be used for interdisciplinary studies of the potential impact of such models, especially in areas that commonly require identifying objects whose label is unavailable during training. #### Primary intended uses The primary intended users of these models are AI researchers. We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models. ## Data The CLIP backbone of the model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet. The prediction heads of OWL-ViT, along with the CLIP backbone, are fine-tuned on publicly available object detection datasets such as [COCO](https://cocodataset.org/#home) and [OpenImages](https://storage.googleapis.com/openimages/web/index.html). (to be updated for v2) ### BibTeX entry and citation info ```bibtex @misc{minderer2023scaling, title={Scaling Open-Vocabulary Object Detection}, author={Matthias Minderer and Alexey Gritsenko and Neil Houlsby}, year={2023}, eprint={2306.09683}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```
mradermacher/Nova-13B-GGUF
mradermacher
"2024-06-24T16:19:33Z"
21,999
1
transformers
[ "transformers", "gguf", "en", "dataset:garage-bAInd/Open-Platypus", "base_model:Weyaxi/Nova-13B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]
null
"2024-06-24T15:33:13Z"
--- base_model: Weyaxi/Nova-13B datasets: - garage-bAInd/Open-Platypus language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Weyaxi/Nova-13B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Nova-13B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Nova-13B-GGUF/resolve/main/Nova-13B.Q2_K.gguf) | Q2_K | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/Nova-13B-GGUF/resolve/main/Nova-13B.IQ3_XS.gguf) | IQ3_XS | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/Nova-13B-GGUF/resolve/main/Nova-13B.IQ3_S.gguf) | IQ3_S | 5.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Nova-13B-GGUF/resolve/main/Nova-13B.Q3_K_S.gguf) | Q3_K_S | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Nova-13B-GGUF/resolve/main/Nova-13B.IQ3_M.gguf) | IQ3_M | 6.1 | | | [GGUF](https://huggingface.co/mradermacher/Nova-13B-GGUF/resolve/main/Nova-13B.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Nova-13B-GGUF/resolve/main/Nova-13B.Q3_K_L.gguf) | Q3_K_L | 7.0 | | | [GGUF](https://huggingface.co/mradermacher/Nova-13B-GGUF/resolve/main/Nova-13B.IQ4_XS.gguf) | IQ4_XS | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/Nova-13B-GGUF/resolve/main/Nova-13B.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Nova-13B-GGUF/resolve/main/Nova-13B.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Nova-13B-GGUF/resolve/main/Nova-13B.Q5_K_S.gguf) | Q5_K_S | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/Nova-13B-GGUF/resolve/main/Nova-13B.Q5_K_M.gguf) | Q5_K_M | 9.3 | | | [GGUF](https://huggingface.co/mradermacher/Nova-13B-GGUF/resolve/main/Nova-13B.Q6_K.gguf) | Q6_K | 10.8 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Nova-13B-GGUF/resolve/main/Nova-13B.Q8_0.gguf) | Q8_0 | 13.9 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/Blue-Orchid-2x7b-i1-GGUF
mradermacher
"2024-06-30T11:39:58Z"
21,991
0
transformers
[ "transformers", "gguf", "en", "base_model:nakodanei/Blue-Orchid-2x7b", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
"2024-06-30T08:50:37Z"
--- base_model: nakodanei/Blue-Orchid-2x7b language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/nakodanei/Blue-Orchid-2x7b <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Blue-Orchid-2x7b-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Blue-Orchid-2x7b-i1-GGUF/resolve/main/Blue-Orchid-2x7b.i1-IQ1_S.gguf) | i1-IQ1_S | 2.8 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Blue-Orchid-2x7b-i1-GGUF/resolve/main/Blue-Orchid-2x7b.i1-IQ1_M.gguf) | i1-IQ1_M | 3.1 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Blue-Orchid-2x7b-i1-GGUF/resolve/main/Blue-Orchid-2x7b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Blue-Orchid-2x7b-i1-GGUF/resolve/main/Blue-Orchid-2x7b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/Blue-Orchid-2x7b-i1-GGUF/resolve/main/Blue-Orchid-2x7b.i1-IQ2_S.gguf) | i1-IQ2_S | 4.1 | | | [GGUF](https://huggingface.co/mradermacher/Blue-Orchid-2x7b-i1-GGUF/resolve/main/Blue-Orchid-2x7b.i1-IQ2_M.gguf) | i1-IQ2_M | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/Blue-Orchid-2x7b-i1-GGUF/resolve/main/Blue-Orchid-2x7b.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Blue-Orchid-2x7b-i1-GGUF/resolve/main/Blue-Orchid-2x7b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Blue-Orchid-2x7b-i1-GGUF/resolve/main/Blue-Orchid-2x7b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/Blue-Orchid-2x7b-i1-GGUF/resolve/main/Blue-Orchid-2x7b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.7 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Blue-Orchid-2x7b-i1-GGUF/resolve/main/Blue-Orchid-2x7b.i1-IQ3_S.gguf) | i1-IQ3_S | 5.7 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Blue-Orchid-2x7b-i1-GGUF/resolve/main/Blue-Orchid-2x7b.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Blue-Orchid-2x7b-i1-GGUF/resolve/main/Blue-Orchid-2x7b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.3 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Blue-Orchid-2x7b-i1-GGUF/resolve/main/Blue-Orchid-2x7b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.8 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Blue-Orchid-2x7b-i1-GGUF/resolve/main/Blue-Orchid-2x7b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 7.0 | | | [GGUF](https://huggingface.co/mradermacher/Blue-Orchid-2x7b-i1-GGUF/resolve/main/Blue-Orchid-2x7b.i1-Q4_0.gguf) | i1-Q4_0 | 7.4 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Blue-Orchid-2x7b-i1-GGUF/resolve/main/Blue-Orchid-2x7b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.4 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Blue-Orchid-2x7b-i1-GGUF/resolve/main/Blue-Orchid-2x7b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Blue-Orchid-2x7b-i1-GGUF/resolve/main/Blue-Orchid-2x7b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 9.0 | | | [GGUF](https://huggingface.co/mradermacher/Blue-Orchid-2x7b-i1-GGUF/resolve/main/Blue-Orchid-2x7b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 9.2 | | | [GGUF](https://huggingface.co/mradermacher/Blue-Orchid-2x7b-i1-GGUF/resolve/main/Blue-Orchid-2x7b.i1-Q6_K.gguf) | i1-Q6_K | 10.7 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->
kwoncho/gaincut_news_pre2020
kwoncho
"2024-06-15T04:57:37Z"
21,984
0
transformers
[ "transformers", "pytorch", "roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
"2024-04-25T04:39:49Z"
Entry not found
bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12
bionlp
"2021-09-24T07:46:11Z"
21,983
18
transformers
[ "transformers", "pytorch", "jax", "bert", "bluebert", "en", "dataset:PubMed", "dataset:MIMIC-III", "license:cc0-1.0", "endpoints_compatible", "region:us" ]
null
"2022-03-02T23:29:05Z"
--- language: - en tags: - bert - bluebert license: cc0-1.0 datasets: - PubMed - MIMIC-III --- # BlueBert-Base, Uncased, PubMed and MIMIC-III ## Model description A BERT model pre-trained on PubMed abstracts and clinical notes ([MIMIC-III](https://mimic.physionet.org/)). ## Intended uses & limitations #### How to use Please see https://github.com/ncbi-nlp/bluebert ## Training data We provide [preprocessed PubMed texts](https://ftp.ncbi.nlm.nih.gov/pub/lu/Suppl/NCBI-BERT/pubmed_uncased_sentence_nltk.txt.tar.gz) that were used to pre-train the BlueBERT models. The corpus contains ~4000M words extracted from the [PubMed ASCII code version](https://www.ncbi.nlm.nih.gov/research/bionlp/APIs/BioC-PubMed/). Pre-trained model: https://huggingface.co/bert-base-uncased ## Training procedure * lowercasing the text * removing speical chars `\x00`-`\x7F` * tokenizing the text using the [NLTK Treebank tokenizer](https://www.nltk.org/_modules/nltk/tokenize/treebank.html) Below is a code snippet for more details. ```python value = value.lower() value = re.sub(r'[\r\n]+', ' ', value) value = re.sub(r'[^\x00-\x7F]+', ' ', value) tokenized = TreebankWordTokenizer().tokenize(value) sentence = ' '.join(tokenized) sentence = re.sub(r"\s's\b", "'s", sentence) ``` ### BibTeX entry and citation info ```bibtex @InProceedings{peng2019transfer, author = {Yifan Peng and Shankai Yan and Zhiyong Lu}, title = {Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets}, booktitle = {Proceedings of the 2019 Workshop on Biomedical Natural Language Processing (BioNLP 2019)}, year = {2019}, pages = {58--65}, } ``` ### Acknowledgments This work was supported by the Intramural Research Programs of the National Institutes of Health, National Library of Medicine and Clinical Center. This work was supported by the National Library of Medicine of the National Institutes of Health under award number 4R00LM013001-01. We are also grateful to the authors of BERT and ELMo to make the data and codes publicly available. We would like to thank Dr Sun Kim for processing the PubMed texts. ### Disclaimer This tool shows the results of research conducted in the Computational Biology Branch, NCBI. The information produced on this website is not intended for direct diagnostic use or medical decision-making without review and oversight by a clinical professional. Individuals should not change their health behavior solely on the basis of information produced on this website. NIH does not independently verify the validity or utility of the information produced by this tool. If you have questions about the information produced on this website, please see a health care professional. More information about NCBI's disclaimer policy is available.
TheBloke/Llama-2-7B-GGUF
TheBloke
"2023-10-24T07:32:45Z"
21,944
167
transformers
[ "transformers", "gguf", "llama", "facebook", "meta", "pytorch", "llama-2", "text-generation", "en", "arxiv:2307.09288", "base_model:meta-llama/Llama-2-7b-hf", "license:llama2", "text-generation-inference", "region:us" ]
text-generation
"2023-09-04T15:53:57Z"
--- language: - en license: llama2 tags: - facebook - meta - pytorch - llama - llama-2 model_name: Llama 2 7B base_model: meta-llama/Llama-2-7b-hf inference: false model_creator: Meta model_type: llama pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Llama 2 7B - GGUF - Model creator: [Meta](https://huggingface.co/meta-llama) - Original model: [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b-hf) <!-- description start --> ## Description This repo contains GGUF format model files for [Meta's Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b-hf). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Llama-2-7B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-7B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama-2-7B-GGUF) * [Meta's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/meta-llama/Llama-2-7b-hf) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: None ``` {prompt} ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [llama-2-7b.Q2_K.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q2_K.gguf) | Q2_K | 2 | 2.83 GB| 5.33 GB | smallest, significant quality loss - not recommended for most purposes | | [llama-2-7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q3_K_S.gguf) | Q3_K_S | 3 | 2.95 GB| 5.45 GB | very small, high quality loss | | [llama-2-7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q3_K_M.gguf) | Q3_K_M | 3 | 3.30 GB| 5.80 GB | very small, high quality loss | | [llama-2-7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q3_K_L.gguf) | Q3_K_L | 3 | 3.60 GB| 6.10 GB | small, substantial quality loss | | [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) | Q4_0 | 4 | 3.83 GB| 6.33 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [llama-2-7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_K_S.gguf) | Q4_K_S | 4 | 3.86 GB| 6.36 GB | small, greater quality loss | | [llama-2-7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_K_M.gguf) | Q4_K_M | 4 | 4.08 GB| 6.58 GB | medium, balanced quality - recommended | | [llama-2-7b.Q5_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q5_0.gguf) | Q5_0 | 5 | 4.65 GB| 7.15 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [llama-2-7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q5_K_S.gguf) | Q5_K_S | 5 | 4.65 GB| 7.15 GB | large, low quality loss - recommended | | [llama-2-7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q5_K_M.gguf) | Q5_K_M | 5 | 4.78 GB| 7.28 GB | large, very low quality loss - recommended | | [llama-2-7b.Q6_K.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q6_K.gguf) | Q6_K | 6 | 5.53 GB| 8.03 GB | very large, extremely low quality loss | | [llama-2-7b.Q8_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q8_0.gguf) | Q8_0 | 8 | 7.16 GB| 9.66 GB | very large, extremely low quality loss - not recommended | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: - LM Studio - LoLLMS Web UI - Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/Llama-2-7B-GGUF and below it, a specific filename to download, such as: llama-2-7b.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub>=0.17.1 ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/Llama-2-7B-GGUF llama-2-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/Llama-2-7B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Llama-2-7B-GGUF llama-2-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m llama-2-7b.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model from Python using ctransformers #### First install the package ```bash # Base ctransformers with no GPU acceleration pip install ctransformers>=0.2.24 # Or with CUDA GPU acceleration pip install ctransformers[cuda]>=0.2.24 # Or with ROCm GPU acceleration CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers ``` #### Simple example code to load one of these GGUF models ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-7B-GGUF", model_file="llama-2-7b.Q4_K_M.gguf", model_type="llama", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here's guides on using llama-cpp-python or ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: Meta's Llama 2 7B # **Llama 2** Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details *Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.* Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. **Model Developers** Meta **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. ||Training Data|Params|Content Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| |Llama 2|*A new mix of publicly available online data*|7B|4k|&#10007;|2.0T|3.0 x 10<sup>-4</sup>| |Llama 2|*A new mix of publicly available online data*|13B|4k|&#10007;|2.0T|3.0 x 10<sup>-4</sup>| |Llama 2|*A new mix of publicly available online data*|70B|4k|&#10004;|2.0T|1.5 x 10<sup>-4</sup>| *Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. **Model Dates** Llama 2 was trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). **Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)| |---|---|---|---| |Llama 2 7B|184320|400|31.22| |Llama 2 13B|368640|400|62.44| |Llama 2 70B|1720320|400|291.42| |Total|3311616||539.00| **CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval| |---|---|---|---|---|---|---|---|---|---| |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9| |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9| |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7| |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6| |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3| |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1| |Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**| **Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. |||TruthfulQA|Toxigen| |---|---|---|---| |Llama 1|7B|27.42|23.00| |Llama 1|13B|41.74|23.08| |Llama 1|33B|44.19|22.57| |Llama 1|65B|48.71|21.77| |Llama 2|7B|33.29|**21.25**| |Llama 2|13B|41.86|26.10| |Llama 2|70B|**50.18**|24.60| **Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). |||TruthfulQA|Toxigen| |---|---|---|---| |Llama-2-Chat|7B|57.04|**0.00**| |Llama-2-Chat|13B|62.18|**0.00**| |Llama-2-Chat|70B|**64.14**|0.01| **Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index |Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf| |---|---|---|---|---| |7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)| |13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)| |70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)| <!-- original-model-card end -->
google/gemma-2-9b-it
google
"2024-07-02T20:00:03Z"
21,924
184
transformers
[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:2009.03300", "arxiv:1905.07830", "arxiv:1911.11641", "arxiv:1904.09728", "arxiv:1905.10044", "arxiv:1907.10641", "arxiv:1811.00937", "arxiv:1809.02789", "arxiv:1911.01547", "arxiv:1705.03551", "arxiv:2107.03374", "arxiv:2108.07732", "arxiv:2110.14168", "arxiv:2009.11462", "arxiv:2101.11718", "arxiv:2110.08193", "arxiv:1804.09301", "arxiv:2109.07958", "arxiv:1804.06876", "arxiv:2103.03874", "arxiv:2304.06364", "arxiv:2206.04615", "arxiv:2203.09509", "license:gemma", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
text-generation
"2024-06-24T08:05:41Z"
--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license tags: - conversational --- # Gemma 2 model card **Model Page**: [Gemma](https://ai.google.dev/gemma/docs) **Resources and Technical Documentation**: * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma] **Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent/verify/huggingface?returnModelRepoId=google/gemma-2-9b-it) **Authors**: Google ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights for both pre-trained variants and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Usage Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. #### Running the model on a single / multi GPU ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-2-9b-it", device_map="auto", torch_dtype=torch.bfloat16 ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` <a name="precisions"></a> #### Running the model on a GPU using different precisions The native weights of this model were exported in `bfloat16` precision. You can also use `float32` if you skip the dtype, but no precision increase will occur (model weights will just be upcasted to `float32`). See examples below. * _Upcasting to `torch.float32`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-2-9b-it", device_map="auto") input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` #### Quantized Versions through `bitsandbytes` * _Using 8-bit precision (int8)_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_8bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-2-9b-it", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` * _Using 4-bit precision_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-2-9b-it", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` #### Other optimizations * _Flash Attention 2_ First make sure to install `flash-attn` in your environment `pip install flash-attn` ```diff model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, + attn_implementation="flash_attention_2" ).to(0) ``` ### Chat Template The instruction-tuned models use a chat template that must be adhered to for conversational use. The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet. Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction: ```py from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model_id = "google/gemma-2-9b-it" dtype = torch.bfloat16 tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, device_map="cuda", torch_dtype=dtype,) chat = [ { "role": "user", "content": "Write a hello world program" }, ] prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) ``` At this point, the prompt contains the following text: ``` <bos><start_of_turn>user Write a hello world program<end_of_turn> <start_of_turn>model ``` As you can see, each turn is preceded by a `<start_of_turn>` delimiter and then the role of the entity (either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with the `<end_of_turn>` token. You can follow this format to build the prompt manually, if you need to do it without the tokenizer's chat template. After the prompt is ready, generation can be performed like this: ```py inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150) print(tokenizer.decode(outputs[0])) ``` ### Inputs and outputs * **Input:** Text string, such as a question, a prompt, or a document to be summarized. * **Output:** Generated English-language text in response to the input, such as an answer to a question, or a summary of a document. ### Citation ```none @article{gemma_2024, title={Gemma}, url={https://www.kaggle.com/m/3301}, DOI={10.34740/KAGGLE/M/3301}, publisher={Kaggle}, author={Gemma Team}, year={2024} } ``` ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 13 trillion tokens and the 9B model was trained with 8 trillion tokens. Here are the key components: * Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. Primarily English-language content. * Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code or understand code-related questions. * Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. The combination of these diverse data sources is crucial for training a powerful language model that can handle a wide variety of different tasks and text formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: * CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content. * Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. * Additional methods: Filtering based on content quality and safety in line with [our policies][safety-policies]. ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using the latest generation of [Tensor Processing Unit (TPU)][tpu] hardware (TPUv5p). Training large language models requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: * Performance: TPUs are specifically designed to handle the massive computations involved in training LLMs. They can speed up training considerably compared to CPUs. * Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. * Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. * Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. * These advantages are aligned with [Google's commitments to operate sustainably][sustainability]. ### Software Training was done using [JAX][jax] and [ML Pathways][ml-pathways]. JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for [foundation models][foundation-models], including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models][gemini-2-paper]; "the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow." ## Evaluation Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: | Benchmark | Metric | Gemma PT 9B | Gemma PT 27B | | ------------------------------ | ------------- | ----------- | ------------ | | [MMLU][mmlu] | 5-shot, top-1 | 71.3 | 75.2 | | [HellaSwag][hellaswag] | 10-shot | 81.9 | 86.4 | | [PIQA][piqa] | 0-shot | 81.7 | 83.2 | | [SocialIQA][socialiqa] | 0-shot | 53.4 | 53.7 | | [BoolQ][boolq] | 0-shot | 84.2 | 84.8 | | [WinoGrande][winogrande] | partial score | 80.6 | 83.7 | | [ARC-e][arc] | 0-shot | 88.0 | 88.6 | | [ARC-c][arc] | 25-shot | 68.4 | 71.4 | | [TriviaQA][triviaqa] | 5-shot | 76.6 | 83.7 | | [Natural Questions][naturalq] | 5-shot | 29.2 | 34.5 | | [HumanEval][humaneval] | pass@1 | 40.2 | 51.8 | | [MBPP][mbpp] | 3-shot | 52.4 | 62.6 | | [GSM8K][gsm8k] | 5-shot, maj@1 | 68.6 | 74.0 | | [MATH][math] | 4-shot | 36.6 | 42.3 | | [AGIEval][agieval] | 3-5-shot | 52.8 | 55.1 | | [BIG-Bench][big-bench] | 3-shot, CoT | 68.2 | 74.9 | | ------------------------------ | ------------- | ----------- | ------------ | ## Ethics and Safety Ethics and safety evaluation approach and results. ### Evaluation Approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: * Text-to-Text Content Safety: Human evaluation on prompts covering safety policies including child sexual abuse and exploitation, harassment, violence and gore, and hate speech. * Text-to-Text Representational Harms: Benchmark against relevant academic datasets such as [WinoBias][winobias] and [BBQ Dataset][bbq]. * Memorization: Automated evaluation of memorization of training data, including the risk of personally identifiable information exposure. * Large-scale harm: Tests for "dangerous capabilities," such as chemical, biological, radiological, and nuclear (CBRN) risks. ### Evaluation Results The results of ethics and safety evaluations are within acceptable thresholds for meeting [internal policies][safety-policies] for categories such as child safety, content safety, representational harms, memorization, large-scale harms. On top of robust internal evaluations, the results of well-known safety benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA are shown here. #### Gemma 2.0 | Benchmark | Metric | Gemma 2 IT 9B | Gemma 2 IT 27B | | ------------------------ | ------------- | --------------- | ---------------- | | [RealToxicity][realtox] | average | 8.25 | 8.84 | | [CrowS-Pairs][crows] | top-1 | 37.47 | 36.67 | | [BBQ Ambig][bbq] | 1-shot, top-1 | 88.58 | 85.99 | | [BBQ Disambig][bbq] | top-1 | 82.67 | 86.94 | | [Winogender][winogender] | top-1 | 79.17 | 77.22 | | [TruthfulQA][truthfulqa] | | 50.27 | 51.60 | | [Winobias 1_2][winobias] | | 78.09 | 81.94 | | [Winobias 2_2][winobias] | | 95.32 | 97.22 | | [Toxigen][toxigen] | | 39.30 | 38.42 | | ------------------------ | ------------- | --------------- | ---------------- | ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. * Content Creation and Communication * Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. * Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. * Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. * Research and Education * Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field. * Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. * Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations * Training Data * The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. * The scope of the training dataset determines the subject areas the model can handle effectively. * Context and Task Complexity * LLMs are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. * A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). * Language Ambiguity and Nuance * Natural language is inherently complex. LLMs might struggle to grasp subtle nuances, sarcasm, or figurative language. * Factual Accuracy * LLMs generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. * Common Sense * LLMs rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of large language models (LLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: * Bias and Fairness * LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. * Misinformation and Misuse * LLMs can be misused to generate text that is false, misleading, or harmful. * Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit][rai-toolkit]. * Transparency and Accountability: * This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. * A responsibly developed open model offers the opportunity to share innovation by making LLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: * Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. * Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. * Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy][prohibited-use]. * Privacy violations: Models were trained on data filtered for removal of PII (Personally Identifiable Information). Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives. [rai-toolkit]: https://ai.google.dev/responsible [kaggle-gemma]: https://www.kaggle.com/models/google/gemma-2 [terms]: https://ai.google.dev/gemma/terms [vertex-mg-gemma]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335 [sensitive-info]: https://cloud.google.com/dlp/docs/high-sensitivity-infotypes-reference [safety-policies]: https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11 [prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy [tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu [sustainability]: https://sustainability.google/operating-sustainably/ [jax]: https://github.com/google/jax [ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ [sustainability]: https://sustainability.google/operating-sustainably/ [foundation-models]: https://ai.google/discover/foundation-models/ [gemini-2-paper]: https://goo.gle/gemma2report [mmlu]: https://arxiv.org/abs/2009.03300 [hellaswag]: https://arxiv.org/abs/1905.07830 [piqa]: https://arxiv.org/abs/1911.11641 [socialiqa]: https://arxiv.org/abs/1904.09728 [boolq]: https://arxiv.org/abs/1905.10044 [winogrande]: https://arxiv.org/abs/1907.10641 [commonsenseqa]: https://arxiv.org/abs/1811.00937 [openbookqa]: https://arxiv.org/abs/1809.02789 [arc]: https://arxiv.org/abs/1911.01547 [triviaqa]: https://arxiv.org/abs/1705.03551 [naturalq]: https://github.com/google-research-datasets/natural-questions [humaneval]: https://arxiv.org/abs/2107.03374 [mbpp]: https://arxiv.org/abs/2108.07732 [gsm8k]: https://arxiv.org/abs/2110.14168 [realtox]: https://arxiv.org/abs/2009.11462 [bold]: https://arxiv.org/abs/2101.11718 [crows]: https://aclanthology.org/2020.emnlp-main.154/ [bbq]: https://arxiv.org/abs/2110.08193v2 [winogender]: https://arxiv.org/abs/1804.09301 [truthfulqa]: https://arxiv.org/abs/2109.07958 [winobias]: https://arxiv.org/abs/1804.06876 [math]: https://arxiv.org/abs/2103.03874 [agieval]: https://arxiv.org/abs/2304.06364 [big-bench]: https://arxiv.org/abs/2206.04615 [toxigen]: https://arxiv.org/abs/2203.09509