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text-to-image	diffusers	<!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - fatimaaa1/padding_40 <Gallery /> ## Model description These are fatimaaa1/padding_40 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a bussiness card to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](fatimaaa1/padding_40/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]	{"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a bussiness card", "widget": []}	fatimaaa1/padding_40	null	[ "diffusers", "text-to-image", "diffusers-training", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	null	2024-04-26T10:05:42+00:00
text-generation	transformers	# Uploaded model - Developed by: Kaizu07 - License: apache-2.0 - Finetuned from model : BanglaLLM/bangla-llama-7b-instruct-v0.1 This llama 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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "BanglaLLM/bangla-llama-7b-instruct-v0.1"}	Kaizu07/llama2-bn-v0_4-4bit	null	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:BanglaLLM/bangla-llama-7b-instruct-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "region:us" ]	null	2024-04-26T10:06:16+00:00
automatic-speech-recognition	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	suke0327/whisper-large_even_de	null	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:06:21+00:00
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # trainer This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.3826 - eval_runtime: 19.8497 - eval_samples_per_second: 1.31 - eval_steps_per_second: 0.655 - epoch: 7.0 - step: 826 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 20 - mixed_precision_training: Native AMP ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2	{"license": "mit", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "microsoft/Phi-3-mini-128k-instruct", "model-index": [{"name": "trainer", "results": []}]}	Surabhi-K/phi3_7epochs	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:microsoft/Phi-3-mini-128k-instruct", "license:mit", "region:us" ]	null	2024-04-26T10:06:29+00:00
text-classification	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Roberta_Text_Classification This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0157 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.0005 \| 1.0 \| 580 \| 0.0159 \| \| 0.0001 \| 2.0 \| 1160 \| 0.0173 \| \| 0.0002 \| 3.0 \| 1740 \| 0.0157 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "roberta-base", "model-index": [{"name": "Roberta_Text_Classification", "results": []}]}	oumaymaMb/Roberta_Text_Classification	null	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:06:36+00:00
text-generation	transformers	# RachidAR/Llama-3-8B-Instruct-DPO-v0.3-Q6_K-GGUF This model was converted to GGUF format from [`MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3`](https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo RachidAR/Llama-3-8B-Instruct-DPO-v0.3-Q6_K-GGUF --model llama-3-8b-instruct-dpo-v0.3.Q6_K.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo RachidAR/Llama-3-8B-Instruct-DPO-v0.3-Q6_K-GGUF --model llama-3-8b-instruct-dpo-v0.3.Q6_K.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-8b-instruct-dpo-v0.3.Q6_K.gguf -n 128 ```	{"language": ["en"], "license": "llama3", "library_name": "transformers", "tags": ["axolotl", "finetune", "dpo", "facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo"], "datasets": ["Intel/orca_dpo_pairs"], "model_name": "Llama-3-8B-Instruct-DPO-v0.3", "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE", "inference": false, "model_creator": "MaziyarPanahi", "quantized_by": "MaziyarPanahi"}	RachidAR/Llama-3-8B-Instruct-DPO-v0.3-Q6_K-GGUF	null	[ "transformers", "gguf", "axolotl", "finetune", "dpo", "facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo", "text-generation", "en", "dataset:Intel/orca_dpo_pairs", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	null	2024-04-26T10:06:45+00:00
null	null	<!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/CP4VSgck) ## This repo contains GGUF versions of the xtuner/llava-llama-3-8b-v1_1 model. # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. Frequently Asked Questions - *How does the compression work?* The model is compressed with GGUF. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *What is the model format?* We use GGUF format. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). # Downloading and running the models You can download the individual files from the Files & versions section. Here is a list of the different versions we provide. For more info checkout [this chart](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) and [this guide](https://www.reddit.com/r/LocalLLaMA/comments/1ba55rj/overview_of_gguf_quantization_methods/): \| Quant type \| Description \| \|------------\|--------------------------------------------------------------------------------------------\| \| Q5_K_M \| High quality, recommended. \| \| Q5_K_S \| High quality, recommended. \| \| Q4_K_M \| Good quality, uses about 4.83 bits per weight, recommended. \| \| Q4_K_S \| Slightly lower quality with more space savings, recommended. \| \| IQ4_NL \| Decent quality, slightly smaller than Q4_K_S with similar performance, recommended. \| \| IQ4_XS \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| Q3_K_L \| Lower quality but usable, good for low RAM availability. \| \| Q3_K_M \| Even lower quality. \| \| IQ3_M \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| IQ3_S \| Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. \| \| Q3_K_S \| Low quality, not recommended. \| \| IQ3_XS \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| Q2_K \| Very low quality but surprisingly usable. \| ## 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 - Option A - Downloading in `text-generation-webui`: - Step 1: Under Download Model, you can enter the model repo: PrunaAI/llava-llama-3-8b-v1_1-GGUF-smashed and below it, a specific filename to download, such as: phi-2.IQ3_M.gguf. - Step 2: Then click Download. - Option B - Downloading on the command line (including multiple files at once): - Step 1: We recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` - Step 2: Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download PrunaAI/llava-llama-3-8b-v1_1-GGUF-smashed llava-llama-3-8b-v1_1.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> Alternatively, you can also download multiple files at once with a pattern: ```shell huggingface-cli download PrunaAI/llava-llama-3-8b-v1_1-GGUF-smashed --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` 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 PrunaAI/llava-llama-3-8b-v1_1-GGUF-smashed llava-llama-3-8b-v1_1.IQ3_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 --> ## How to run model in GGUF format? - Option A - Introductory example with `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 35 -m llava-llama-3-8b-v1_1.IQ3_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] {prompt\} [/INST]" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value. 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) - Option B - Running in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20-%20Model%20Tab.md#llamacpp). - Option C - Running 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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # 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 = Llama( model_path="./llava-llama-3-8b-v1_1.IQ3_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<s>[INST] {prompt} [/INST]", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./llava-llama-3-8b-v1_1.IQ3_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` - Option D - Running 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) ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).	{"tags": ["pruna-ai"], "metrics": ["memory_disk", "memory_inference", "inference_latency", "inference_throughput", "inference_CO2_emissions", "inference_energy_consumption"], "thumbnail": "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"}	PrunaAI/llava-llama-3-8b-v1_1-GGUF-smashed	null	[ "gguf", "pruna-ai", "region:us" ]	null	2024-04-26T10:07:12+00:00
null	transformers	## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/chujiezheng/tulu-2-dpo-7b-ExPO <!-- 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/tulu-2-dpo-7b-ExPO-GGUF/resolve/main/tulu-2-dpo-7b-ExPO.Q2_K.gguf) \| Q2_K \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-7b-ExPO-GGUF/resolve/main/tulu-2-dpo-7b-ExPO.IQ3_XS.gguf) \| IQ3_XS \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-7b-ExPO-GGUF/resolve/main/tulu-2-dpo-7b-ExPO.IQ3_S.gguf) \| IQ3_S \| 3.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-7b-ExPO-GGUF/resolve/main/tulu-2-dpo-7b-ExPO.Q3_K_S.gguf) \| Q3_K_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-7b-ExPO-GGUF/resolve/main/tulu-2-dpo-7b-ExPO.IQ3_M.gguf) \| IQ3_M \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-7b-ExPO-GGUF/resolve/main/tulu-2-dpo-7b-ExPO.Q3_K_M.gguf) \| Q3_K_M \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-7b-ExPO-GGUF/resolve/main/tulu-2-dpo-7b-ExPO.Q3_K_L.gguf) \| Q3_K_L \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-7b-ExPO-GGUF/resolve/main/tulu-2-dpo-7b-ExPO.IQ4_XS.gguf) \| IQ4_XS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-7b-ExPO-GGUF/resolve/main/tulu-2-dpo-7b-ExPO.Q4_K_S.gguf) \| Q4_K_S \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-7b-ExPO-GGUF/resolve/main/tulu-2-dpo-7b-ExPO.Q4_K_M.gguf) \| Q4_K_M \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-7b-ExPO-GGUF/resolve/main/tulu-2-dpo-7b-ExPO.Q5_K_S.gguf) \| Q5_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-7b-ExPO-GGUF/resolve/main/tulu-2-dpo-7b-ExPO.Q5_K_M.gguf) \| Q5_K_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-7b-ExPO-GGUF/resolve/main/tulu-2-dpo-7b-ExPO.Q6_K.gguf) \| Q6_K \| 5.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-7b-ExPO-GGUF/resolve/main/tulu-2-dpo-7b-ExPO.Q8_0.gguf) \| Q8_0 \| 7.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-7b-ExPO-GGUF/resolve/main/tulu-2-dpo-7b-ExPO.f16.gguf) \| f16 \| 13.6 \| 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 -->	{"language": ["en"], "license": "other", "library_name": "transformers", "base_model": "chujiezheng/tulu-2-dpo-7b-ExPO", "license_link": "https://allenai.org/impact-license", "license_name": "ai2-impact-license-low-risk", "quantized_by": "mradermacher"}	mradermacher/tulu-2-dpo-7b-ExPO-GGUF	null	[ "transformers", "gguf", "en", "base_model:chujiezheng/tulu-2-dpo-7b-ExPO", "license:other", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:07:32+00:00
text-generation	transformers	# Fadikkop/openbuddy-openllama-3b-v10-bf16-Q4_K_M-GGUF This model was converted to GGUF format from [`OpenBuddy/openbuddy-openllama-3b-v10-bf16`](https://huggingface.co/OpenBuddy/openbuddy-openllama-3b-v10-bf16) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/OpenBuddy/openbuddy-openllama-3b-v10-bf16) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo Fadikkop/openbuddy-openllama-3b-v10-bf16-Q4_K_M-GGUF --model openbuddy-openllama-3b-v10-bf16.Q4_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo Fadikkop/openbuddy-openllama-3b-v10-bf16-Q4_K_M-GGUF --model openbuddy-openllama-3b-v10-bf16.Q4_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m openbuddy-openllama-3b-v10-bf16.Q4_K_M.gguf -n 128 ```	{"language": ["zh", "en", "fr", "de", "ja", "ko", "it", "ru"], "license": "apache-2.0", "library_name": "transformers", "tags": ["llama-cpp", "gguf-my-repo"], "pipeline_tag": "text-generation", "inference": false}	Fadikkop/openbuddy-openllama-3b-v10-bf16-Q4_K_M-GGUF	null	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "zh", "en", "fr", "de", "ja", "ko", "it", "ru", "license:apache-2.0", "region:us" ]	null	2024-04-26T10:07:41+00:00
text-generation	transformers		{"license": "apache-2.0"}	4piken/Llama-3-Gozaru-8B-Instruct	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T10:10:11+00:00
null	allennlp	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"language": ["ru"], "license": "cc", "library_name": "allennlp", "tags": ["not-for-all-audiences"], "datasets": ["PleIAs/YouTube-Commons"], "metrics": ["accuracy"]}	IuraHD/Raya	null	[ "allennlp", "not-for-all-audiences", "ru", "dataset:PleIAs/YouTube-Commons", "arxiv:1910.09700", "license:cc", "region:us" ]	null	2024-04-26T10:10:16+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	tom-brady/6-210	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:10:29+00:00
null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	zandfj/LLaMA2-7B-Chat-dpo-z-042617-moren	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:12:19+00:00
null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	chrispinugu/gpt2-reuters-tokenizer	null	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:13:21+00:00
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # zephyr-7b-gemma-sft-5p-2048 This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the HuggingFaceH4/ultrachat_200k dataset. It achieves the following results on the evaluation set: - Loss: 1.1822 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9062 \| 1.0 \| 651 \| 1.2442 \| \| 0.907 \| 2.0 \| 1303 \| 1.1708 \| \| 0.8209 \| 3.0 \| 1953 \| 1.1822 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.39.0.dev0 - Pytorch 2.1.2 - Datasets 2.14.6 - Tokenizers 0.15.2	{"license": "gemma", "library_name": "peft", "tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrachat_200k"], "base_model": "google/gemma-7b", "model-index": [{"name": "zephyr-7b-gemma-sft-5p-2048", "results": []}]}	Jackie999/zephyr-7b-gemma-sft-5p-2048	null	[ "peft", "tensorboard", "safetensors", "gemma", "alignment-handbook", "trl", "sft", "generated_from_trainer", "dataset:HuggingFaceH4/ultrachat_200k", "base_model:google/gemma-7b", "license:gemma", "region:us" ]	null	2024-04-26T10:13:33+00:00
null	null		{}	ndizeye/l	null	[ "region:us" ]	null	2024-04-26T10:14:44+00:00
null	transformers	## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/OmnicromsBrain/EverythingBagel-DPO-7B <!-- 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/EverythingBagel-DPO-7B-GGUF/resolve/main/EverythingBagel-DPO-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/EverythingBagel-DPO-7B-GGUF/resolve/main/EverythingBagel-DPO-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/EverythingBagel-DPO-7B-GGUF/resolve/main/EverythingBagel-DPO-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/EverythingBagel-DPO-7B-GGUF/resolve/main/EverythingBagel-DPO-7B.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/EverythingBagel-DPO-7B-GGUF/resolve/main/EverythingBagel-DPO-7B.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/EverythingBagel-DPO-7B-GGUF/resolve/main/EverythingBagel-DPO-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/EverythingBagel-DPO-7B-GGUF/resolve/main/EverythingBagel-DPO-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/EverythingBagel-DPO-7B-GGUF/resolve/main/EverythingBagel-DPO-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/EverythingBagel-DPO-7B-GGUF/resolve/main/EverythingBagel-DPO-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/EverythingBagel-DPO-7B-GGUF/resolve/main/EverythingBagel-DPO-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/EverythingBagel-DPO-7B-GGUF/resolve/main/EverythingBagel-DPO-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/EverythingBagel-DPO-7B-GGUF/resolve/main/EverythingBagel-DPO-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/EverythingBagel-DPO-7B-GGUF/resolve/main/EverythingBagel-DPO-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/EverythingBagel-DPO-7B-GGUF/resolve/main/EverythingBagel-DPO-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/EverythingBagel-DPO-7B-GGUF/resolve/main/EverythingBagel-DPO-7B.f16.gguf) \| f16 \| 14.6 \| 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 -->	{"language": ["en"], "library_name": "transformers", "tags": ["merge", "mergekit", "lazymergekit", "jondurbin/bagel-dpo-7b-v0.5", "SanjiWatsuki/Silicon-Maid-7B"], "base_model": "OmnicromsBrain/EverythingBagel-DPO-7B", "quantized_by": "mradermacher"}	mradermacher/EverythingBagel-DPO-7B-GGUF	null	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "jondurbin/bagel-dpo-7b-v0.5", "SanjiWatsuki/Silicon-Maid-7B", "en", "base_model:OmnicromsBrain/EverythingBagel-DPO-7B", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:15:01+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	mehdisebai/Enlighten_Instruct-text-to-rule_merged	null	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T10:15:02+00:00
null	null		{}	0qwpifs/SenyashimaV35	null	[ "region:us" ]	null	2024-04-26T10:15:15+00:00
null	null		{}	Mikhail1/emotionet	null	[ "region:us" ]	null	2024-04-26T10:16:12+00:00
null	null		{}	armyshope/j-hope	null	[ "region:us" ]	null	2024-04-26T10:16:24+00:00
null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # donut_synDB_w This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0997 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 5 - total_train_batch_size: 20 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.4955 \| 0.86 \| 42 \| 0.2360 \| \| 0.2824 \| 1.29 \| 63 \| 0.1119 \| \| 0.1889 \| 1.71 \| 84 \| 0.0979 \| \| 0.149 \| 2.14 \| 105 \| 0.1038 \| \| 0.1159 \| 2.57 \| 126 \| 0.0932 \| \| 0.1066 \| 3.0 \| 147 \| 0.0907 \| \| 0.0717 \| 3.43 \| 168 \| 0.1096 \| \| 0.0787 \| 3.86 \| 189 \| 0.0970 \| \| 0.0735 \| 4.29 \| 210 \| 0.0957 \| \| 0.0609 \| 4.71 \| 231 \| 0.1020 \| \| 0.0609 \| 5.14 \| 252 \| 0.0946 \| \| 0.0519 \| 5.57 \| 273 \| 0.0980 \| \| 0.0453 \| 6.0 \| 294 \| 0.0997 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "naver-clova-ix/donut-base", "model-index": [{"name": "donut_synDB_w", "results": []}]}	Donut01/donut_synDB_w	null	[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "generated_from_trainer", "dataset:imagefolder", "base_model:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:17:00+00:00
null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	adldl/Meta-Llama-3-8B-Instruct_V2	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:17:00+00:00
null	null		{}	LinxuanPastel/MartinaCotasV1	null	[ "region:us" ]	null	2024-04-26T10:19:28+00:00
text-classification	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # robust_llm_pythia-160m_mz-131f_PasswordMatch This model is a fine-tuned version of [EleutherAI/pythia-160m](https://huggingface.co/EleutherAI/pythia-160m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-160m", "model-index": [{"name": "robust_llm_pythia-160m_mz-131f_PasswordMatch", "results": []}]}	AlignmentResearch/robust_llm_pythia-160m_mz-131f_PasswordMatch	null	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-160m", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T10:19:54+00:00
null	null		{}	OrientalAlex/testmodel	null	[ "region:us" ]	null	2024-04-26T10:20:36+00:00
null	transformers	# Uploaded model - Developed by: rathodj08 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama 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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}	rathodj08/lora_model	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:20:41+00:00
null	null		{}	berkekapukaya/db-dog-model	null	[ "region:us" ]	null	2024-04-26T10:22:18+00:00
object-detection	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	ankit-katewa/DETR-model-1659-epoch50.pth	null	[ "transformers", "safetensors", "detr", "object-detection", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:22:39+00:00
text-classification	transformers		{}	AlignmentResearch/robust_llm_pythia-14m_mz-131f_PasswordMatch	null	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-14m", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T10:23:19+00:00
null	null		{"license": "mit"}	Rohit1412/finrtunedver3	null	[ "safetensors", "license:mit", "region:us" ]	null	2024-04-26T10:23:29+00:00
object-detection	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	ankit-katewa/DETR-model-1659-epoch50	null	[ "transformers", "safetensors", "detr", "object-detection", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:23:50+00:00
null	null		{}	LinxuanPastel/JanDefinitivo2	null	[ "region:us" ]	null	2024-04-26T10:24:11+00:00
null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	sachin/debug-vision-model	null	[ "transformers", "safetensors", "vision", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:24:12+00:00
null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	sachin/debug-text-model	null	[ "transformers", "safetensors", "text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:24:30+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	vishruthnath/deepseek_ft_exec	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T10:27:34+00:00
null	transformers	## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/chujiezheng/Starling-LM-7B-beta-ExPO <!-- 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/Starling-LM-7B-beta-ExPO-GGUF/resolve/main/Starling-LM-7B-beta-ExPO.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Starling-LM-7B-beta-ExPO-GGUF/resolve/main/Starling-LM-7B-beta-ExPO.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Starling-LM-7B-beta-ExPO-GGUF/resolve/main/Starling-LM-7B-beta-ExPO.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Starling-LM-7B-beta-ExPO-GGUF/resolve/main/Starling-LM-7B-beta-ExPO.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Starling-LM-7B-beta-ExPO-GGUF/resolve/main/Starling-LM-7B-beta-ExPO.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Starling-LM-7B-beta-ExPO-GGUF/resolve/main/Starling-LM-7B-beta-ExPO.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Starling-LM-7B-beta-ExPO-GGUF/resolve/main/Starling-LM-7B-beta-ExPO.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Starling-LM-7B-beta-ExPO-GGUF/resolve/main/Starling-LM-7B-beta-ExPO.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Starling-LM-7B-beta-ExPO-GGUF/resolve/main/Starling-LM-7B-beta-ExPO.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Starling-LM-7B-beta-ExPO-GGUF/resolve/main/Starling-LM-7B-beta-ExPO.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Starling-LM-7B-beta-ExPO-GGUF/resolve/main/Starling-LM-7B-beta-ExPO.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Starling-LM-7B-beta-ExPO-GGUF/resolve/main/Starling-LM-7B-beta-ExPO.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Starling-LM-7B-beta-ExPO-GGUF/resolve/main/Starling-LM-7B-beta-ExPO.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Starling-LM-7B-beta-ExPO-GGUF/resolve/main/Starling-LM-7B-beta-ExPO.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Starling-LM-7B-beta-ExPO-GGUF/resolve/main/Starling-LM-7B-beta-ExPO.f16.gguf) \| f16 \| 14.6 \| 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 -->	{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "base_model": "chujiezheng/Starling-LM-7B-beta-ExPO", "quantized_by": "mradermacher"}	mradermacher/Starling-LM-7B-beta-ExPO-GGUF	null	[ "transformers", "gguf", "en", "base_model:chujiezheng/Starling-LM-7B-beta-ExPO", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:27:54+00:00
null	transformers	## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/chujiezheng/zephyr-7b-dpo-full-ExPO <!-- 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/zephyr-7b-dpo-full-ExPO-GGUF/resolve/main/zephyr-7b-dpo-full-ExPO.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-dpo-full-ExPO-GGUF/resolve/main/zephyr-7b-dpo-full-ExPO.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-dpo-full-ExPO-GGUF/resolve/main/zephyr-7b-dpo-full-ExPO.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-dpo-full-ExPO-GGUF/resolve/main/zephyr-7b-dpo-full-ExPO.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-dpo-full-ExPO-GGUF/resolve/main/zephyr-7b-dpo-full-ExPO.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-dpo-full-ExPO-GGUF/resolve/main/zephyr-7b-dpo-full-ExPO.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-dpo-full-ExPO-GGUF/resolve/main/zephyr-7b-dpo-full-ExPO.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-dpo-full-ExPO-GGUF/resolve/main/zephyr-7b-dpo-full-ExPO.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-dpo-full-ExPO-GGUF/resolve/main/zephyr-7b-dpo-full-ExPO.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-dpo-full-ExPO-GGUF/resolve/main/zephyr-7b-dpo-full-ExPO.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-dpo-full-ExPO-GGUF/resolve/main/zephyr-7b-dpo-full-ExPO.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-dpo-full-ExPO-GGUF/resolve/main/zephyr-7b-dpo-full-ExPO.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-dpo-full-ExPO-GGUF/resolve/main/zephyr-7b-dpo-full-ExPO.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-dpo-full-ExPO-GGUF/resolve/main/zephyr-7b-dpo-full-ExPO.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/zephyr-7b-dpo-full-ExPO-GGUF/resolve/main/zephyr-7b-dpo-full-ExPO.f16.gguf) \| f16 \| 14.6 \| 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 -->	{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "base_model": "chujiezheng/zephyr-7b-dpo-full-ExPO", "quantized_by": "mradermacher"}	mradermacher/zephyr-7b-dpo-full-ExPO-GGUF	null	[ "transformers", "gguf", "en", "base_model:chujiezheng/zephyr-7b-dpo-full-ExPO", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:28:47+00:00
null	null		{}	Huijigo/llama3_20w_qlora	null	[ "tensorboard", "safetensors", "region:us" ]	null	2024-04-26T10:29:05+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	nem012/gemma2b-r16MHC	null	[ "transformers", "tensorboard", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T10:29:22+00:00
null	null		{}	Gabi00/whisper-small-hi	null	[ "region:us" ]	null	2024-04-26T10:29:39+00:00
null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	zandfj/LLaMA2-7B-Chat-dpo-042618-mix-1epoch	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:30:56+00:00
null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	ytcheng/Llama-2-7b-lora	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:31:25+00:00
null	null		{"license": "mit"}	Bluebomber182/Arnold-Shortman-Nicktoons-Nick-Tunes-Version-StyleTTS2-Model	null	[ "license:mit", "region:us" ]	null	2024-04-26T10:32:24+00:00
null	null		{}	Anna15/sn25-andrey-1	null	[ "region:us" ]	null	2024-04-26T10:32:50+00:00
null	null		{}	iow9/me2.0	null	[ "region:us" ]	null	2024-04-26T10:33:19+00:00
null	transformers	# Uploaded model - Developed by: rathodj08 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama 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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}	rathodj08/llama3_finetune_model	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:33:32+00:00
text-classification	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # robust_llm_pythia-1b_mz-130_IMDB_n-its-10-seed-0 This model is a fine-tuned version of [EleutherAI/pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-1b", "model-index": [{"name": "robust_llm_pythia-1b_mz-130_IMDB_n-its-10-seed-0", "results": []}]}	AlignmentResearch/robust_llm_pythia-1b_mz-130_IMDB_n-its-10-seed-0	null	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-1b", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T10:33:40+00:00
image-classification	transformers		{"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["medical"], "metrics": ["accuracy"], "pipeline_tag": "image-classification"}	SJChaudhuri/Efficient-MaxViT	null	[ "transformers", "medical", "image-classification", "en", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:33:49+00:00
text-generation	transformers	# Llama-3-Mistral-v0.2-Instruct-passthrough Llama-3-Mistral-v0.2-Instruct-passthrough is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) * [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) ## 🧩 Configuration ```yaml slices: - sources: - model: meta-llama/Meta-Llama-3-8B-Instruct layer_range: [0, 16] - sources: - model: mistralai/Mistral-7B-Instruct-v0.2 layer_range: [16, 32] merge_method: passthrough dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "varox34/Llama-3-Mistral-v0.2-Instruct-passthrough" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```	{"tags": ["merge", "mergekit", "lazymergekit", "meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"], "base_model": ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"]}	varox34/Llama-3-Mistral-v0.2-Instruct-passthrough	null	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2", "conversational", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T10:33:50+00:00
null	null		{}	SonicInGug/Mickey-Mouse-Wayne-Allwine	null	[ "region:us" ]	null	2024-04-26T10:36:07+00:00
null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	tutuhu/style5	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:37:34+00:00
fill-mask	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-bible This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2468 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.6142 \| 1.0 \| 157 \| 2.3380 \| \| 2.409 \| 2.0 \| 314 \| 2.2316 \| \| 2.3373 \| 3.0 \| 471 \| 2.2146 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-bible", "results": []}]}	Pragash-Mohanarajah/distilbert-base-uncased-finetuned-bible	null	[ "transformers", "tensorboard", "safetensors", "distilbert", "fill-mask", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:37:42+00:00
text-to-image	diffusers		{}	mrtuandao/pokemon2604	null	[ "diffusers", "tensorboard", "safetensors", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	null	2024-04-26T10:39:45+00:00
text-generation	transformers		{"license": "apache-2.0"}	AlekseyScorpi/saiga_mistral_7b_vacancies_merged	null	[ "transformers", "safetensors", "mistral", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T10:40:18+00:00
null	transformers	## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/mlx-community/Llama-3-8B-Instruct-262k-unquantized <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-unquantized-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/Llama-3-8B-Instruct-262k-unquantized-GGUF/resolve/main/Llama-3-8B-Instruct-262k-unquantized.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-unquantized-GGUF/resolve/main/Llama-3-8B-Instruct-262k-unquantized.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-unquantized-GGUF/resolve/main/Llama-3-8B-Instruct-262k-unquantized.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-unquantized-GGUF/resolve/main/Llama-3-8B-Instruct-262k-unquantized.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-unquantized-GGUF/resolve/main/Llama-3-8B-Instruct-262k-unquantized.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-unquantized-GGUF/resolve/main/Llama-3-8B-Instruct-262k-unquantized.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-unquantized-GGUF/resolve/main/Llama-3-8B-Instruct-262k-unquantized.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-unquantized-GGUF/resolve/main/Llama-3-8B-Instruct-262k-unquantized.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-unquantized-GGUF/resolve/main/Llama-3-8B-Instruct-262k-unquantized.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-unquantized-GGUF/resolve/main/Llama-3-8B-Instruct-262k-unquantized.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-unquantized-GGUF/resolve/main/Llama-3-8B-Instruct-262k-unquantized.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-unquantized-GGUF/resolve/main/Llama-3-8B-Instruct-262k-unquantized.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-unquantized-GGUF/resolve/main/Llama-3-8B-Instruct-262k-unquantized.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-unquantized-GGUF/resolve/main/Llama-3-8B-Instruct-262k-unquantized.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-262k-unquantized-GGUF/resolve/main/Llama-3-8B-Instruct-262k-unquantized.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 -->	{"language": ["en"], "library_name": "transformers", "tags": ["meta", "llama-3", "mlx"], "base_model": "mlx-community/Llama-3-8B-Instruct-262k-unquantized", "quantized_by": "mradermacher"}	mradermacher/Llama-3-8B-Instruct-262k-unquantized-GGUF	null	[ "transformers", "gguf", "meta", "llama-3", "mlx", "en", "base_model:mlx-community/Llama-3-8B-Instruct-262k-unquantized", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:40:37+00:00
null	null		{"license": "llama3"}	EnverLee/llama-3-open-ko-8b-instruct-preview.Q8_0-gguf	null	[ "license:llama3", "region:us" ]	null	2024-04-26T10:40:44+00:00
image-to-text	null	<div align="center"> <img src="https://github.com/InternLM/lmdeploy/assets/36994684/0cf8d00f-e86b-40ba-9b54-dc8f1bc6c8d8" width="600"/> [![Generic badge](https://img.shields.io/badge/GitHub-%20XTuner-black.svg)](https://github.com/InternLM/xtuner) </div> ## Model llava-llama-3-8b-v1_1 is a LLaVA model fine-tuned from [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) and [CLIP-ViT-Large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) with [ShareGPT4V-PT](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V) and [InternVL-SFT](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat#prepare-training-datasets) by [XTuner](https://github.com/InternLM/xtuner). Note: This model is in GGUF format. Resources: - GitHub: [xtuner](https://github.com/InternLM/xtuner) - HuggingFace LLaVA format model: [xtuner/llava-llama-3-8b-v1_1-transformers](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers) - Official LLaVA format model: [xtuner/llava-llama-3-8b-v1_1-hf](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-hf) - XTuner LLaVA format model: [xtuner/llava-llama-3-8b-v1_1](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1) ## Details \| Model \| Visual Encoder \| Projector \| Resolution \| Pretraining Strategy \| Fine-tuning Strategy \| Pretrain Dataset \| Fine-tune Dataset \| \| :-------------------- \| ------------------: \| --------: \| ---------: \| ---------------------: \| ------------------------: \| ------------------------: \| -----------------------: \| \| LLaVA-v1.5-7B \| CLIP-L \| MLP \| 336 \| Frozen LLM, Frozen ViT \| Full LLM, Frozen ViT \| LLaVA-PT (558K) \| LLaVA-Mix (665K) \| \| LLaVA-Llama-3-8B \| CLIP-L \| MLP \| 336 \| Frozen LLM, Frozen ViT \| Full LLM, LoRA ViT \| LLaVA-PT (558K) \| LLaVA-Mix (665K) \| \| LLaVA-Llama-3-8B-v1.1 \| CLIP-L \| MLP \| 336 \| Frozen LLM, Frozen ViT \| Full LLM, LoRA ViT \| ShareGPT4V-PT (1246K) \| InternVL-SFT (1268K) \| ## Results <div align="center"> <img src="https://github.com/InternLM/xtuner/assets/36994684/a157638c-3500-44ed-bfab-d8d8249f91bb" alt="Image" width=500" /> </div> \| Model \| MMBench Test (EN) \| MMBench Test (CN) \| CCBench Dev \| MMMU Val \| SEED-IMG \| AI2D Test \| ScienceQA Test \| HallusionBench aAcc \| POPE \| GQA \| TextVQA \| MME \| MMStar \| \| :-------------------- \| :---------------: \| :---------------: \| :---------: \| :-------: \| :------: \| :-------: \| :------------: \| :-----------------: \| :--: \| :--: \| :-----: \| :------: \| :----: \| \| LLaVA-v1.5-7B \| 66.5 \| 59.0 \| 27.5 \| 35.3 \| 60.5 \| 54.8 \| 70.4 \| 44.9 \| 85.9 \| 62.0 \| 58.2 \| 1511/348 \| 30.3 \| \| LLaVA-Llama-3-8B \| 68.9 \| 61.6 \| 30.4 \| 36.8 \| 69.8 \| 60.9 \| 73.3 \| 47.3 \| 87.2 \| 63.5 \| 58.0 \| 1506/295 \| 38.2 \| \| LLaVA-Llama-3-8B-v1.1 \| 72.3 \| 66.4 \| 31.6 \| 36.8 \| 70.1 \| 70.0 \| 72.9 \| 47.7 \| 86.4 \| 62.6 \| 59.0 \| 1469/349 \| 45.1 \| ## Quickstart ### Download models ```bash # mmproj wget https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf/resolve/main/llava-llama-3-8b-v1_1-mmproj-f16.gguf # fp16 llm wget https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf/resolve/main/llava-llama-3-8b-v1_1-f16.gguf # int4 llm wget https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf/resolve/main/llava-llama-3-8b-v1_1-int4.gguf # (optional) ollama fp16 modelfile wget https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf/resolve/main/OLLAMA_MODELFILE_F16 # (optional) ollama int4 modelfile wget https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf/resolve/main/OLLAMA_MODELFILE_INT4 ``` ### Chat by `ollama` ```bash # fp16 ollama create llava-llama3-f16 -f ./OLLAMA_MODELFILE_F16 ollama run llava-llama3-f16 "xx.png Describe this image" # int4 ollama create llava-llama3-int4 -f ./OLLAMA_MODELFILE_INT4 ollama run llava-llama3-int4 "xx.png Describe this image" ``` ### Chat by `llama.cpp` 1. Build [llama.cpp](https://github.com/ggerganov/llama.cpp) ([docs](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage)) . 2. Build `./llava-cli` ([docs](https://github.com/ggerganov/llama.cpp/tree/master/examples/llava#usage)). Note: llava-llama-3-8b-v1_1 uses the Llama-3-instruct chat template. ```bash # fp16 ./llava-cli -m ./llava-llama-3-8b-v1_1-f16.gguf --mmproj ./llava-llama-3-8b-v1_1-mmproj-f16.gguf --image YOUR_IMAGE.jpg -c 4096 -e -p "<\|start_header_id\|>user<\|end_header_id\|>\n\n<image>\nDescribe this image<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>\n\n" # int4 ./llava-cli -m ./llava-llama-3-8b-v1_1-int4.gguf --mmproj ./llava-llama-3-8b-v1_1-mmproj-f16.gguf --image YOUR_IMAGE.jpg -c 4096 -e -p "<\|start_header_id\|>user<\|end_header_id\|>\n\n<image>\nDescribe this image<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>\n\n" ``` ### Reproduce Please refer to [docs](https://github.com/InternLM/xtuner/tree/main/xtuner/configs/llava/llama3_8b_instruct_clip_vit_large_p14_336#readme). ## Citation ```bibtex @misc{2023xtuner, title={XTuner: A Toolkit for Efficiently Fine-tuning LLM}, author={XTuner Contributors}, howpublished = {\url{https://github.com/InternLM/xtuner}}, year={2023} } ```	{"datasets": ["Lin-Chen/ShareGPT4V"], "pipeline_tag": "image-to-text"}	xtuner/llava-llama-3-8b-v1_1-gguf	null	[ "gguf", "image-to-text", "dataset:Lin-Chen/ShareGPT4V", "region:us" ]	null	2024-04-26T10:41:02+00:00
text-generation	null	## Llamacpp imatrix Quantizations of Llama-3-8B-LexiFun-Uncensored-V1 Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2717">b2717</a> for quantization. Original model: https://huggingface.co/Orenguteng/Llama-3-8B-LexiFun-Uncensored-V1 All quants made using imatrix option with dataset provided by Kalomaze [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384) ## Prompt format ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> {system_prompt}<\|end_of_text\|><\|start_header_id\|>user<\|end_header_id\|> {prompt}<\|end_of_text\|><\|start_header_id\|>assistant<\|end_header_id\|> ``` ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [Llama-3-8B-LexiFun-Uncensored-V1-Q8_0.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-Q8_0.gguf) \| Q8_0 \| 8.54GB \| Extremely high quality, generally unneeded but max available quant. \| \| [Llama-3-8B-LexiFun-Uncensored-V1-Q6_K.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-Q6_K.gguf) \| Q6_K \| 6.59GB \| Very high quality, near perfect, recommended. \| \| [Llama-3-8B-LexiFun-Uncensored-V1-Q5_K_M.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-Q5_K_M.gguf) \| Q5_K_M \| 5.73GB \| High quality, recommended. \| \| [Llama-3-8B-LexiFun-Uncensored-V1-Q5_K_S.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-Q5_K_S.gguf) \| Q5_K_S \| 5.59GB \| High quality, recommended. \| \| [Llama-3-8B-LexiFun-Uncensored-V1-Q4_K_M.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-Q4_K_M.gguf) \| Q4_K_M \| 4.92GB \| Good quality, uses about 4.83 bits per weight, recommended. \| \| [Llama-3-8B-LexiFun-Uncensored-V1-Q4_K_S.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-Q4_K_S.gguf) \| Q4_K_S \| 4.69GB \| Slightly lower quality with more space savings, recommended. \| \| [Llama-3-8B-LexiFun-Uncensored-V1-IQ4_NL.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-IQ4_NL.gguf) \| IQ4_NL \| 4.67GB \| Decent quality, slightly smaller than Q4_K_S with similar performance recommended. \| \| [Llama-3-8B-LexiFun-Uncensored-V1-IQ4_XS.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-IQ4_XS.gguf) \| IQ4_XS \| 4.44GB \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [Llama-3-8B-LexiFun-Uncensored-V1-Q3_K_L.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-Q3_K_L.gguf) \| Q3_K_L \| 4.32GB \| Lower quality but usable, good for low RAM availability. \| \| [Llama-3-8B-LexiFun-Uncensored-V1-Q3_K_M.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-Q3_K_M.gguf) \| Q3_K_M \| 4.01GB \| Even lower quality. \| \| [Llama-3-8B-LexiFun-Uncensored-V1-IQ3_M.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-IQ3_M.gguf) \| IQ3_M \| 3.78GB \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| [Llama-3-8B-LexiFun-Uncensored-V1-IQ3_S.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-IQ3_S.gguf) \| IQ3_S \| 3.68GB \| Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. \| \| [Llama-3-8B-LexiFun-Uncensored-V1-Q3_K_S.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-Q3_K_S.gguf) \| Q3_K_S \| 3.66GB \| Low quality, not recommended. \| \| [Llama-3-8B-LexiFun-Uncensored-V1-IQ3_XS.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-IQ3_XS.gguf) \| IQ3_XS \| 3.51GB \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| [Llama-3-8B-LexiFun-Uncensored-V1-IQ3_XXS.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-IQ3_XXS.gguf) \| IQ3_XXS \| 3.27GB \| Lower quality, new method with decent performance, comparable to Q3 quants. \| \| [Llama-3-8B-LexiFun-Uncensored-V1-Q2_K.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-Q2_K.gguf) \| Q2_K \| 3.17GB \| Very low quality but surprisingly usable. \| \| [Llama-3-8B-LexiFun-Uncensored-V1-IQ2_M.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-IQ2_M.gguf) \| IQ2_M \| 2.94GB \| Very low quality, uses SOTA techniques to also be surprisingly usable. \| \| [Llama-3-8B-LexiFun-Uncensored-V1-IQ2_S.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-IQ2_S.gguf) \| IQ2_S \| 2.75GB \| Very low quality, uses SOTA techniques to be usable. \| \| [Llama-3-8B-LexiFun-Uncensored-V1-IQ2_XS.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-IQ2_XS.gguf) \| IQ2_XS \| 2.60GB \| Very low quality, uses SOTA techniques to be usable. \| \| [Llama-3-8B-LexiFun-Uncensored-V1-IQ2_XXS.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-IQ2_XXS.gguf) \| IQ2_XXS \| 2.39GB \| Lower quality, uses SOTA techniques to be usable. \| \| [Llama-3-8B-LexiFun-Uncensored-V1-IQ1_M.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-IQ1_M.gguf) \| IQ1_M \| 2.16GB \| Extremely low quality, not recommended. \| \| [Llama-3-8B-LexiFun-Uncensored-V1-IQ1_S.gguf](https://huggingface.co/bartowski/Llama-3-8B-LexiFun-Uncensored-V1-GGUF/blob/main/Llama-3-8B-LexiFun-Uncensored-V1-IQ1_S.gguf) \| IQ1_S \| 2.01GB \| Extremely low quality, not recommended. \| ## 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	{"language": ["en"], "license": "other", "tags": ["llama3", "comedy", "comedian", "fun", "funny", "llama38b", "laugh", "sarcasm", "roleplay"], "license_name": "llama3", "license_link": "https://llama.meta.com/llama3/license/", "quantized_by": "bartowski", "pipeline_tag": "text-generation"}	bartowski/Llama-3-8B-LexiFun-Uncensored-V1-old-GGUF	null	[ "gguf", "llama3", "comedy", "comedian", "fun", "funny", "llama38b", "laugh", "sarcasm", "roleplay", "text-generation", "en", "license:other", "region:us" ]	null	2024-04-26T10:41:17+00:00
null	null		{}	Techminator/Sokoban_Tech_Agent	null	[ "region:us" ]	null	2024-04-26T10:41:47+00:00
null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	Aarushhh/sstgpt-try1	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:42:08+00:00
null	null		{"license": "mit"}	llm-slayer/CroissantLLMChat-v0.1-q4f16_1-MLC	null	[ "license:mit", "region:us" ]	null	2024-04-26T10:42:14+00:00
feature-extraction	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned_bge_ver25 This model is a fine-tuned version of [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "BAAI/bge-m3", "model-index": [{"name": "finetuned_bge_ver25", "results": []}]}	comet24082002/finetuned_bge_ver25	null	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "feature-extraction", "generated_from_trainer", "base_model:BAAI/bge-m3", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:42:30+00:00
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SST-GPT This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 10 - mixed_precision_training: Native AMP ### Framework versions - PEFT 0.10.0 - Transformers 4.39.3 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	{"license": "mit", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "microsoft/Phi-3-mini-128k-instruct", "model-index": [{"name": "SST-GPT", "results": []}]}	Aarushhh/SST-GPT	null	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:microsoft/Phi-3-mini-128k-instruct", "license:mit", "region:us" ]	null	2024-04-26T10:42:35+00:00
null	null		{"license": "mit"}	llm-slayer/CroissantLLMChat-v0.1-q3f16_1-MLC	null	[ "license:mit", "region:us" ]	null	2024-04-26T10:43:14+00:00
fill-mask	transformers		{"license": "mit"}	Pragash-Mohanarajah/distilbert-base-uncased-finetuned-bible-accelerate	null	[ "transformers", "safetensors", "distilbert", "fill-mask", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:43:18+00:00
null	null		{"license": "mit"}	llm-slayer/CroissantLLMChat-v0.1-q0f16-MLC	null	[ "license:mit", "region:us" ]	null	2024-04-26T10:43:48+00:00
null	null		{"license": "mit"}	llm-slayer/CroissantLLMChat-v0.1-q0f32-MLC	null	[ "license:mit", "region:us" ]	null	2024-04-26T10:44:07+00:00
reinforcement-learning	null	# Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction	{"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "500.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}	thekoc11/Reinforce-v1	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	null	2024-04-26T10:44:48+00:00
null	null		{}	LinxuanPastel/Martiv2	null	[ "region:us" ]	null	2024-04-26T10:45:08+00:00
null	null		{}	rowanwinters/model1painters	null	[ "region:us" ]	null	2024-04-26T10:45:30+00:00
text-generation	transformers	# Uploaded model - Developed by: adrien-alloreview - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama 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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "unsloth/llama-3-8b-Instruct-bnb-4bit"}	adrien-alloreview/llama-3-STAN-alpha	null	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:46:58+00:00
text-classification	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # robust_llm_pythia-31m_mz-131f_PasswordMatch This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2	{"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-31m", "model-index": [{"name": "robust_llm_pythia-31m_mz-131f_PasswordMatch", "results": []}]}	AlignmentResearch/robust_llm_pythia-31m_mz-131f_PasswordMatch	null	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-31m", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T10:47:01+00:00
null	diffusers	<div align="center"> <h1> <a>Paint3D: Paint Anything 3D with Lighting-Less Texture Diffusion Models</a></h1> <p align="center"> <a href=https://paint3d.github.io/>Project Page</a> • <a href=https://arxiv.org/abs/2312.13913>Arxiv</a> • <a href=https://github.com/OpenTexture/Paint3D>GitHub</a> </p> </div> <div align="center"> <video width="1280" height="720" controls> <source src="https://github.com/OpenTexture/Paint3D/assets/18525299/9aef7eeb-a783-482c-87d5-78055da3bfc0" type="video/mp4"> </video> </div> ## Introduction Paint3D is a novel coarse-to-fine generative framework that is capable of producing high-resolution, lighting-less, and diverse 2K UV texture maps for untextured 3D meshes conditioned on text or image inputs. <details open="open"> <summary><b>Technical details</b></summary> We present Paint3D, a novel coarse-to-fine generative framework that is capable of producing high-resolution, lighting-less, and diverse 2K UV texture maps for untextured 3D meshes conditioned on text or image inputs. The key challenge addressed is generating high-quality textures without embedded illumination information, which allows the textures to be re-lighted or re-edited within modern graphics pipelines. To achieve this, our method first leverages a pre-trained depth-aware 2D diffusion model to generate view-conditional images and perform multi-view texture fusion, producing an initial coarse texture map. However, as 2D models cannot fully represent 3D shapes and disable lighting effects, the coarse texture map exhibits incomplete areas and illumination artifacts. To resolve this, we train separate UV Inpainting and UVHD diffusion models specialized for the shape-aware refinement of incomplete areas and the removal of illumination artifacts. Through this coarse-to-fine process, Paint3D can produce high-quality 2K UV textures that maintain semantic consistency while being lighting-less, significantly advancing the state-of-the-art in texturing 3D objects. <div align="center"> <img width="1194" alt="pipeline" src="./assets/pipeline.jpg"> </div> </details> ## 📖 Citation ```bib @misc{zeng2023paint3d, title={Paint3D: Paint Anything 3D with Lighting-Less Texture Diffusion Models}, author={Xianfang Zeng and Xin Chen and Zhongqi Qi and Wen Liu and Zibo Zhao and Zhibin Wang and BIN FU and Yong Liu and Gang Yu}, year={2023}, eprint={2312.13913}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```	{"license": "apache-2.0", "tags": ["texture-generation"]}	GeorgeQi/Paint3d_UVPos_Control	null	[ "diffusers", "texture-generation", "arxiv:2312.13913", "license:apache-2.0", "region:us" ]	null	2024-04-26T10:47:17+00:00
text-classification	transformers	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # basakdemirok/bert-base-turkish-cased-off_detect_v0 This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0401 - Validation Loss: 0.4939 - Train F1: 0.6946 - Epoch: 3 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7936, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train F1 \| Epoch \| \|:----------:\|:---------------:\|:--------:\|:-----:\| \| 0.3059 \| 0.2634 \| 0.6928 \| 0 \| \| 0.1913 \| 0.3052 \| 0.7012 \| 1 \| \| 0.0943 \| 0.4022 \| 0.6942 \| 2 \| \| 0.0401 \| 0.4939 \| 0.6946 \| 3 \| ### Framework versions - Transformers 4.40.1 - TensorFlow 2.13.1 - Datasets 2.4.0 - Tokenizers 0.19.1	{"license": "mit", "tags": ["generated_from_keras_callback"], "base_model": "dbmdz/bert-base-turkish-cased", "model-index": [{"name": "basakdemirok/bert-base-turkish-cased-off_detect_v0", "results": []}]}	basakdemirok/bert-base-turkish-cased-off_detect_v0	null	[ "transformers", "tf", "tensorboard", "bert", "text-classification", "generated_from_keras_callback", "base_model:dbmdz/bert-base-turkish-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:48:03+00:00
null	transformers	# Uploaded model - Developed by: baconnier - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama 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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-Instruct-bnb-4bit"}	baconnier/finance_orpo_llama3_Instruct_8B_r64_51K_Adapters	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:48:39+00:00
sentence-similarity	sentence-transformers	본 모델은 multi-task loss (MultipleNegativeLoss -> AnglELoss) 로, KlueNLI 및 KlueSTS 데이터로 학습되었습니다. 학습 코드는 다음 [Github hyperlink](https://github.com/comchobo/SFT_sent_emb?tab=readme-ov-file)에서 보실 수 있습니다. ## Usage (Huggingface inference API) ```python import requests API_URL = "https://api-inference.huggingface.co/models/sorryhyun/sentence-embedding-klue-large" headers = {"Authorization": "your_HF_token"} def query(payload): response = requests.post(API_URL, headers=headers, json=payload) return response.json() output = query({ "inputs": { "source_sentence": "좋아요, 추천, 알림설정까지", "sentences": [ "좋아요 눌러주세요!!", "좋아요, 추천 등 유투버들이 좋아해요", "알림설정을 눌러주시면 감사드리겠습니다." ] }, }) if __name__ == '__main__': print(output) ``` ## Usage (HuggingFace Transformers) ```python from transformers import AutoTokenizer, AutoModel import torch device = torch.device('cuda') # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}').to(device) tokenized_data = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') dataloader = DataLoader(tokenized_data, batch_size=batch_size, pin_memory=True) all_outputs = torch.zeros((len(tokenized_data), self.hidden_size)).to(device) start_idx = 0 # I used mean-pool method for sentence representation with torch.no_grad(): for inputs in tqdm(dataloader): inputs = {k: v.to(device) for k, v in inputs.items()} representations, _ = self.model(*inputs, return_dict=False) attention_mask = inputs["attention_mask"] input_mask_expanded = (attention_mask.unsqueeze(-1).expand(representations.size()).to(representations.dtype)) summed = torch.sum(representations input_mask_expanded, 1) sum_mask = input_mask_expanded.sum(1) sum_mask = torch.clamp(sum_mask, min=1e-9) end_idx = start_idx + representations.shape[0] all_outputs[start_idx:end_idx] = (summed / sum_mask) start_idx = end_idx ``` ## Evaluation Results \| Organization \| Backbone Model \| KlueSTS average \| KorSTS average \| \| -------- \| ------- \| ------- \| ------- \| \| team-lucid \| DeBERTa-base \| 54.15 \| 29.72 \| \| monologg \| Electra-base \| 66.97 \| 40.98 \| \| LMkor \| Electra-base \| 70.98 \| 43.09 \| \| deliciouscat \| DeBERTa-base \| - \| 67.65 \| \| BM-K \| Roberta-base \| 82.93 \| 85.77 \| \| Klue \| Roberta-large \| 86.71 \| 71.70 \| \| Klue (Hyperparameter searched) \| Roberta-large \| 86.21 \| 75.54 \| 기존 한국어 문장 임베딩 모델은 mnli, snli 등 영어 데이터셋을 기계번역하여 학습된 점을 참고삼아 Klue 데이터셋으로 대신 학습해 보았습니다. 그 결과, Klue-Roberta-large 모델 기반으로 학습했을 경우 KlueSTS 및 KorSTS 테스트셋에 모두에 대해 준수한 성능을 보여, 좀 더 elaborate한 representation을 형성하는 것으로 사료했습니다. 다만 평가 수치는 하이퍼파라미터 세팅, 시드 넘버 등으로 크게 달라질 수 있으므로 참고하시길 바랍니다. ## Training NegativeRank loss -> simcse loss 로 학습했습니다.	{"language": ["ko"], "license": "cc-by-sa-4.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["klue"], "pipeline_tag": "sentence-similarity"}	sorryhyun/sentence-embedding-klue-large	null	[ "sentence-transformers", "safetensors", "roberta", "feature-extraction", "sentence-similarity", "transformers", "ko", "dataset:klue", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:48:53+00:00
null	diffusers		{}	Priya-H/Tune-A-Video_Output	null	[ "diffusers", "region:us" ]	null	2024-04-26T10:49:32+00:00
text-classification	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: Pongsasit Thongpramoon - Model type: Cross Encoder - Language(s) (NLP): Thai - ## How to Get Started with the Model Use the code below to get started with the model. ```python from sentence_transformers.cross_encoder import CrossEncoder model = CrossEncoder("Pongsasit/mod-th-cross-encoder") scores = model.predict([["อาหารตามสั่ง", "หมู เห็ด เป็ด ไก่"], ["อาหารตามสั่ง", "รถ เรือ เครื่องบิน จักรยาน"]]) ```	{"library_name": "transformers", "tags": []}	Pongsasit/mod-th-cross-encoder	null	[ "transformers", "safetensors", "roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:51:32+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	happylayers/sc34	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:52:04+00:00
null	null		{}	andryxinson/sn25-1	null	[ "region:us" ]	null	2024-04-26T10:52:08+00:00
text-to-image	diffusers	# Crybaby Samples and prompts: ![AI image generator Crybaby samples](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/DQd80QaA8T5jW2EAX_pob.png) Top left: pretty cute little girl as Marie Antoinette playing on toy piano in bedroom Top right: Masterpiece, Best Quality, highres, fantasy, official art, kitten, grass, sky, scenery, Fuji 85mm, fairytale illustration, colored sclera, black eyes, perfect eyes, happy, cute, cat, whiskers, pawpads, claws, furry, plush, soft, perfect, tail, christmas lights, christmas tree, christmas ornaments, warmth Bottom left: analog style 70s color photograph of young Jet Lee as Invincible Man, star wars behind the scenes Bottom right: absurdres, adorable cute harley quinn, at night, dark alley, moon, :) red ponytail, blonde ponytail, in matte black hardsuit, military, roughed up, bat, city fog, A mix of MGM and CocaCola (which includes many models) to create a realistic version of Cryptids. Original pages: https://civitai.com/models/109568/mgmv1 https://huggingface.co/Yntec/Cryptids https://huggingface.co/Yntec/CocaCola https://civitai.com/models/142552?modelVersionId=163068 (Kitsch-In-Sync v2) https://civitai.com/models/21493/hellmix?modelVersionId=25632	{"language": ["en"], "license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["Paintings", "Style Art", "Landscapes", "Wick_J4", "iamxenos", "RIXYN", "Barons", "stable-diffusion", "stable-diffusion-diffusers", "diffusers", "text-to-image"], "pipeline_tag": "text-to-image"}	Yntec/Crybaby	null	[ "diffusers", "safetensors", "Paintings", "Style Art", "Landscapes", "Wick_J4", "iamxenos", "RIXYN", "Barons", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "en", "license:creativeml-openrail-m", "endpoints_compatible", "has_space", "diffusers:StableDiffusionPipeline", "region:us" ]	null	2024-04-26T10:52:19+00:00
image-classification	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned-waste This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the BioNonbioWaste dataset. It achieves the following results on the evaluation set: - Loss: 0.0048 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.1044 \| 0.5435 \| 100 \| 0.0418 \| 0.9826 \| \| 0.0517 \| 1.0870 \| 200 \| 0.0545 \| 0.9749 \| \| 0.0168 \| 1.6304 \| 300 \| 0.0099 \| 0.9961 \| \| 0.0526 \| 2.1739 \| 400 \| 0.0048 \| 1.0 \| \| 0.062 \| 2.7174 \| 500 \| 0.0196 \| 0.9942 \| \| 0.0088 \| 3.2609 \| 600 \| 0.0155 \| 0.9981 \| \| 0.0239 \| 3.8043 \| 700 \| 0.0106 \| 0.9981 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "finetuned-waste", "results": []}]}	Shamsaa/finetuned-waste	null	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:52:28+00:00
feature-extraction	transformers	This is the converted model from Unbabel/wmt23-cometkiwi-da 1) Just kept the weights/bias keys() 2) Renamed the keys to match the original Facebook/XLM-roberta-XL 3) kept the layer_wise_attention / estimator layers Because of a hack in HF's code I had to rename the "layerwise_attention.gamma" key to "layerwise_attention.gam" I changed the config.json key "layer_transformation" from sparsemax to softmax because there is a bug in COMET since the flag is not passed, the actual function used is the default which is softmax. Usage: ``` from transformers import XLMRobertaTokenizer, XLMRobertaTokenizerFast, AutoModel tokenizer = XLMRobertaTokenizerFast.from_pretrained("vince62s/wmt23-cometkiwi-da-roberta-xl", trust_remote_code=True) model = AutoModel.from_pretrained("vince62s/wmt23-cometkiwi-da-roberta-xl", trust_remote_code=True) text = "Hello world!</s></s>Bonjour le monde" encoded_text = tokenizer(text, return_tensors='pt') print(encoded_text) output = model(encoded_text) print(output[0]) {'input_ids': tensor([[ 0, 35378, 8999, 38, 2, 2, 84602, 95, 11146, 2]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])} tensor([[0.8217]], grad_fn=<AddmmBackward0>) ``` Let's double check with the original code from Unbabel Comet: ``` from comet import download_model, load_from_checkpoint model = load_from_checkpoint("/home/vincent/Downloads/cometkiwi23/checkpoints/model.ckpt") # this is the Unbabel checkpoint data = [{"mt": "Hello world!", "src": "Bonjour le monde"}] output = model.predict(data, gpus=0) print(output) Prediction([('scores', [0.8216837048530579]), ('system_score', 0.8216837048530579)]) ``` --- extra_gated_heading: Acknowledge license to accept the repository extra_gated_button_content: Acknowledge license pipeline_tag: translation language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: cc-by-nc-sa-4.0 library_name: transformers --- This is a [COMET](https://github.com/Unbabel/COMET) quality estimation model: It receives a source sentence and the respective translation and returns a score that reflects the quality of the translation. # Paper [CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task](https://aclanthology.org/2022.wmt-1.60) (Rei et al., WMT 2022) # License: cc-by-nc-sa-4.0 # Usage (unbabel-comet) Using this model requires unbabel-comet to be installed: ```bash pip install --upgrade pip # ensures that pip is current pip install "unbabel-comet>=2.0.0" ``` Make sure you acknowledge its License and Log in into Hugging face hub before using: ```bash huggingface-cli login # or using an environment variable huggingface-cli login --token $HUGGINGFACE_TOKEN ``` Then you can use it through comet CLI: ```bash comet-score -s {source-input}.txt -t {translation-output}.txt --model Unbabel/wmt22-cometkiwi-da ``` Or using Python: ```python from comet import download_model, load_from_checkpoint model_path = download_model("Unbabel/wmt22-cometkiwi-da") model = load_from_checkpoint(model_path) data = [ { "src": "The output signal provides constant sync so the display never glitches.", "mt": "Das Ausgangssignal bietet eine konstante Synchronisation, so dass die Anzeige nie stört." }, { "src": "Kroužek ilustrace je určen všem milovníkům umění ve věku od 10 do 15 let.", "mt": "Кільце ілюстрації призначене для всіх любителів мистецтва у віці від 10 до 15 років." }, { "src": "Mandela then became South Africa's first black president after his African National Congress party won the 1994 election.", "mt": "その後、1994年の選挙でアフリカ国民会議派が勝利し、南アフリカ初の黒人大統領となった。" } ] model_output = model.predict(data, batch_size=8, gpus=1) print (model_output) ``` # Intended uses Our model is intented to be used for reference-free MT evaluation**. Given a source text and its translation, outputs a single score between 0 and 1 where 1 represents a perfect translation. # Languages Covered: This model builds on top of InfoXLM which cover the following languages: Afrikaans, Albanian, Amharic, Arabic, Armenian, Assamese, Azerbaijani, Basque, Belarusian, Bengali, Bengali Romanized, Bosnian, Breton, Bulgarian, Burmese, Burmese, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Hausa, Hebrew, Hindi, Hindi Romanized, Hungarian, Icelandic, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish (Kurmanji), Kyrgyz, Lao, Latin, Latvian, Lithuanian, Macedonian, Malagasy, Malay, Malayalam, Marathi, Mongolian, Nepali, Norwegian, Oriya, Oromo, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Sanskri, Scottish, Gaelic, Serbian, Sindhi, Sinhala, Slovak, Slovenian, Somali, Spanish, Sundanese, Swahili, Swedish, Tamil, Tamil Romanized, Telugu, Telugu Romanized, Thai, Turkish, Ukrainian, Urdu, Urdu Romanized, Uyghur, Uzbek, Vietnamese, Welsh, Western, Frisian, Xhosa, Yiddish. Thus, results for language pairs containing uncovered languages are unreliable!	{}	vince62s/wmt23-cometkiwi-da-roberta-xl	null	[ "transformers", "pytorch", "xlm-roberta-xl", "feature-extraction", "custom_code", "region:us" ]	null	2024-04-26T10:52:57+00:00
text-generation	transformers		{"license": "llama2"}	AlekseyScorpi/llama_2_13b_vacancies_merged	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-26T10:53:04+00:00
text2text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "pipeline_tag": "text2text-generation"}	omertafveez/Llama-3-TherapyChatBot	null	[ "transformers", "safetensors", "llama", "feature-extraction", "text2text-generation", "arxiv:1910.09700", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-04-26T10:53:25+00:00
null	null		{"license": "openrail"}	Doutorfake/Gru_150	null	[ "license:openrail", "region:us" ]	null	2024-04-26T10:54:09+00:00
null	null		{"license": "apache-2.0"}	Ahmed123has/ahm	null	[ "license:apache-2.0", "region:us" ]	null	2024-04-26T10:54:44+00:00
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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: cosine - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "model-index": [{"name": "results", "results": []}]}	tariq9mehmood9/results	null	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "license:apache-2.0", "region:us" ]	null	2024-04-26T10:54:56+00:00
null	null		{}	ivykopal/english_prompt_squad_adapter	null	[ "region:us" ]	null	2024-04-26T10:55:00+00:00
null	null	# nchen909/Apollo-7B-Q4_K_M-GGUF This model was converted to GGUF format from [`FreedomIntelligence/Apollo-7B`](https://huggingface.co/FreedomIntelligence/Apollo-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/FreedomIntelligence/Apollo-7B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo nchen909/Apollo-7B-Q4_K_M-GGUF --model apollo-7b.Q4_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo nchen909/Apollo-7B-Q4_K_M-GGUF --model apollo-7b.Q4_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m apollo-7b.Q4_K_M.gguf -n 128 ```	{"license": "apache-2.0", "tags": ["llama-cpp", "gguf-my-repo"]}	nchen909/Apollo-7B-Q4_K_M-GGUF	null	[ "gguf", "llama-cpp", "gguf-my-repo", "license:apache-2.0", "region:us" ]	null	2024-04-26T10:56:13+00:00
text2text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	Nilesh360/llama-vid-7b-full-224-video-fps-1	null	[ "transformers", "safetensors", "llamavid", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:56:48+00:00
null	null		{}	mehdisebai/Enlighten_Instruct-text-to-rule_merged-GGUF	null	[ "gguf", "region:us" ]	null	2024-04-26T10:57:46+00:00
visual-question-answering	transformers		{}	seitzm97/Fine-tuned-BLIB-VQA	null	[ "transformers", "tensorboard", "safetensors", "blip", "visual-question-answering", "endpoints_compatible", "region:us" ]	null	2024-04-26T10:58:08+00:00
null	null		{}	ivykopal/english_prompt_squad_prompt	null	[ "region:us" ]	null	2024-04-26T11:00:19+00:00
question-answering	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # shipping_qa_model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5682 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 142 \| 0.5914 \| \| No log \| 2.0 \| 284 \| 0.5791 \| \| No log \| 3.0 \| 426 \| 0.5682 \| ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.2.2+cu118 - Datasets 2.18.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert/distilbert-base-uncased", "model-index": [{"name": "shipping_qa_model", "results": []}]}	SurajSphinx/shipping_qa_model	null	[ "transformers", "tensorboard", "safetensors", "distilbert", "question-answering", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-26T11:01:15+00:00
null	null		{}	mosesdaudu/whisper-small-hi	null	[ "region:us" ]	null	2024-04-26T11:01:48+00:00
null	null		{}	yuxiuaw/keqing	null	[ "region:us" ]	null	2024-04-26T11:03:37+00:00
object-detection	transformers		{"license": "cc-by-4.0"}	RoblabWhGe/rescuedet-yolos-small	null	[ "transformers", "safetensors", "yolos", "object-detection", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	null	2024-04-26T11:04:02+00:00
image-classification	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-xray-pneumonia-classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1489 - Accuracy: 0.9502 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.2873 \| 0.9961 \| 127 \| 0.1489 \| 0.9502 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "vit-xray-pneumonia-classification", "results": []}]}	cchoo1/vit-xray-pneumonia-classification	null	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-26T11:04:08+00:00

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