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NbAiLab/nb-bert-base-mnli
|
NbAiLab
| 2023-03-24T11:32:00Z | 363 | 9 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"nb-bert",
"zero-shot-classification",
"tensorflow",
"norwegian",
"no",
"dataset:mnli",
"dataset:multi_nli",
"dataset:xnli",
"arxiv:1909.00161",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
zero-shot-classification
| 2022-03-02T23:29:04Z |
---
language: no
license: cc-by-4.0
thumbnail: https://raw.githubusercontent.com/NBAiLab/notram/master/images/nblogo_2.png
pipeline_tag: zero-shot-classification
tags:
- nb-bert
- zero-shot-classification
- pytorch
- tensorflow
- norwegian
- bert
datasets:
- mnli
- multi_nli
- xnli
widget:
- example_title: Nyhetsartikkel om FHI
text: Folkehelseinstituttets mest optimistiske anslag er at alle voksne er ferdigvaksinert innen midten av september.
candidate_labels: helse, politikk, sport, religion
---
**Release 1.0** (March 11, 2021)
# NB-Bert base model finetuned on Norwegian machine translated MNLI
## Description
The most effective way of creating a good classifier is to finetune a pre-trained model for the specific task at hand. However, in many cases this is simply impossible.
[Yin et al.](https://arxiv.org/abs/1909.00161) proposed a very clever way of using pre-trained MNLI models as zero-shot sequence classifiers. The methods works by reformulating the question to an MNLI hypothesis. If we want to figure out if a text is about "sport", we simply state that "This text is about sport" ("Denne teksten handler om sport").
When the model is finetuned on the 400k large MNLI task, it is in many cases able to solve this classification tasks. There are no MNLI-set of this size in Norwegian but we have trained it on a machine translated version of the original MNLI-set.
## Testing the model
For testing the model, we recommend the [NbAiLab Colab Notebook](https://colab.research.google.com/gist/peregilk/769b5150a2f807219ab8f15dd11ea449/nbailab-mnli-norwegian-demo.ipynb)
## Hugging Face zero-shot-classification pipeline
The easiest way to try this out is by using the Hugging Face pipeline. Please, note that you will get better results when using Norwegian hypothesis template instead of the default English one.
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model="NbAiLab/nb-bert-base-mnli")
```
You can then use this pipeline to classify sequences into any of the class names you specify.
```python
sequence_to_classify = 'Folkehelseinstituttets mest optimistiske anslag er at alle voksne er ferdigvaksinert innen midten av september.'
candidate_labels = ['politikk', 'helse', 'sport', 'religion']
hypothesis_template = 'Dette eksempelet er {}.'
classifier(sequence_to_classify, candidate_labels, hypothesis_template=hypothesis_template, multi_class=True)
# {'labels': ['helse', 'politikk', 'sport', 'religion'],
# 'scores': [0.4210019111633301, 0.0674605593085289, 0.000840459018945694, 0.0007541406666859984],
# 'sequence': 'Folkehelseinstituttets mest optimistiske anslag er at alle over 18 år er ferdigvaksinert innen midten av september.'}
```
## More information
For more information on the model, see
https://github.com/NBAiLab/notram
Here you will also find a Colab explaining more in details how to use the zero-shot-classification pipeline.
|
Salesforce/mixqg-3b
|
Salesforce
| 2021-10-18T16:19:00Z | 363 | 7 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"arxiv:2110.08175",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
language: en
widget:
- text: Robert Boyle \\n In the late 17th century, Robert Boyle proved that air is necessary for combustion.
---
# MixQG (3b-sized model)
MixQG is a new question generation model pre-trained on a collection of QA datasets with a mix of answer types. It was introduced in the paper [MixQG: Neural Question Generation with Mixed Answer Types](https://arxiv.org/abs/2110.08175) and the associated code is released in [this](https://github.com/salesforce/QGen) repository.
### How to use
Using Huggingface pipeline abstraction:
```
from transformers import pipeline
nlp = pipeline("text2text-generation", model='Salesforce/mixqg-3b', tokenizer='Salesforce/mixqg-3b')
CONTEXT = "In the late 17th century, Robert Boyle proved that air is necessary for combustion."
ANSWER = "Robert Boyle"
def format_inputs(context: str, answer: str):
return f"{answer} \\n {context}"
text = format_inputs(CONTEXT, ANSWER)
nlp(text)
# should output [{'generated_text': 'Who proved that air is necessary for combustion?'}]
```
Using the pre-trained model directly:
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained('Salesforce/mixqg-3b')
model = AutoModelForSeq2SeqLM.from_pretrained('Salesforce/mixqg-3b')
CONTEXT = "In the late 17th century, Robert Boyle proved that air is necessary for combustion."
ANSWER = "Robert Boyle"
def format_inputs(context: str, answer: str):
return f"{answer} \\n {context}"
text = format_inputs(CONTEXT, ANSWER)
input_ids = tokenizer(text, return_tensors="pt").input_ids
generated_ids = model.generate(input_ids, max_length=32, num_beams=4)
output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
print(output)
# should output "Who proved that air is necessary for combustion?"
```
### Citation
```
@misc{murakhovska2021mixqg,
title={MixQG: Neural Question Generation with Mixed Answer Types},
author={Lidiya Murakhovs'ka and Chien-Sheng Wu and Tong Niu and Wenhao Liu and Caiming Xiong},
year={2021},
eprint={2110.08175},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
jonatasgrosman/whisper-large-zh-cv11
|
jonatasgrosman
| 2022-12-22T23:51:35Z | 363 | 66 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"zh",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-12-18T07:28:34Z |
---
language:
- zh
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
- cer
model-index:
- name: Whisper Large Chinese (Mandarin)
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 zh-CN
type: mozilla-foundation/common_voice_11_0
config: zh-CN
split: test
args: zh-CN
metrics:
- name: WER
type: wer
value: 55.02141421204441
- name: CER
type: cer
value: 9.550758567294045
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs cmn_hans_cn
type: google/fleurs
config: cmn_hans_cn
split: test
args: cmn_hans_cn
metrics:
- name: WER
type: wer
value: 70.62596203181118
- name: CER
type: cer
value: 11.761282471826888
---
# Whisper Large Chinese (Mandarin)
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on Chinese (Mandarin) using the train and validation splits of [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0). Not all validation split data were used during training, I extracted 1k samples from the validation split to be used for evaluation during fine-tuning.
## Usage
```python
from transformers import pipeline
transcriber = pipeline(
"automatic-speech-recognition",
model="jonatasgrosman/whisper-large-zh-cv11"
)
transcriber.model.config.forced_decoder_ids = (
transcriber.tokenizer.get_decoder_prompt_ids(
language="zh",
task="transcribe"
)
)
transcription = transcriber("path/to/my_audio.wav")
```
## Evaluation
I've performed the evaluation of the model using the test split of two datasets, the [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (same dataset used for the fine-tuning) and the [Fleurs](https://huggingface.co/datasets/google/fleurs) (dataset not seen during the fine-tuning). As Whisper can transcribe casing and punctuation, I've performed the model evaluation in 2 different scenarios, one using the raw text and the other using the normalized text (lowercase + removal of punctuations). Additionally, for the Fleurs dataset, I've evaluated the model in a scenario where there are no transcriptions of numerical values since the way these values are described in this dataset is different from how they are described in the dataset used in fine-tuning (Common Voice), so it is expected that this difference in the way of describing numerical values will affect the performance of the model for this type of transcription in Fleurs.
### Common Voice 11
| | CER | WER |
| --- | --- | --- |
| [jonatasgrosman/whisper-large-zh-cv11](https://huggingface.co/jonatasgrosman/whisper-large-zh-cv11) | 9.31 | 55.94 |
| [jonatasgrosman/whisper-large-zh-cv11](https://huggingface.co/jonatasgrosman/whisper-large-zh-cv11) + text normalization | 9.55 | 55.02 |
| [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) | 33.33 | 101.80 |
| [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) + text normalization | 29.90 | 95.91 |
### Fleurs
| | CER | WER |
| --- | --- | --- |
| [jonatasgrosman/whisper-large-zh-cv11](https://huggingface.co/jonatasgrosman/whisper-large-zh-cv11) | 15.00 | 93.45 |
| [jonatasgrosman/whisper-large-zh-cv11](https://huggingface.co/jonatasgrosman/whisper-large-zh-cv11) + text normalization | 11.76 | 70.63 |
| [jonatasgrosman/whisper-large-zh-cv11](https://huggingface.co/jonatasgrosman/whisper-large-zh-cv11) + keep only non-numeric samples | 10.95 | 87.91 |
| [jonatasgrosman/whisper-large-zh-cv11](https://huggingface.co/jonatasgrosman/whisper-large-zh-cv11) + text normalization + keep only non-numeric samples | 7.83 | 62.12 |
| [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) | 23.49 | 101.28 |
| [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) + text normalization | 17.58 | 83.22 |
| [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) + keep only non-numeric samples | 21.03 | 101.95 |
| [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) + text normalization + keep only non-numeric samples | 15.22 | 79.28 |
|
SargeZT/controlnet-sd-xl-1.0-depth-faid-vidit
|
SargeZT
| 2023-07-29T07:29:51Z | 363 | 5 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"controlnet",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-07-28T22:48:38Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-xl-base-1.0
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- controlnet
inference: true
---
# controlnet-SargeZT/controlnet-sd-xl-1.0-depth-faid-vidit
These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with new type of conditioning.
You can find some example images below.
prompt: the vaporwave hills from your nightmare, unsettling, light temperature 3500, light direction south-east
## License
[SDXL 1.0 License](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md)
|
TheBloke/LLaMA-7b-AWQ
|
TheBloke
| 2023-11-09T18:18:27Z | 363 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2023-09-20T02:27:31Z |
---
base_model: https://ai.meta.com/blog/large-language-model-llama-meta-ai
inference: false
license: other
model_creator: Meta
model_name: LLaMA 7B
model_type: llama
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# LLaMA 7B - AWQ
- Model creator: [Meta](https://huggingface.co/none)
- Original model: [LLaMA 7B](https://ai.meta.com/blog/large-language-model-llama-meta-ai)
<!-- description start -->
## Description
This repo contains AWQ model files for [Meta's LLaMA 7b](https://ai.meta.com/blog/large-language-model-llama-meta-ai).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference.
It is also now supported by continuous batching server [vLLM](https://github.com/vllm-project/vllm), allowing use of AWQ models for high-throughput concurrent inference in multi-user server scenarios. Note that, at the time of writing, overall throughput is still lower than running vLLM with unquantised models, however using AWQ enables using much smaller GPUs which can lead to easier deployment and overall cost savings. For example, a 70B model can be run on 1 x 48GB GPU instead of 2 x 80GB.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/LLaMA-7b-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/LLaMA-7b-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/LLaMA-7b-GGUF)
* [Meta's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/huggyllama/llama-7b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: None
```
{prompt}
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files and AWQ parameters
For my first release of AWQ models, I am releasing 128g models only. I will consider adding 32g as well if there is interest, and once I have done perplexity and evaluation comparisons, but at this time 32g models are still not fully tested with AutoAWQ and vLLM.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/LLaMA-7b-AWQ/tree/main) | 4 | 128 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 3.89 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Serving this model from vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- When using vLLM as a server, pass the `--quantization awq` parameter, for example:
```shell
python3 python -m vllm.entrypoints.api_server --model TheBloke/LLaMA-7b-AWQ --quantization awq
```
When using vLLM from Python code, pass the `quantization=awq` parameter, for example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/LLaMA-7b-AWQ", quantization="awq")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-python start -->
## How to use this AWQ model from Python code
### Install the necessary packages
Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.0.2 or later
```shell
pip3 install autoawq
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### You can then try the following example code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
model_name_or_path = "TheBloke/LLaMA-7b-AWQ"
# Load model
model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True,
trust_remote_code=False, safetensors=True)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False)
prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''
print("\n\n*** Generate:")
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
# Generate output
generation_output = model.generate(
tokens,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
max_new_tokens=512
)
print("Output: ", tokenizer.decode(generation_output[0]))
# Inference can also be done using transformers' pipeline
from transformers import pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with [AutoAWQ](https://github.com/casper-hansen/AutoAWQ), and [vLLM](https://github.com/vllm-project/vllm).
[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is not yet compatible with AWQ, but a PR is open which should bring support soon: [TGI PR #781](https://github.com/huggingface/text-generation-inference/issues/781).
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Meta's LLaMA 7b
This contains the weights for the LLaMA-7b model. This model is under a non-commercial license (see the LICENSE file).
You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform?usp=send_form) but either lost your copy of the weights or got some trouble converting them to the Transformers format.
|
TheBloke/MegaMix-S1-13B-GGUF
|
TheBloke
| 2023-09-30T18:32:19Z | 363 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"base_model:gradientputri/MegaMix-S1-13B",
"license:llama2",
"text-generation-inference",
"region:us"
] | null | 2023-09-30T18:26:24Z |
---
base_model: gradientputri/MegaMix-S1-13B
inference: false
license: llama2
model_creator: Putri
model_name: Megamix S1 13B
model_type: llama
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
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# Megamix S1 13B - GGUF
- Model creator: [Putri](https://huggingface.co/gradientputri)
- Original model: [Megamix S1 13B](https://huggingface.co/gradientputri/MegaMix-S1-13B)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Putri's Megamix S1 13B](https://huggingface.co/gradientputri/MegaMix-S1-13B).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/MegaMix-S1-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/MegaMix-S1-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/MegaMix-S1-13B-GGUF)
* [Putri's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/gradientputri/MegaMix-S1-13B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [megamix-s1-13b.Q2_K.gguf](https://huggingface.co/TheBloke/MegaMix-S1-13B-GGUF/blob/main/megamix-s1-13b.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes |
| [megamix-s1-13b.Q3_K_S.gguf](https://huggingface.co/TheBloke/MegaMix-S1-13B-GGUF/blob/main/megamix-s1-13b.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss |
| [megamix-s1-13b.Q3_K_M.gguf](https://huggingface.co/TheBloke/MegaMix-S1-13B-GGUF/blob/main/megamix-s1-13b.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss |
| [megamix-s1-13b.Q3_K_L.gguf](https://huggingface.co/TheBloke/MegaMix-S1-13B-GGUF/blob/main/megamix-s1-13b.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss |
| [megamix-s1-13b.Q4_0.gguf](https://huggingface.co/TheBloke/MegaMix-S1-13B-GGUF/blob/main/megamix-s1-13b.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [megamix-s1-13b.Q4_K_S.gguf](https://huggingface.co/TheBloke/MegaMix-S1-13B-GGUF/blob/main/megamix-s1-13b.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss |
| [megamix-s1-13b.Q4_K_M.gguf](https://huggingface.co/TheBloke/MegaMix-S1-13B-GGUF/blob/main/megamix-s1-13b.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended |
| [megamix-s1-13b.Q5_0.gguf](https://huggingface.co/TheBloke/MegaMix-S1-13B-GGUF/blob/main/megamix-s1-13b.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [megamix-s1-13b.Q5_K_S.gguf](https://huggingface.co/TheBloke/MegaMix-S1-13B-GGUF/blob/main/megamix-s1-13b.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
| [megamix-s1-13b.Q5_K_M.gguf](https://huggingface.co/TheBloke/MegaMix-S1-13B-GGUF/blob/main/megamix-s1-13b.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |
| [megamix-s1-13b.Q6_K.gguf](https://huggingface.co/TheBloke/MegaMix-S1-13B-GGUF/blob/main/megamix-s1-13b.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss |
| [megamix-s1-13b.Q8_0.gguf](https://huggingface.co/TheBloke/MegaMix-S1-13B-GGUF/blob/main/megamix-s1-13b.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/MegaMix-S1-13B-GGUF and below it, a specific filename to download, such as: megamix-s1-13b.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/MegaMix-S1-13B-GGUF megamix-s1-13b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/MegaMix-S1-13B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/MegaMix-S1-13B-GGUF megamix-s1-13b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m megamix-s1-13b.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/MegaMix-S1-13B-GGUF", model_file="megamix-s1-13b.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
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## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
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**Special thanks to**: Aemon Algiz.
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Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Putri's Megamix S1 13B
No original model card was available.
<!-- original-model-card end -->
|
LanguageBind/LanguageBind_Depth
|
LanguageBind
| 2024-02-01T06:57:09Z | 363 | 0 |
transformers
|
[
"transformers",
"pytorch",
"LanguageBindDepth",
"zero-shot-image-classification",
"arxiv:2310.01852",
"license:mit",
"endpoints_compatible",
"region:us"
] |
zero-shot-image-classification
| 2023-10-06T09:07:38Z |
---
license: mit
---
<p align="center">
<img src="https://s11.ax1x.com/2024/02/01/pFMDAm9.png" width="250" style="margin-bottom: 0.2;"/>
<p>
<h2 align="center"> <a href="https://arxiv.org/pdf/2310.01852.pdf">【ICLR 2024 🔥】LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment</a></h2>
<h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for latest update. </h2>
## 📰 News
* **[2024.01.27]** 👀👀👀 Our [MoE-LLaVA](https://github.com/PKU-YuanGroup/MoE-LLaVA) is released! A sparse model with 3B parameters outperformed the dense model with 7B parameters.
* **[2024.01.16]** 🔥🔥🔥 Our LanguageBind has been accepted at ICLR 2024! We earn the score of 6(3)8(6)6(6)6(6) [here](https://openreview.net/forum?id=QmZKc7UZCy¬eId=OgsxQxAleA).
* **[2023.12.15]** 💪💪💪 We expand the 💥💥💥 VIDAL dataset and now have **10M video-text data**. We launch **LanguageBind_Video 1.5**, checking our [model zoo](#-model-zoo).
* **[2023.12.10]** We expand the 💥💥💥 VIDAL dataset and now have **10M depth and 10M thermal data**. We are in the process of uploading thermal and depth data on [Hugging Face](https://huggingface.co/datasets/LanguageBind/VIDAL-Depth-Thermal) and expect the whole process to last 1-2 months.
* **[2023.11.27]** 🔥🔥🔥 We have updated our [paper](https://arxiv.org/abs/2310.01852) with emergency zero-shot results., checking our ✨ [results](#emergency-results).
* **[2023.11.26]** 💥💥💥 We have open-sourced all textual sources and corresponding YouTube IDs [here](DATASETS.md).
* **[2023.11.26]** 📣📣📣 We have open-sourced fully fine-tuned **Video & Audio**, achieving improved performance once again, checking our [model zoo](#-model-zoo).
* **[2023.11.22]** We are about to release a fully fine-tuned version, and the **HUGE** version is currently undergoing training.
* **[2023.11.21]** 💥 We are releasing sample data in [DATASETS.md](DATASETS.md) so that individuals who are interested can further modify the code to train it on their own data.
* **[2023.11.20]** 🚀🚀🚀 [Video-LLaVA](https://github.com/PKU-YuanGroup/Video-LLaVA) builds a large visual-language model to achieve 🎉SOTA performances based on LanguageBind encoders.
* **[2023.10.23]** 🎶 LanguageBind-Audio achieves 🎉🎉🎉**state-of-the-art (SOTA) performance on 5 datasets**, checking our ✨ [results](#multiple-modalities)!
* **[2023.10.14]** 😱 Released a stronger LanguageBind-Video, checking our ✨ [results](#video-language)! The video checkpoint **have updated** on Huggingface Model Hub!
* **[2023.10.10]** We provide sample data, which can be found in [assets](assets), and [emergency zero-shot usage](#emergency-zero-shot) is described.
* **[2023.10.07]** The checkpoints are available on 🤗 [Huggingface Model](https://huggingface.co/LanguageBind).
* **[2023.10.04]** Code and [demo](https://huggingface.co/spaces/LanguageBind/LanguageBind) are available now! Welcome to **watch** 👀 this repository for the latest updates.
## 😮 Highlights
### 💡 High performance, but NO intermediate modality required
LanguageBind is a **language-centric** multimodal pretraining approach, **taking the language as the bind across different modalities** because the language modality is well-explored and contains rich semantics.
* The following first figure shows the architecture of LanguageBind. LanguageBind can be easily extended to segmentation, detection tasks, and potentially to unlimited modalities.
### ⚡️ A multimodal, fully aligned and voluminous dataset
We propose **VIDAL-10M**, **10 Million data** with **V**ideo, **I**nfrared, **D**epth, **A**udio and their corresponding **L**anguage, which greatly expands the data beyond visual modalities.
* The second figure shows our proposed VIDAL-10M dataset, which includes five modalities: video, infrared, depth, audio, and language.
### 🔥 Multi-view enhanced description for training
We make multi-view enhancements to language. We produce multi-view description that combines **meta-data**, **spatial**, and **temporal** to greatly enhance the semantic information of the language. In addition we further **enhance the language with ChatGPT** to create a good semantic space for each modality aligned language.
## 🤗 Demo
* **Local demo.** Highly recommend trying out our web demo, which incorporates all features currently supported by LanguageBind.
```bash
python gradio_app.py
```
* **Online demo.** We provide the [online demo](https://huggingface.co/spaces/LanguageBind/LanguageBind) in Huggingface Spaces. In this demo, you can calculate the similarity of modalities to language, such as audio-to-language, video-to-language, and depth-to-image.
## 🛠️ Requirements and Installation
* Python >= 3.8
* Pytorch >= 1.13.1
* CUDA Version >= 11.6
* Install required packages:
```bash
git clone https://github.com/PKU-YuanGroup/LanguageBind
cd LanguageBind
pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
pip install -r requirements.txt
```
## 🐳 Model Zoo
The names in the table represent different encoder models. For example, `LanguageBind/LanguageBind_Video_FT` represents the fully fine-tuned version, while `LanguageBind/LanguageBind_Video` represents the LoRA-tuned version.
You can freely replace them in the recommended [API usage](#-api). We recommend using the fully fine-tuned version, as it offers stronger performance.
<div align="center">
<table border="1" width="100%">
<tr align="center">
<th>Modality</th><th>LoRA tuning</th><th>Fine-tuning</th>
</tr>
<tr align="center">
<td>Video</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video">LanguageBind_Video</a></td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_FT">LanguageBind_Video_FT</a></td>
</tr>
<tr align="center">
<td>Audio</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Audio">LanguageBind_Audio</a></td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Audio_FT">LanguageBind_Audio_FT</a></td>
</tr>
<tr align="center">
<td>Depth</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Depth">LanguageBind_Depth</a></td><td>-</td>
</tr>
<tr align="center">
<td>Thermal</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Thermal">LanguageBind_Thermal</a></td><td>-</td>
</tr>
</table>
</div>
<div align="center">
<table border="1" width="100%">
<tr align="center">
<th>Version</th><th>Tuning</th><th>Model size</th><th>Num_frames</th><th>HF Link</th><th>MSR-VTT</th><th>DiDeMo</th><th>ActivityNet</th><th>MSVD</th>
</tr>
<tr align="center">
<td>LanguageBind_Video</td><td>LoRA</td><td>Large</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video">Link</a></td><td>42.6</td><td>37.8</td><td>35.1</td><td>52.2</td>
</tr>
<tr align="center">
<td>LanguageBind_Video_FT</td><td>Full-tuning</td><td>Large</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_FT">Link</a></td><td>42.7</td><td>38.1</td><td>36.9</td><td>53.5</td>
</tr>
<tr align="center">
<td>LanguageBind_Video_V1.5_FT</td><td>Full-tuning</td><td>Large</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_V1.5_FT">Link</a></td><td>42.8</td><td>39.7</td><td>38.4</td><td>54.1</td>
</tr>
<tr align="center">
<td>LanguageBind_Video_V1.5_FT</td><td>Full-tuning</td><td>Large</td><td>12</td><td>Coming soon</td>
</tr>
<tr align="center">
<td>LanguageBind_Video_Huge_V1.5_FT</td><td>Full-tuning</td><td>Huge</td><td>8</td><td><a href="https://huggingface.co/LanguageBind/LanguageBind_Video_Huge_V1.5_FT">Link</a></td><td>44.8</td><td>39.9</td><td>41.0</td><td>53.7</td>
</tr>
<tr align="center">
<td>LanguageBind_Video_Huge_V1.5_FT</td><td>Full-tuning</td><td>Huge</td><td>12</td><td>Coming soon</td>
</tr>
</table>
</div>
## 🤖 API
**We open source all modalities preprocessing code.** If you want to load the model (e.g. ```LanguageBind/LanguageBind_Thermal```) from the model hub on Huggingface or on local, you can use the following code snippets!
### Inference for Multi-modal Binding
We have provided some sample datasets in [assets](assets) to quickly see how languagebind works.
```python
import torch
from languagebind import LanguageBind, to_device, transform_dict, LanguageBindImageTokenizer
if __name__ == '__main__':
device = 'cuda:0'
device = torch.device(device)
clip_type = {
'video': 'LanguageBind_Video_FT', # also LanguageBind_Video
'audio': 'LanguageBind_Audio_FT', # also LanguageBind_Audio
'thermal': 'LanguageBind_Thermal',
'image': 'LanguageBind_Image',
'depth': 'LanguageBind_Depth',
}
model = LanguageBind(clip_type=clip_type, cache_dir='./cache_dir')
model = model.to(device)
model.eval()
pretrained_ckpt = f'lb203/LanguageBind_Image'
tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir/tokenizer_cache_dir')
modality_transform = {c: transform_dict[c](model.modality_config[c]) for c in clip_type.keys()}
image = ['assets/image/0.jpg', 'assets/image/1.jpg']
audio = ['assets/audio/0.wav', 'assets/audio/1.wav']
video = ['assets/video/0.mp4', 'assets/video/1.mp4']
depth = ['assets/depth/0.png', 'assets/depth/1.png']
thermal = ['assets/thermal/0.jpg', 'assets/thermal/1.jpg']
language = ["Training a parakeet to climb up a ladder.", 'A lion climbing a tree to catch a monkey.']
inputs = {
'image': to_device(modality_transform['image'](image), device),
'video': to_device(modality_transform['video'](video), device),
'audio': to_device(modality_transform['audio'](audio), device),
'depth': to_device(modality_transform['depth'](depth), device),
'thermal': to_device(modality_transform['thermal'](thermal), device),
}
inputs['language'] = to_device(tokenizer(language, max_length=77, padding='max_length',
truncation=True, return_tensors='pt'), device)
with torch.no_grad():
embeddings = model(inputs)
print("Video x Text: \n",
torch.softmax(embeddings['video'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
print("Image x Text: \n",
torch.softmax(embeddings['image'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
print("Depth x Text: \n",
torch.softmax(embeddings['depth'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
print("Audio x Text: \n",
torch.softmax(embeddings['audio'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
print("Thermal x Text: \n",
torch.softmax(embeddings['thermal'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
```
Then returns the following result.
```bash
Video x Text:
[[9.9989331e-01 1.0667283e-04]
[1.3255903e-03 9.9867439e-01]]
Image x Text:
[[9.9990666e-01 9.3292067e-05]
[4.6132666e-08 1.0000000e+00]]
Depth x Text:
[[0.9954276 0.00457235]
[0.12042473 0.8795753 ]]
Audio x Text:
[[0.97634876 0.02365119]
[0.02917843 0.97082156]]
Thermal x Text:
[[0.9482511 0.0517489 ]
[0.48746133 0.5125386 ]]
```
### Emergency zero-shot
Since languagebind binds each modality together, we also found the **emergency zero-shot**. It's very simple to use.
```python
print("Video x Audio: \n", torch.softmax(embeddings['video'] @ embeddings['audio'].T, dim=-1).detach().cpu().numpy())
print("Image x Depth: \n", torch.softmax(embeddings['image'] @ embeddings['depth'].T, dim=-1).detach().cpu().numpy())
print("Image x Thermal: \n", torch.softmax(embeddings['image'] @ embeddings['thermal'].T, dim=-1).detach().cpu().numpy())
```
Then, you will get:
```
Video x Audio:
[[1.0000000e+00 0.0000000e+00]
[3.1150486e-32 1.0000000e+00]]
Image x Depth:
[[1. 0.]
[0. 1.]]
Image x Thermal:
[[1. 0.]
[0. 1.]]
```
### Different branches for X-Language task
Additionally, LanguageBind can be **disassembled into different branches** to handle different tasks. Note that we do not train Image, which just initialize from OpenCLIP.
#### Thermal
```python
import torch
from languagebind import LanguageBindThermal, LanguageBindThermalTokenizer, LanguageBindThermalProcessor
pretrained_ckpt = 'LanguageBind/LanguageBind_Thermal'
model = LanguageBindThermal.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
tokenizer = LanguageBindThermalTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
thermal_process = LanguageBindThermalProcessor(model.config, tokenizer)
model.eval()
data = thermal_process([r"your/thermal.jpg"], ['your text'], return_tensors='pt')
with torch.no_grad():
out = model(**data)
print(out.text_embeds @ out.image_embeds.T)
```
#### Depth
```python
import torch
from languagebind import LanguageBindDepth, LanguageBindDepthTokenizer, LanguageBindDepthProcessor
pretrained_ckpt = 'LanguageBind/LanguageBind_Depth'
model = LanguageBindDepth.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
tokenizer = LanguageBindDepthTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
depth_process = LanguageBindDepthProcessor(model.config, tokenizer)
model.eval()
data = depth_process([r"your/depth.png"], ['your text.'], return_tensors='pt')
with torch.no_grad():
out = model(**data)
print(out.text_embeds @ out.image_embeds.T)
```
#### Video
```python
import torch
from languagebind import LanguageBindVideo, LanguageBindVideoTokenizer, LanguageBindVideoProcessor
pretrained_ckpt = 'LanguageBind/LanguageBind_Video_FT' # also 'LanguageBind/LanguageBind_Video'
model = LanguageBindVideo.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
tokenizer = LanguageBindVideoTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
video_process = LanguageBindVideoProcessor(model.config, tokenizer)
model.eval()
data = video_process(["your/video.mp4"], ['your text.'], return_tensors='pt')
with torch.no_grad():
out = model(**data)
print(out.text_embeds @ out.image_embeds.T)
```
#### Audio
```python
import torch
from languagebind import LanguageBindAudio, LanguageBindAudioTokenizer, LanguageBindAudioProcessor
pretrained_ckpt = 'LanguageBind/LanguageBind_Audio_FT' # also 'LanguageBind/LanguageBind_Audio'
model = LanguageBindAudio.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
tokenizer = LanguageBindAudioTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
audio_process = LanguageBindAudioProcessor(model.config, tokenizer)
model.eval()
data = audio_process([r"your/audio.wav"], ['your audio.'], return_tensors='pt')
with torch.no_grad():
out = model(**data)
print(out.text_embeds @ out.image_embeds.T)
```
#### Image
Note that our image encoder is the same as OpenCLIP. **Not** as fine-tuned as other modalities.
```python
import torch
from languagebind import LanguageBindImage, LanguageBindImageTokenizer, LanguageBindImageProcessor
pretrained_ckpt = 'LanguageBind/LanguageBind_Image'
model = LanguageBindImage.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
image_process = LanguageBindImageProcessor(model.config, tokenizer)
model.eval()
data = image_process([r"your/image.jpg"], ['your text.'], return_tensors='pt')
with torch.no_grad():
out = model(**data)
print(out.text_embeds @ out.image_embeds.T)
```
## 💥 VIDAL-10M
The datasets is in [DATASETS.md](DATASETS.md).
## 🗝️ Training & Validating
The training & validating instruction is in [TRAIN_AND_VALIDATE.md](TRAIN_AND_VALIDATE.md).
## 👍 Acknowledgement
* [OpenCLIP](https://github.com/mlfoundations/open_clip) An open source pretraining framework.
* [CLIP4Clip](https://github.com/ArrowLuo/CLIP4Clip) An open source Video-Text retrieval framework.
* [sRGB-TIR](https://github.com/rpmsnu/sRGB-TIR) An open source framework to generate infrared (thermal) images.
* [GLPN](https://github.com/vinvino02/GLPDepth) An open source framework to generate depth images.
## 🔒 License
* The majority of this project is released under the MIT license as found in the [LICENSE](https://github.com/PKU-YuanGroup/LanguageBind/blob/main/LICENSE) file.
* The dataset of this project is released under the CC-BY-NC 4.0 license as found in the [DATASET_LICENSE](https://github.com/PKU-YuanGroup/LanguageBind/blob/main/DATASET_LICENSE) file.
## ✏️ Citation
If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.
```BibTeX
@misc{zhu2023languagebind,
title={LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment},
author={Bin Zhu and Bin Lin and Munan Ning and Yang Yan and Jiaxi Cui and Wang HongFa and Yatian Pang and Wenhao Jiang and Junwu Zhang and Zongwei Li and Cai Wan Zhang and Zhifeng Li and Wei Liu and Li Yuan},
year={2023},
eprint={2310.01852},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## ✨ Star History
[](https://star-history.com/#PKU-YuanGroup/LanguageBind&Date)
## 🤝 Contributors
<a href="https://github.com/PKU-YuanGroup/LanguageBind/graphs/contributors">
<img src="https://contrib.rocks/image?repo=PKU-YuanGroup/LanguageBind" />
</a>
|
maddes8cht/syzymon-long_llama_3b_instruct-gguf
|
maddes8cht
| 2023-11-26T23:51:54Z | 363 | 2 | null |
[
"gguf",
"code",
"text-generation-inference",
"text-generation",
"dataset:Open-Orca/OpenOrca",
"dataset:zetavg/ShareGPT-Processed",
"dataset:bigcode/starcoderdata",
"dataset:togethercomputer/RedPajama-Data-1T",
"dataset:tiiuae/falcon-refinedweb",
"arxiv:2307.03170",
"arxiv:2305.16300",
"model-index",
"region:us"
] |
text-generation
| 2023-11-26T21:33:10Z |
---
datasets:
- Open-Orca/OpenOrca
- zetavg/ShareGPT-Processed
- bigcode/starcoderdata
- togethercomputer/RedPajama-Data-1T
- tiiuae/falcon-refinedweb
metrics:
- code_eval
- accuracy
pipeline_tag: text-generation
tags:
- code
- text-generation-inference
model-index:
- name: long_llama_3b_instruct
results:
- task:
name: Code Generation
type: code-generation
dataset:
name: "HumanEval"
type: openai_humaneval
metrics:
- name: pass@1
type: pass@1
value: 0.12
verified: false
---
[]()
I'm constantly enhancing these model descriptions to provide you with the most relevant and comprehensive information
# long_llama_3b_instruct - GGUF
- Model creator: [syzymon](https://huggingface.co/syzymon)
- Original model: [long_llama_3b_instruct](https://huggingface.co/syzymon/long_llama_3b_instruct)
OpenLlama is a free reimplementation of the original Llama Model which is licensed under Apache 2 license.
# About GGUF format
`gguf` is the current file format used by the [`ggml`](https://github.com/ggerganov/ggml) library.
A growing list of Software is using it and can therefore use this model.
The core project making use of the ggml library is the [llama.cpp](https://github.com/ggerganov/llama.cpp) project by Georgi Gerganov
# Quantization variants
There is a bunch of quantized files available to cater to your specific needs. Here's how to choose the best option for you:
# Legacy quants
Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are `legacy` quantization types.
Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants.
## Note:
Now there's a new option to use K-quants even for previously 'incompatible' models, although this involves some fallback solution that makes them not *real* K-quants. More details can be found in affected model descriptions.
(This mainly refers to Falcon 7b and Starcoder models)
# K-quants
K-quants are designed with the idea that different levels of quantization in specific parts of the model can optimize performance, file size, and memory load.
So, if possible, use K-quants.
With a Q6_K, you'll likely find it challenging to discern a quality difference from the original model - ask your model two times the same question and you may encounter bigger quality differences.
---
# Original Model Card:
# LongLLaMA: Focused Transformer Training for Context Scaling
<div align="center">
<a href="https://colab.research.google.com/github/CStanKonrad/long_llama/blob/main/long_llama_instruct_colab.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg"></a>
</div>
<div align="center">
[TLDR](#TLDR) | [Overview](#Overview) | [Usage](#Usage) | [LongLLaMA performance](#LongLLaMA-performance) | [Authors](#Authors) | [Citation](#Citation) | [License](#License) | [Acknowledgments](#Acknowledgments)
</div>
## TLDR
This repo contains [LongLLaMA-Instruct-3Bv1.1](https://huggingface.co/syzymon/long_llama_3b_instruct) that is for **research purposes only**.
LongLLaMA is built upon the foundation of [OpenLLaMA](https://github.com/openlm-research/open_llama) and fine-tuned using the [Focused Transformer (FoT)](https://arxiv.org/abs/2307.03170) method. We release a smaller 3B base variant (not instruction tuned) of the LongLLaMA model on a permissive license (Apache 2.0) and inference code supporting longer contexts on [Hugging Face](https://huggingface.co/syzymon/long_llama_3b). Our model weights can serve as the drop-in replacement of LLaMA in existing implementations (for short context up to 2048 tokens). Additionally, we provide evaluation results and comparisons against the original OpenLLaMA models. Stay tuned for further updates.
## Overview
### Base models
[Focused Transformer: Contrastive Training for Context Scaling](https://arxiv.org/abs/2307.03170) (FoT) presents a simple method for endowing language models with the ability to handle context consisting possibly of millions of tokens while training on significantly shorter input. FoT permits a subset of attention layers to access a memory cache of (key, value) pairs to extend the context length. The distinctive aspect of FoT is its training procedure, drawing from contrastive learning. Specifically, we deliberately expose the memory attention layers to both relevant and irrelevant keys (like negative samples from unrelated documents). This strategy incentivizes the model to differentiate keys connected with semantically diverse values, thereby enhancing their structure. This, in turn, makes it possible to extrapolate the effective context length much beyond what is seen in training.
**LongLLaMA** is an [OpenLLaMA](https://github.com/openlm-research/open_llama) model finetuned with the FoT method,
with three layers used for context extension. **Crucially, LongLLaMA is able to extrapolate much beyond the context length seen in training: $8k$. E.g., in the passkey retrieval task, it can handle inputs of length $256k$**.
<div align="center">
| | [LongLLaMA-3B](https://huggingface.co/syzymon/long_llama_3b_instruct) | [LongLLaMA-3Bv1.1](https://huggingface.co/syzymon/long_llama_3b_v1_1) | LongLLaMA-7B<br />*(coming soon)*| LongLLaMA-13B<br />*(coming soon)*|
|----------------|----------|----------|-----------|-----------|
| Source model | [OpenLLaMA-3B](https://huggingface.co/openlm-research/open_llama_3b_easylm) | [OpenLLaMA-3Bv2](https://huggingface.co/openlm-research/open_llama_3b_v2_easylm) | - | - |
| Source model tokens | 1T | 1 T | - | - |
| Fine-tuning tokens | 10B | 5B | - | -|
| Memory layers | 6, 12, 18 | 6, 12, 18 | - | -|
</div>
### Instruction/Chat tuning
In the [fine_tuning](fine_tuning) subfolder we provide the code that was used to create [LongLLaMA-Instruct-3Bv1.1](https://huggingface.co/syzymon/long_llama_3b_instruct), an instruction-tuned version of [LongLLaMA-3Bv1.1](https://huggingface.co/syzymon/long_llama_3b_v1_1). We used [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) (instructions) and [zetavg/ShareGPT-Processed](https://huggingface.co/datasets/zetavg/ShareGPT-Processed) (chat) datasets for tuning.
## Usage
See also:
* [Colab with LongLLaMA-Instruct-3Bv1.1](https://colab.research.google.com/github/CStanKonrad/long_llama/blob/main/long_llama_instruct_colab.ipynb).
* [Colab with an example usage of base LongLLaMA](https://colab.research.google.com/github/CStanKonrad/long_llama/blob/main/long_llama_colab.ipynb).
### Requirements
```
pip install --upgrade pip
pip install transformers==4.30 sentencepiece accelerate
```
### Loading model
```python
import torch
from transformers import LlamaTokenizer, AutoModelForCausalLM
tokenizer = LlamaTokenizer.from_pretrained("syzymon/long_llama_3b_instruct")
model = AutoModelForCausalLM.from_pretrained("syzymon/long_llama_3b_instruct",
torch_dtype=torch.float32,
trust_remote_code=True)
```
### Input handling and generation
LongLLaMA uses the Hugging Face interface, the long input given to the model will be
split into context windows and loaded into the memory cache.
```python
prompt = "My name is Julien and I like to"
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
outputs = model(input_ids=input_ids)
```
During the model call, one can provide the parameter `last_context_length` (default $1024$), which specifies the number of tokens left in the last context window. Tuning this parameter can improve generation as the first layers do not have access to memory. See details in [How LongLLaMA handles long inputs](#How-LongLLaMA-handles-long-inputs).
```python
generation_output = model.generate(
input_ids=input_ids,
max_new_tokens=256,
num_beams=1,
last_context_length=1792,
do_sample=True,
temperature=1.0,
)
print(tokenizer.decode(generation_output[0]))
```
### Additional configuration
LongLLaMA has several other parameters:
* `mem_layers` specifies layers endowed with memory (should be either an empty list or a list of all memory layers specified in the description of the checkpoint).
* `mem_dtype` allows changing the type of memory cache
* `mem_attention_grouping` can trade off speed for reduced memory usage.
When equal to `(4, 2048)`, the memory layers will process at most $4*2048$ queries at once ($4$ heads and $2048$ queries for each head).
```python
import torch
from transformers import LlamaTokenizer, AutoModelForCausalLM
tokenizer = LlamaTokenizer.from_pretrained("syzymon/long_llama_3b_instruct")
model = AutoModelForCausalLM.from_pretrained(
"syzymon/long_llama_3b_instruct", torch_dtype=torch.float32,
mem_layers=[],
mem_dtype='bfloat16',
trust_remote_code=True,
mem_attention_grouping=(4, 2048),
)
```
### Drop-in use with LLaMA code
LongLLaMA checkpoints can also be used as a drop-in replacement for LLaMA checkpoints in [Hugging Face implementation of LLaMA](https://huggingface.co/docs/transformers/main/model_doc/llama), but in this case, they will be limited to the original context length of $2048$.
```python
from transformers import LlamaTokenizer, LlamaForCausalLM
import torch
tokenizer = LlamaTokenizer.from_pretrained("syzymon/long_llama_3b_instruct")
model = LlamaForCausalLM.from_pretrained("syzymon/long_llama_3b_instruct", torch_dtype=torch.float32)
```
### How LongLLaMA handles long inputs
Inputs over $2048$ tokens are automatically split into windows $w_1, \ldots, w_m$. The first $m-2$ windows contain $2048$ tokens each, $w_{m-1}$ has no more than $2048$ tokens, and $w_m$ contains the number of tokens specified by `last_context_length`. The model processes the windows one by one extending the memory cache after each. If `use_cache` is `True`, the last window will not be loaded to the memory cache but to the local (generation) cache.
The memory cache stores $(key, value)$ pairs for each head of the specified memory layers `mem_layers`. In addition to this, it stores attention masks.
If `use_cache=True` (which is the case in generation), LongLLaMA will use two caches: the memory cache for the specified layers and the local (generation) cache for all layers. When the local cache exceeds $2048$ elements, its content is moved to the memory cache for the memory layers.
For simplicity, context extension is realized with a memory cache and full attention in this repo. Replacing this simple mechanism with a KNN search over an external database is possible with systems like [Faiss](https://github.com/facebookresearch/faiss). This potentially would enable further context length scaling. We leave this as a future work.
## LongLLaMA performance
We present some illustrative examples of LongLLaMA results. Refer to our paper [Focused Transformer: Contrastive Training for Context Scaling](https://arxiv.org/abs/2307.03170) for more details.
We manage to achieve good performance on the passkey retrieval task from [Landmark Attention: Random-Access Infinite Context Length for Transformers](https://arxiv.org/abs/2305.16300). The code for generating the prompt and running the model is located in `examples/passkey.py`.
<p align="center" width="100%">
<img src="assets/plot_passkey.png" alt="LongLLaMA" style="width: 70%; min-width: 300px; display: block; margin: auto;">
</p>
Our LongLLaMA 3B model also shows improvements when using long context on two downstream tasks, TREC question classification and WebQS question answering.
<div align="center">
| Context/Dataset | TREC | WebQS |
| --- | --- | --- |
| $2K$ | 67.0 | 21.2 |
| $4K$ | 71.6 | 21.4 |
| $6K$ | 72.9 | 22.2 |
| $8K$ | **73.3** | **22.4** |
</div>
LongLLaMA retains performance on tasks that do not require long context. We provide a comparison with OpenLLaMA
on [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) in the zero-shot setting.
<div align="center">
| Task/Metric | OpenLLaMA-3B | LongLLaMA-3B |
|----------------|----------|-----------|
| anli_r1/acc | 0.33 | 0.32 |
| anli_r2/acc | 0.32 | 0.33 |
| anli_r3/acc | 0.35 | 0.35 |
| arc_challenge/acc | 0.34 | 0.34 |
| arc_challenge/acc_norm | 0.37 | 0.37 |
| arc_easy/acc | 0.69 | 0.68 |
| arc_easy/acc_norm | 0.65 | 0.63 |
| boolq/acc | 0.68 | 0.68 |
| hellaswag/acc | 0.49 | 0.48 |
| hellaswag/acc_norm | 0.67 | 0.65 |
| openbookqa/acc | 0.27 | 0.28 |
| openbookqa/acc_norm | 0.40 | 0.38 |
| piqa/acc | 0.75 | 0.73 |
| piqa/acc_norm | 0.76 | 0.75 |
| record/em | 0.88 | 0.87 |
| record/f1 | 0.89 | 0.87 |
| rte/acc | 0.58 | 0.60 |
| truthfulqa_mc/mc1 | 0.22 | 0.24 |
| truthfulqa_mc/mc2 | 0.35 | 0.38 |
| wic/acc | 0.48 | 0.50 |
| winogrande/acc | 0.62 | 0.60 |
| Avg score | 0.53 | 0.53 |
</div>
Starting with v1.1 models we have decided to use [EleutherAI](https://github.com/EleutherAI) implementation of [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) wit a slight modification, that adds `<bos>` token at beginning of input sequence. The results are provided in the table below.
<div align="center">
| description | LongLLaMA-3B | OpenLLaMA-3Bv2 | LongLLaMA-3Bv1.1 | LongLLaMA-Instruct-3Bv1.1 |
|:-----------------------|:--------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|
| anli_r1/acc | 0.32 | 0.33 | 0.31 | 0.33 |
| anli_r2/acc | 0.33 | 0.35 | 0.33 | 0.35 |
| anli_r3/acc | 0.35 | 0.38 | 0.35 | 0.38 |
| arc_challenge/acc | 0.34 | 0.33 | 0.32 | 0.36 |
| arc_challenge/acc_norm | 0.37 | 0.36 | 0.36 | 0.37 |
| arc_easy/acc | 0.67 | 0.68 | 0.68 | 0.7 |
| arc_easy/acc_norm | 0.63 | 0.63 | 0.63 | 0.63 |
| boolq/acc | 0.68 | 0.67 | 0.66 | 0.77 |
| hellaswag/acc | 0.48 | 0.53 | 0.52 | 0.52 |
| hellaswag/acc_norm | 0.65 | 0.7 | 0.69 | 0.68 |
| openbookqa/acc | 0.28 | 0.28 | 0.28 | 0.28 |
| openbookqa/acc_norm | 0.38 | 0.39 | 0.37 | 0.41 |
| piqa/acc | 0.73 | 0.77 | 0.77 | 0.78 |
| piqa/acc_norm | 0.75 | 0.78 | 0.77 | 0.77 |
| record/em | 0.87 | 0.87 | 0.86 | 0.85 |
| record/f1 | 0.88 | 0.88 | 0.87 | 0.86 |
| rte/acc | 0.6 | 0.53 | 0.62 | 0.7 |
| truthfulqa_mc/mc1 | 0.24 | 0.22 | 0.21 | 0.25 |
| truthfulqa_mc/mc2 | 0.38 | 0.35 | 0.35 | 0.4 |
| wic/acc | 0.5 | 0.5 | 0.5 | 0.54 |
| winogrande/acc | 0.6 | 0.66 | 0.63 | 0.65 |
| Avg score | 0.53 | 0.53 | 0.53 | 0.55 |
</div>
We also provide the results on human-eval. We cut the generated text after either
* `"\ndef "`
* `"\nclass "`
* `"\nif __name__"`
<div align="center">
| | OpenLLaMA-3Bv2 | LongLLaMA-3Bv1.1 | LongLLaMA-Instruct-3Bv1.1 |
| - | - | - | - |
| pass@1| 0.09| 0.12 | 0.12 |
</div>
## Authors
- [Szymon Tworkowski](https://scholar.google.com/citations?user=1V8AeXYAAAAJ&hl=en)
- [Konrad Staniszewski](https://scholar.google.com/citations?user=CM6PCBYAAAAJ)
- [Mikołaj Pacek](https://scholar.google.com/citations?user=eh6iEbQAAAAJ&hl=en&oi=ao)
- [Henryk Michalewski](https://scholar.google.com/citations?user=YdHW1ycAAAAJ&hl=en)
- [Yuhuai Wu](https://scholar.google.com/citations?user=bOQGfFIAAAAJ&hl=en)
- [Piotr Miłoś](https://scholar.google.pl/citations?user=Se68XecAAAAJ&hl=pl&oi=ao)
## Citation
To cite this work please use
```bibtex
@misc{tworkowski2023focused,
title={Focused Transformer: Contrastive Training for Context Scaling},
author={Szymon Tworkowski and Konrad Staniszewski and Mikołaj Pacek and Yuhuai Wu and Henryk Michalewski and Piotr Miłoś},
year={2023},
eprint={2307.03170},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
The code and base models checkpoints are licensed under [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0).
The instruction/chat tuned models are for research purposes only.
Some of the examples use external code (see headers of files for copyright notices and licenses).
## Acknowledgments
We gratefully acknowledge the TPU Research Cloud program, which was instrumental to our research by providing significant computational resources. We are also grateful to Xinyang Geng and Hao Liu for releasing [OpenLLaMA](https://github.com/openlm-research/open_llama) checkpoints and the [EasyLM](https://github.com/young-geng/EasyLM) library.
***End of original Model File***
---
## Please consider to support my work
**Coming Soon:** I'm in the process of launching a sponsorship/crowdfunding campaign for my work. I'm evaluating Kickstarter, Patreon, or the new GitHub Sponsors platform, and I am hoping for some support and contribution to the continued availability of these kind of models. Your support will enable me to provide even more valuable resources and maintain the models you rely on. Your patience and ongoing support are greatly appreciated as I work to make this page an even more valuable resource for the community.
<center>
[](https://maddes8cht.github.io)
[](https://stackexchange.com/users/26485911)
[](https://github.com/maddes8cht)
[](https://huggingface.co/maddes8cht)
[](https://twitter.com/maddes1966)
</center>
|
SparseLLM/prosparse-llama-2-7b-gguf
|
SparseLLM
| 2024-03-11T12:27:27Z | 363 | 1 |
transformers
|
[
"transformers",
"gguf",
"sparsellama",
"feature-extraction",
"custom_code",
"en",
"arxiv:2402.13516",
"license:llama2",
"region:us"
] |
feature-extraction
| 2024-02-20T16:08:35Z |
---
license: llama2
language:
- en
---
# ProSparse-LLaMA-2-7B-GGUF
- Original model: [SparseLLM/ProSparse-LLaMA-2-7B](https://huggingface.co/SparseLLM/prosparse-llama-2-7b)
- Converted & distributed by: [THUNLP](https://nlp.csai.tsinghua.edu.cn/), [ModelBest](modelbest.cn), and [PowerInfer](https://huggingface.co/PowerInfer)
This model is the downstream distribution of [SparseLLM/ProSparse-LLaMA-2-7B](https://huggingface.co/SparseLLM/prosparse-llama-2-7b) in PowerInfer GGUF format consisting of the LLM model weights and predictor weights.
Note: `prosparse-llama-2-7b-clip15.gguf` is a variant GGUF version with the same model but different activation predictors, which are trained with data only reserving top 15% activation values. Compared with `prosparse-llama-2-7b.gguf`, this variant has higher predicted sparsity and inference speed, but suffering from relatively lower activation recall.
### Citation
Please kindly cite using the following BibTeX:
```bibtex
@article{song2024prosparse,
title={{ProSparse}: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models},
author={Song, Chenyang and Han, Xu and Zhang, Zhengyan and Hu, Shengding and Shi, Xiyu and Li, Kuai and Chen, Chen and Liu, Zhiyuan and Li, Guangli and Yang, Tao and Sun, Maosong},
year={2024},
journal={arXiv preprint arXiv:2402.13516},
url={https://arxiv.org/pdf/2402.13516.pdf}
}
```
|
dreamgen/opus-v1.2-70b-gguf
|
dreamgen
| 2024-03-13T19:34:29Z | 363 | 2 | null |
[
"gguf",
"unsloth",
"axolotl",
"text-generation",
"en",
"region:us"
] |
text-generation
| 2024-03-07T07:15:47Z |
---
language:
- en
pipeline_tag: text-generation
tags:
- unsloth
- axolotl
---
# DreamGen Opus V1
**DreamGen Opus V1** is a family of **uncensored** models fine-tuned for **(steerable) story-writing and role-playing**.
*WARNING:* GGUF versions might not perform as well as FP16 or AWQ.
See the full model [dreamgen/opus-v1.2-70b](https://huggingface.co/dreamgen/opus-v1.2-70b) for documentation.
[See other Opus V1 variants](https://huggingface.co/collections/dreamgen/opus-v1-65d092a6f8ab7fc669111b31).
|
mradermacher/opus-v1-34b-i1-GGUF
|
mradermacher
| 2024-05-10T16:42:50Z | 363 | 1 |
transformers
|
[
"transformers",
"gguf",
"unsloth",
"axolotl",
"en",
"base_model:dreamgen/opus-v1-34b",
"license:cc-by-nc-nd-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-03-14T17:43:21Z |
---
base_model: dreamgen/opus-v1-34b
language:
- en
library_name: transformers
license: cc-by-nc-nd-4.0
quantized_by: mradermacher
tags:
- unsloth
- axolotl
---
## About
weighted/imatrix quants of https://huggingface.co/dreamgen/opus-v1-34b
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/opus-v1-34b-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/opus-v1-34b-i1-GGUF/resolve/main/opus-v1-34b.i1-IQ1_S.gguf) | i1-IQ1_S | 8.2 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/opus-v1-34b-i1-GGUF/resolve/main/opus-v1-34b.i1-IQ1_M.gguf) | i1-IQ1_M | 8.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/opus-v1-34b-i1-GGUF/resolve/main/opus-v1-34b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 10.0 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v1-34b-i1-GGUF/resolve/main/opus-v1-34b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 11.0 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v1-34b-i1-GGUF/resolve/main/opus-v1-34b.i1-IQ2_S.gguf) | i1-IQ2_S | 11.6 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v1-34b-i1-GGUF/resolve/main/opus-v1-34b.i1-IQ2_M.gguf) | i1-IQ2_M | 12.5 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v1-34b-i1-GGUF/resolve/main/opus-v1-34b.i1-Q2_K.gguf) | i1-Q2_K | 13.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/opus-v1-34b-i1-GGUF/resolve/main/opus-v1-34b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 14.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/opus-v1-34b-i1-GGUF/resolve/main/opus-v1-34b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 14.8 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v1-34b-i1-GGUF/resolve/main/opus-v1-34b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 15.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/opus-v1-34b-i1-GGUF/resolve/main/opus-v1-34b.i1-IQ3_S.gguf) | i1-IQ3_S | 15.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/opus-v1-34b-i1-GGUF/resolve/main/opus-v1-34b.i1-IQ3_M.gguf) | i1-IQ3_M | 16.2 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v1-34b-i1-GGUF/resolve/main/opus-v1-34b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 17.3 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/opus-v1-34b-i1-GGUF/resolve/main/opus-v1-34b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 18.6 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v1-34b-i1-GGUF/resolve/main/opus-v1-34b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 18.8 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/opus-v1-34b-i1-GGUF/resolve/main/opus-v1-34b.i1-Q4_0.gguf) | i1-Q4_0 | 19.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/opus-v1-34b-i1-GGUF/resolve/main/opus-v1-34b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 20.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/opus-v1-34b-i1-GGUF/resolve/main/opus-v1-34b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 21.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/opus-v1-34b-i1-GGUF/resolve/main/opus-v1-34b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 24.3 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v1-34b-i1-GGUF/resolve/main/opus-v1-34b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 25.0 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v1-34b-i1-GGUF/resolve/main/opus-v1-34b.i1-Q6_K.gguf) | i1-Q6_K | 28.9 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/WestIceLemonTeaRP-32k-GGUF
|
mradermacher
| 2024-05-06T04:40:30Z | 363 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"alpaca",
"mistral",
"not-for-all-audiences",
"nsfw",
"en",
"base_model:icefog72/WestIceLemonTeaRP-32k",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-18T12:41:51Z |
---
base_model: icefog72/WestIceLemonTeaRP-32k
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- mergekit
- merge
- alpaca
- mistral
- not-for-all-audiences
- nsfw
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/icefog72/WestIceLemonTeaRP-32k
<!-- 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/WestIceLemonTeaRP-32k-GGUF/resolve/main/WestIceLemonTeaRP-32k.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/WestIceLemonTeaRP-32k-GGUF/resolve/main/WestIceLemonTeaRP-32k.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/WestIceLemonTeaRP-32k-GGUF/resolve/main/WestIceLemonTeaRP-32k.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/WestIceLemonTeaRP-32k-GGUF/resolve/main/WestIceLemonTeaRP-32k.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/WestIceLemonTeaRP-32k-GGUF/resolve/main/WestIceLemonTeaRP-32k.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/WestIceLemonTeaRP-32k-GGUF/resolve/main/WestIceLemonTeaRP-32k.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/WestIceLemonTeaRP-32k-GGUF/resolve/main/WestIceLemonTeaRP-32k.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/WestIceLemonTeaRP-32k-GGUF/resolve/main/WestIceLemonTeaRP-32k.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/WestIceLemonTeaRP-32k-GGUF/resolve/main/WestIceLemonTeaRP-32k.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/WestIceLemonTeaRP-32k-GGUF/resolve/main/WestIceLemonTeaRP-32k.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/WestIceLemonTeaRP-32k-GGUF/resolve/main/WestIceLemonTeaRP-32k.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/WestIceLemonTeaRP-32k-GGUF/resolve/main/WestIceLemonTeaRP-32k.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/WestIceLemonTeaRP-32k-GGUF/resolve/main/WestIceLemonTeaRP-32k.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/WestIceLemonTeaRP-32k-GGUF/resolve/main/WestIceLemonTeaRP-32k.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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 -->
|
DavidAU/D_AU-Multi-Verse-RP-Yarn-Mistral-7b-128k-DPO-Q4_K_M-GGUF
|
DavidAU
| 2024-04-30T08:12:50Z | 363 | 0 | null |
[
"gguf",
"merge",
"mergekit",
"lazymergekit",
"Eric111/Yarn-Mistral-7b-128k-DPO",
"saishf/Multi-Verse-RP-7B",
"llama-cpp",
"gguf-my-repo",
"base_model:Eric111/Yarn-Mistral-7b-128k-DPO",
"base_model:saishf/Multi-Verse-RP-7B",
"region:us"
] | null | 2024-04-30T08:12:34Z |
---
tags:
- merge
- mergekit
- lazymergekit
- Eric111/Yarn-Mistral-7b-128k-DPO
- saishf/Multi-Verse-RP-7B
- llama-cpp
- gguf-my-repo
base_model:
- Eric111/Yarn-Mistral-7b-128k-DPO
- saishf/Multi-Verse-RP-7B
---
# DavidAU/D_AU-Multi-Verse-RP-Yarn-Mistral-7b-128k-DPO-Q4_K_M-GGUF
This model was converted to GGUF format from [`DavidAU/D_AU-Multi-Verse-RP-Yarn-Mistral-7b-128k-DPO`](https://huggingface.co/DavidAU/D_AU-Multi-Verse-RP-Yarn-Mistral-7b-128k-DPO) 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/DavidAU/D_AU-Multi-Verse-RP-Yarn-Mistral-7b-128k-DPO) 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 DavidAU/D_AU-Multi-Verse-RP-Yarn-Mistral-7b-128k-DPO-Q4_K_M-GGUF --model d_au-multi-verse-rp-yarn-mistral-7b-128k-dpo.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/D_AU-Multi-Verse-RP-Yarn-Mistral-7b-128k-DPO-Q4_K_M-GGUF --model d_au-multi-verse-rp-yarn-mistral-7b-128k-dpo.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 d_au-multi-verse-rp-yarn-mistral-7b-128k-dpo.Q4_K_M.gguf -n 128
```
|
PekingU/rtdetr_r18vd
|
PekingU
| 2024-07-01T14:18:02Z | 363 | 3 |
transformers
|
[
"transformers",
"safetensors",
"rt_detr",
"object-detection",
"vision",
"en",
"dataset:coco",
"arxiv:2304.08069",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2024-05-21T13:38:11Z |
---
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: object-detection
tags:
- object-detection
- vision
datasets:
- coco
widget:
- src: >-
https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg
example_title: Savanna
- src: >-
https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
example_title: Football Match
- src: >-
https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg
example_title: Airport
---
# Model Card for RT-DETR
## Table of Contents
1. [Model Details](#model-details)
2. [Model Sources](#model-sources)
3. [How to Get Started with the Model](#how-to-get-started-with-the-model)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Model Architecture and Objective](#model-architecture-and-objective)
7. [Citation](#citation)
## Model Details
> The YOLO series has become the most popular framework for real-time object detection due to its reasonable trade-off between speed and accuracy.
However, we observe that the speed and accuracy of YOLOs are negatively affected by the NMS.
Recently, end-to-end Transformer-based detectors (DETRs) have provided an alternative to eliminating NMS.
Nevertheless, the high computational cost limits their practicality and hinders them from fully exploiting the advantage of excluding NMS.
In this paper, we propose the Real-Time DEtection TRansformer (RT-DETR), the first real-time end-to-end object detector to our best knowledge that addresses the above dilemma.
We build RT-DETR in two steps, drawing on the advanced DETR:
first we focus on maintaining accuracy while improving speed, followed by maintaining speed while improving accuracy.
Specifically, we design an efficient hybrid encoder to expeditiously process multi-scale features by decoupling intra-scale interaction and cross-scale fusion to improve speed.
Then, we propose the uncertainty-minimal query selection to provide high-quality initial queries to the decoder, thereby improving accuracy.
In addition, RT-DETR supports flexible speed tuning by adjusting the number of decoder layers to adapt to various scenarios without retraining.
Our RT-DETR-R50 / R101 achieves 53.1% / 54.3% AP on COCO and 108 / 74 FPS on T4 GPU, outperforming previously advanced YOLOs in both speed and accuracy.
We also develop scaled RT-DETRs that outperform the lighter YOLO detectors (S and M models).
Furthermore, RT-DETR-R50 outperforms DINO-R50 by 2.2% AP in accuracy and about 21 times in FPS.
After pre-training with Objects365, RT-DETR-R50 / R101 achieves 55.3% / 56.2% AP. The project page: this [https URL](https://zhao-yian.github.io/RTDETR/).
This is the model card of a 🤗 [transformers](https://huggingface.co/docs/transformers/index) model that has been pushed on the Hub.
- **Developed by:** Yian Zhao and Sangbum Choi
- **Funded by:** National Key R&D Program of China (No.2022ZD0118201), Natural Science Foundation of China (No.61972217, 32071459, 62176249, 62006133, 62271465),
and the Shenzhen Medical Research Funds in China (No.
B2302037).
- **Shared by:** Sangbum Choi
- **Model type:** [RT-DETR](https://huggingface.co/docs/transformers/main/en/model_doc/rt_detr)
- **License:** Apache-2.0
### Model Sources
<!-- Provide the basic links for the model. -->
- **HF Docs:** [RT-DETR](https://huggingface.co/docs/transformers/main/en/model_doc/rt_detr)
- **Repository:** https://github.com/lyuwenyu/RT-DETR
- **Paper:** https://arxiv.org/abs/2304.08069
- **Demo:** [RT-DETR Tracking](https://huggingface.co/spaces/merve/RT-DETR-tracking-coco)
## How to Get Started with the Model
Use the code below to get started with the model.
```python
import torch
import requests
from PIL import Image
from transformers import RTDetrForObjectDetection, RTDetrImageProcessor
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_r18vd")
model = RTDetrForObjectDetection.from_pretrained("PekingU/rtdetr_r18vd")
inputs = image_processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3)
for result in results:
for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
score, label = score.item(), label_id.item()
box = [round(i, 2) for i in box.tolist()]
print(f"{model.config.id2label[label]}: {score:.2f} {box}")
```
This should output
```
sofa: 0.97 [0.14, 0.38, 640.13, 476.21]
cat: 0.96 [343.38, 24.28, 640.14, 371.5]
cat: 0.96 [13.23, 54.18, 318.98, 472.22]
remote: 0.95 [40.11, 73.44, 175.96, 118.48]
remote: 0.92 [333.73, 76.58, 369.97, 186.99]
```
## 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. -->
The RTDETR model was trained on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively.
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
We conduct experiments on COCO and Objects365 datasets, where RT-DETR is trained on COCO train2017 and validated on COCO val2017 dataset.
We report the standard COCO metrics, including AP (averaged over uniformly sampled IoU thresholds ranging from 0.50-0.95 with a step size of 0.05),
AP50, AP75, as well as AP at different scales: APS, APM, APL.
### Preprocessing
Images are resized to 640x640 pixels and rescaled with `image_mean=[0.485, 0.456, 0.406]` and `image_std=[0.229, 0.224, 0.225]`.
### Training Hyperparameters
- **Training regime:** <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
## Evaluation
| Model | #Epochs | #Params (M) | GFLOPs | FPS_bs=1 | AP (val) | AP50 (val) | AP75 (val) | AP-s (val) | AP-m (val) | AP-l (val) |
|----------------------------|---------|-------------|--------|----------|--------|-----------|-----------|----------|----------|----------|
| RT-DETR-R18 | 72 | 20 | 60.7 | 217 | 46.5 | 63.8 | 50.4 | 28.4 | 49.8 | 63.0 |
| RT-DETR-R34 | 72 | 31 | 91.0 | 172 | 48.5 | 66.2 | 52.3 | 30.2 | 51.9 | 66.2 |
| RT-DETR R50 | 72 | 42 | 136 | 108 | 53.1 | 71.3 | 57.7 | 34.8 | 58.0 | 70.0 |
| RT-DETR R101| 72 | 76 | 259 | 74 | 54.3 | 72.7 | 58.6 | 36.0 | 58.8 | 72.1 |
| RT-DETR-R18 (Objects 365 pretrained) | 60 | 20 | 61 | 217 | 49.2 | 66.6 | 53.5 | 33.2 | 52.3 | 64.8 |
| RT-DETR-R50 (Objects 365 pretrained) | 24 | 42 | 136 | 108 | 55.3 | 73.4 | 60.1 | 37.9 | 59.9 | 71.8 |
| RT-DETR-R101 (Objects 365 pretrained) | 24 | 76 | 259 | 74 | 56.2 | 74.6 | 61.3 | 38.3 | 60.5 | 73.5 |
### Model Architecture and Objective
Overview of RT-DETR. We feed the features from the last three stages of the backbone into the encoder. The efficient hybrid
encoder transforms multi-scale features into a sequence of image features through the Attention-based Intra-scale Feature Interaction (AIFI)
and the CNN-based Cross-scale Feature Fusion (CCFF). Then, the uncertainty-minimal query selection selects a fixed number of encoder
features to serve as initial object queries for the decoder. Finally, the decoder with auxiliary prediction heads iteratively optimizes object
queries to generate categories and boxes.
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
```bibtex
@misc{lv2023detrs,
title={DETRs Beat YOLOs on Real-time Object Detection},
author={Yian Zhao and Wenyu Lv and Shangliang Xu and Jinman Wei and Guanzhong Wang and Qingqing Dang and Yi Liu and Jie Chen},
year={2023},
eprint={2304.08069},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## Model Card Authors
[Sangbum Choi](https://huggingface.co/danelcsb)
[Pavel Iakubovskii](https://huggingface.co/qubvel-hf)
|
mradermacher/openbuddy-yi1.5-9b-v21.1-32k-GGUF
|
mradermacher
| 2024-05-22T21:39:05Z | 363 | 1 |
transformers
|
[
"transformers",
"gguf",
"mixtral",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"fi",
"base_model:OpenBuddy/openbuddy-yi1.5-9b-v21.1-32k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-22T20:42:35Z |
---
base_model: OpenBuddy/openbuddy-yi1.5-9b-v21.1-32k
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- ru
- fi
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mixtral
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/OpenBuddy/openbuddy-yi1.5-9b-v21.1-32k
<!-- 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/openbuddy-yi1.5-9b-v21.1-32k-GGUF/resolve/main/openbuddy-yi1.5-9b-v21.1-32k.Q2_K.gguf) | Q2_K | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-9b-v21.1-32k-GGUF/resolve/main/openbuddy-yi1.5-9b-v21.1-32k.IQ3_XS.gguf) | IQ3_XS | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-9b-v21.1-32k-GGUF/resolve/main/openbuddy-yi1.5-9b-v21.1-32k.Q3_K_S.gguf) | Q3_K_S | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-9b-v21.1-32k-GGUF/resolve/main/openbuddy-yi1.5-9b-v21.1-32k.IQ3_S.gguf) | IQ3_S | 4.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-9b-v21.1-32k-GGUF/resolve/main/openbuddy-yi1.5-9b-v21.1-32k.IQ3_M.gguf) | IQ3_M | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-9b-v21.1-32k-GGUF/resolve/main/openbuddy-yi1.5-9b-v21.1-32k.Q3_K_M.gguf) | Q3_K_M | 4.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-9b-v21.1-32k-GGUF/resolve/main/openbuddy-yi1.5-9b-v21.1-32k.Q3_K_L.gguf) | Q3_K_L | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-9b-v21.1-32k-GGUF/resolve/main/openbuddy-yi1.5-9b-v21.1-32k.IQ4_XS.gguf) | IQ4_XS | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-9b-v21.1-32k-GGUF/resolve/main/openbuddy-yi1.5-9b-v21.1-32k.Q4_K_S.gguf) | Q4_K_S | 5.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-9b-v21.1-32k-GGUF/resolve/main/openbuddy-yi1.5-9b-v21.1-32k.Q4_K_M.gguf) | Q4_K_M | 5.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-9b-v21.1-32k-GGUF/resolve/main/openbuddy-yi1.5-9b-v21.1-32k.Q5_K_S.gguf) | Q5_K_S | 6.2 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-9b-v21.1-32k-GGUF/resolve/main/openbuddy-yi1.5-9b-v21.1-32k.Q5_K_M.gguf) | Q5_K_M | 6.4 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-9b-v21.1-32k-GGUF/resolve/main/openbuddy-yi1.5-9b-v21.1-32k.Q6_K.gguf) | Q6_K | 7.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-9b-v21.1-32k-GGUF/resolve/main/openbuddy-yi1.5-9b-v21.1-32k.Q8_0.gguf) | Q8_0 | 9.5 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-yi1.5-9b-v21.1-32k-GGUF/resolve/main/openbuddy-yi1.5-9b-v21.1-32k.f16.gguf) | f16 | 17.8 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Maixtchup-4x7b-i1-GGUF
|
mradermacher
| 2024-06-01T16:29:22Z | 363 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:StatsGary/Maixtchup-4x7b",
"endpoints_compatible",
"region:us"
] | null | 2024-05-31T05:40:59Z |
---
base_model: StatsGary/Maixtchup-4x7b
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/StatsGary/Maixtchup-4x7b
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Maixtchup-4x7b-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/Maixtchup-4x7b-i1-GGUF/resolve/main/Maixtchup-4x7b.i1-IQ1_S.gguf) | i1-IQ1_S | 5.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Maixtchup-4x7b-i1-GGUF/resolve/main/Maixtchup-4x7b.i1-IQ1_M.gguf) | i1-IQ1_M | 5.6 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Maixtchup-4x7b-i1-GGUF/resolve/main/Maixtchup-4x7b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 6.5 | |
| [GGUF](https://huggingface.co/mradermacher/Maixtchup-4x7b-i1-GGUF/resolve/main/Maixtchup-4x7b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 7.2 | |
| [GGUF](https://huggingface.co/mradermacher/Maixtchup-4x7b-i1-GGUF/resolve/main/Maixtchup-4x7b.i1-IQ2_S.gguf) | i1-IQ2_S | 7.4 | |
| [GGUF](https://huggingface.co/mradermacher/Maixtchup-4x7b-i1-GGUF/resolve/main/Maixtchup-4x7b.i1-IQ2_M.gguf) | i1-IQ2_M | 8.1 | |
| [GGUF](https://huggingface.co/mradermacher/Maixtchup-4x7b-i1-GGUF/resolve/main/Maixtchup-4x7b.i1-Q2_K.gguf) | i1-Q2_K | 8.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Maixtchup-4x7b-i1-GGUF/resolve/main/Maixtchup-4x7b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 9.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Maixtchup-4x7b-i1-GGUF/resolve/main/Maixtchup-4x7b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 10.0 | |
| [GGUF](https://huggingface.co/mradermacher/Maixtchup-4x7b-i1-GGUF/resolve/main/Maixtchup-4x7b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 10.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Maixtchup-4x7b-i1-GGUF/resolve/main/Maixtchup-4x7b.i1-IQ3_S.gguf) | i1-IQ3_S | 10.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Maixtchup-4x7b-i1-GGUF/resolve/main/Maixtchup-4x7b.i1-IQ3_M.gguf) | i1-IQ3_M | 10.7 | |
| [GGUF](https://huggingface.co/mradermacher/Maixtchup-4x7b-i1-GGUF/resolve/main/Maixtchup-4x7b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 11.7 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Maixtchup-4x7b-i1-GGUF/resolve/main/Maixtchup-4x7b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 12.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Maixtchup-4x7b-i1-GGUF/resolve/main/Maixtchup-4x7b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 13.0 | |
| [GGUF](https://huggingface.co/mradermacher/Maixtchup-4x7b-i1-GGUF/resolve/main/Maixtchup-4x7b.i1-Q4_0.gguf) | i1-Q4_0 | 13.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Maixtchup-4x7b-i1-GGUF/resolve/main/Maixtchup-4x7b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 13.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Maixtchup-4x7b-i1-GGUF/resolve/main/Maixtchup-4x7b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 14.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Maixtchup-4x7b-i1-GGUF/resolve/main/Maixtchup-4x7b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 16.7 | |
| [GGUF](https://huggingface.co/mradermacher/Maixtchup-4x7b-i1-GGUF/resolve/main/Maixtchup-4x7b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 17.2 | |
| [GGUF](https://huggingface.co/mradermacher/Maixtchup-4x7b-i1-GGUF/resolve/main/Maixtchup-4x7b.i1-Q6_K.gguf) | i1-Q6_K | 19.9 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
gx123/test-model-1
|
gx123
| 2024-06-18T05:47:14Z | 363 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-3-medium",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2024-06-17T02:29:21Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: 艺术
parameters:
negative_prompt: PP
output:
url: images/generated_00.png
base_model: stabilityai/stable-diffusion-3-medium
instance_prompt: null
license: apache-2.0
---
|
ckiplab/albert-base-chinese
|
ckiplab
| 2022-05-10T03:28:08Z | 362 | 8 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"fill-mask",
"lm-head",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language:
- zh
thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
tags:
- pytorch
- lm-head
- albert
- zh
license: gpl-3.0
---
# CKIP ALBERT Base Chinese
This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。
## Homepage
- https://github.com/ckiplab/ckip-transformers
## Contributers
- [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer)
## Usage
Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.
請使用 BertTokenizerFast 而非 AutoTokenizer。
```
from transformers import (
BertTokenizerFast,
AutoModel,
)
tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
model = AutoModel.from_pretrained('ckiplab/albert-base-chinese')
```
For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.
有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
|
hfl/chinese-electra-base-discriminator
|
hfl
| 2021-03-03T01:40:07Z | 362 | 6 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language:
- zh
license: "apache-2.0"
---
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
pie/example-ner-spanclf-conll03
|
pie
| 2022-01-02T10:13:27Z | 362 | 0 |
transformers
|
[
"transformers",
"pytorch",
"TransformerSpanClassificationModel",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
Entry not found
|
prajjwal1/bert-medium-mnli
|
prajjwal1
| 2021-10-05T17:56:07Z | 362 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"arxiv:1908.08962",
"arxiv:2110.01518",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert). These BERT variants were introduced in the paper [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962). These models are trained on MNLI.
If you use the model, please consider citing the paper
```
@misc{bhargava2021generalization,
title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
year={2021},
eprint={2110.01518},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
Original Implementation and more info can be found in [this Github repository](https://github.com/prajjwal1/generalize_lm_nli).
```
MNLI: 75.86%
MNLI-mm: 77.03%
```
These models are trained for 4 epochs.
[@prajjwal_1](https://twitter.com/prajjwal_1)
|
crumb/bloom-560m-RLHF-SD2-prompter-aesthetic
|
crumb
| 2023-03-19T06:19:32Z | 362 | 19 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bloom",
"text-generation",
"stable-diffusion",
"diffusion",
"dataset:Gustavosta/Stable-Diffusion-Prompts",
"license:bigscience-bloom-rail-1.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2022-12-01T13:59:28Z |
---
license: bigscience-bloom-rail-1.0
tags:
- stable-diffusion
- diffusion
model-index:
- name: bloom-560m-RLHF-SD2-prompter
results: []
datasets:
- Gustavosta/Stable-Diffusion-Prompts
widget:
- text: "<s>Prompt: "
inference:
parameters:
eos_token_id: 2
max_length: 128
do_sample: true
---
# The RAT (RLHF-Aesthetic Tuned model for prompt synthesis)
**COLAB DEMO INCLUDING STABLE DIFFUSION: https://colab.research.google.com/github/aicrumb/doohickey/blob/main/rlhf_prompt_tuner.ipynb**
This is a further finetuned version of [crumb/bloom-560m-RLHF-SD2-prompter](https://hf.co/crumb/bloom-560m-RLHF-SD2-prompter) to optimize for aesthetic score with models from https://github.com/crowsonkb/simulacra-aesthetic-models instead of me hand scoring each image
donate so i can do this on real hardware : https://github.com/aicrumb/aicrumb/blob/main/README.md
trained at bs=32, lr=0.0001, only tuning biases and layernorm weights
## Example usage
```python
# Install libraries needed to run the models
!pip install transformers diffusers accelerate -qq
# Import the libraries
from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
from transformers import pipeline
import torch
# This is the model that the transformer was finetuned to generate prompts for
model_id = "stabilityai/stable-diffusion-2-base"
# Use the Euler scheduler here
scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, revision="fp16", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
# Load the transformer model
prompt_pipe = pipeline("text-generation", model="crumb/bloom-560m-RLHF-SD2-prompter-aesthetic")
prompt = "cool landscape"
# Auto-complete prompt
prompt = "<s>Prompt: " + prompt + ","
extended_prompt = prompt_pipe(prompt, do_sample=True, max_length=42)[0]['generated_text']
extended_prompt = extended_prompt[10:]
print("Prompt is now: ", extended_prompt)
# Generate image
image = pipe(extended_prompt).images[0]
image.save("output.png")
image
```
## Limitations
Aesthetic scoring models have been shown to have very large biases, and one I noticed is it really likes images of women no matter the actual quality, so those were optimized for more than other things.
Also it fell into the trap of rlhf models, it gets kinda same-ey, so if you don't like the general "stable diffusion, trending on artstation" look this might not be for you.
|
timm/resnet152d.ra2_in1k
|
timm
| 2024-02-10T23:40:22Z | 362 | 0 |
timm
|
[
"timm",
"pytorch",
"safetensors",
"image-classification",
"arxiv:2110.00476",
"arxiv:1512.03385",
"arxiv:1812.01187",
"license:apache-2.0",
"region:us"
] |
image-classification
| 2023-04-05T18:38:17Z |
---
license: apache-2.0
library_name: timm
tags:
- image-classification
- timm
---
# Model card for resnet152d.ra2_in1k
A ResNet-D image classification model.
This model features:
* ReLU activations
* 3-layer stem of 3x3 convolutions with pooling
* 2x2 average pool + 1x1 convolution shortcut downsample
Trained on ImageNet-1k in `timm` using recipe template described below.
Recipe details:
* RandAugment `RA2` recipe. Inspired by and evolved from EfficientNet RandAugment recipes. Published as `B` recipe in [ResNet Strikes Back](https://arxiv.org/abs/2110.00476).
* RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging
* Step (exponential decay w/ staircase) LR schedule with warmup
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 60.2
- GMACs: 15.4
- Activations (M): 30.5
- Image size: train = 256 x 256, test = 320 x 320
- **Papers:**
- ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476
- Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385
- Bag of Tricks for Image Classification with Convolutional Neural Networks: https://arxiv.org/abs/1812.01187
- **Original:** https://github.com/huggingface/pytorch-image-models
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('resnet152d.ra2_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'resnet152d.ra2_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 64, 128, 128])
# torch.Size([1, 256, 64, 64])
# torch.Size([1, 512, 32, 32])
# torch.Size([1, 1024, 16, 16])
# torch.Size([1, 2048, 8, 8])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'resnet152d.ra2_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 2048, 8, 8) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
|model |img_size|top1 |top5 |param_count|gmacs|macts|img/sec|
|------------------------------------------|--------|-----|-----|-----------|-----|-----|-------|
|[seresnextaa101d_32x8d.sw_in12k_ft_in1k_288](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k_288)|320 |86.72|98.17|93.6 |35.2 |69.7 |451 |
|[seresnextaa101d_32x8d.sw_in12k_ft_in1k_288](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k_288)|288 |86.51|98.08|93.6 |28.5 |56.4 |560 |
|[seresnextaa101d_32x8d.sw_in12k_ft_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k)|288 |86.49|98.03|93.6 |28.5 |56.4 |557 |
|[seresnextaa101d_32x8d.sw_in12k_ft_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k)|224 |85.96|97.82|93.6 |17.2 |34.2 |923 |
|[resnext101_32x32d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x32d.fb_wsl_ig1b_ft_in1k)|224 |85.11|97.44|468.5 |87.3 |91.1 |254 |
|[resnetrs420.tf_in1k](https://huggingface.co/timm/resnetrs420.tf_in1k)|416 |85.0 |97.12|191.9 |108.4|213.8|134 |
|[ecaresnet269d.ra2_in1k](https://huggingface.co/timm/ecaresnet269d.ra2_in1k)|352 |84.96|97.22|102.1 |50.2 |101.2|291 |
|[ecaresnet269d.ra2_in1k](https://huggingface.co/timm/ecaresnet269d.ra2_in1k)|320 |84.73|97.18|102.1 |41.5 |83.7 |353 |
|[resnetrs350.tf_in1k](https://huggingface.co/timm/resnetrs350.tf_in1k)|384 |84.71|96.99|164.0 |77.6 |154.7|183 |
|[seresnextaa101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.ah_in1k)|288 |84.57|97.08|93.6 |28.5 |56.4 |557 |
|[resnetrs200.tf_in1k](https://huggingface.co/timm/resnetrs200.tf_in1k)|320 |84.45|97.08|93.2 |31.5 |67.8 |446 |
|[resnetrs270.tf_in1k](https://huggingface.co/timm/resnetrs270.tf_in1k)|352 |84.43|96.97|129.9 |51.1 |105.5|280 |
|[seresnext101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101d_32x8d.ah_in1k)|288 |84.36|96.92|93.6 |27.6 |53.0 |595 |
|[seresnet152d.ra2_in1k](https://huggingface.co/timm/seresnet152d.ra2_in1k)|320 |84.35|97.04|66.8 |24.1 |47.7 |610 |
|[resnetrs350.tf_in1k](https://huggingface.co/timm/resnetrs350.tf_in1k)|288 |84.3 |96.94|164.0 |43.7 |87.1 |333 |
|[resnext101_32x8d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_swsl_ig1b_ft_in1k)|224 |84.28|97.17|88.8 |16.5 |31.2 |1100 |
|[resnetrs420.tf_in1k](https://huggingface.co/timm/resnetrs420.tf_in1k)|320 |84.24|96.86|191.9 |64.2 |126.6|228 |
|[seresnext101_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101_32x8d.ah_in1k)|288 |84.19|96.87|93.6 |27.2 |51.6 |613 |
|[resnext101_32x16d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_wsl_ig1b_ft_in1k)|224 |84.18|97.19|194.0 |36.3 |51.2 |581 |
|[resnetaa101d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa101d.sw_in12k_ft_in1k)|288 |84.11|97.11|44.6 |15.1 |29.0 |1144 |
|[resnet200d.ra2_in1k](https://huggingface.co/timm/resnet200d.ra2_in1k)|320 |83.97|96.82|64.7 |31.2 |67.3 |518 |
|[resnetrs200.tf_in1k](https://huggingface.co/timm/resnetrs200.tf_in1k)|256 |83.87|96.75|93.2 |20.2 |43.4 |692 |
|[seresnextaa101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.ah_in1k)|224 |83.86|96.65|93.6 |17.2 |34.2 |923 |
|[resnetrs152.tf_in1k](https://huggingface.co/timm/resnetrs152.tf_in1k)|320 |83.72|96.61|86.6 |24.3 |48.1 |617 |
|[seresnet152d.ra2_in1k](https://huggingface.co/timm/seresnet152d.ra2_in1k)|256 |83.69|96.78|66.8 |15.4 |30.6 |943 |
|[seresnext101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101d_32x8d.ah_in1k)|224 |83.68|96.61|93.6 |16.7 |32.0 |986 |
|[resnet152d.ra2_in1k](https://huggingface.co/timm/resnet152d.ra2_in1k)|320 |83.67|96.74|60.2 |24.1 |47.7 |706 |
|[resnetrs270.tf_in1k](https://huggingface.co/timm/resnetrs270.tf_in1k)|256 |83.59|96.61|129.9 |27.1 |55.8 |526 |
|[seresnext101_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101_32x8d.ah_in1k)|224 |83.58|96.4 |93.6 |16.5 |31.2 |1013 |
|[resnetaa101d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa101d.sw_in12k_ft_in1k)|224 |83.54|96.83|44.6 |9.1 |17.6 |1864 |
|[resnet152.a1h_in1k](https://huggingface.co/timm/resnet152.a1h_in1k)|288 |83.46|96.54|60.2 |19.1 |37.3 |904 |
|[resnext101_32x16d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_swsl_ig1b_ft_in1k)|224 |83.35|96.85|194.0 |36.3 |51.2 |582 |
|[resnet200d.ra2_in1k](https://huggingface.co/timm/resnet200d.ra2_in1k)|256 |83.23|96.53|64.7 |20.0 |43.1 |809 |
|[resnext101_32x4d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x4d.fb_swsl_ig1b_ft_in1k)|224 |83.22|96.75|44.2 |8.0 |21.2 |1814 |
|[resnext101_64x4d.c1_in1k](https://huggingface.co/timm/resnext101_64x4d.c1_in1k)|288 |83.16|96.38|83.5 |25.7 |51.6 |590 |
|[resnet152d.ra2_in1k](https://huggingface.co/timm/resnet152d.ra2_in1k)|256 |83.14|96.38|60.2 |15.4 |30.5 |1096 |
|[resnet101d.ra2_in1k](https://huggingface.co/timm/resnet101d.ra2_in1k)|320 |83.02|96.45|44.6 |16.5 |34.8 |992 |
|[ecaresnet101d.miil_in1k](https://huggingface.co/timm/ecaresnet101d.miil_in1k)|288 |82.98|96.54|44.6 |13.4 |28.2 |1077 |
|[resnext101_64x4d.tv_in1k](https://huggingface.co/timm/resnext101_64x4d.tv_in1k)|224 |82.98|96.25|83.5 |15.5 |31.2 |989 |
|[resnetrs152.tf_in1k](https://huggingface.co/timm/resnetrs152.tf_in1k)|256 |82.86|96.28|86.6 |15.6 |30.8 |951 |
|[resnext101_32x8d.tv2_in1k](https://huggingface.co/timm/resnext101_32x8d.tv2_in1k)|224 |82.83|96.22|88.8 |16.5 |31.2 |1099 |
|[resnet152.a1h_in1k](https://huggingface.co/timm/resnet152.a1h_in1k)|224 |82.8 |96.13|60.2 |11.6 |22.6 |1486 |
|[resnet101.a1h_in1k](https://huggingface.co/timm/resnet101.a1h_in1k)|288 |82.8 |96.32|44.6 |13.0 |26.8 |1291 |
|[resnet152.a1_in1k](https://huggingface.co/timm/resnet152.a1_in1k)|288 |82.74|95.71|60.2 |19.1 |37.3 |905 |
|[resnext101_32x8d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_wsl_ig1b_ft_in1k)|224 |82.69|96.63|88.8 |16.5 |31.2 |1100 |
|[resnet152.a2_in1k](https://huggingface.co/timm/resnet152.a2_in1k)|288 |82.62|95.75|60.2 |19.1 |37.3 |904 |
|[resnetaa50d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa50d.sw_in12k_ft_in1k)|288 |82.61|96.49|25.6 |8.9 |20.6 |1729 |
|[resnet61q.ra2_in1k](https://huggingface.co/timm/resnet61q.ra2_in1k)|288 |82.53|96.13|36.8 |9.9 |21.5 |1773 |
|[wide_resnet101_2.tv2_in1k](https://huggingface.co/timm/wide_resnet101_2.tv2_in1k)|224 |82.5 |96.02|126.9 |22.8 |21.2 |1078 |
|[resnext101_64x4d.c1_in1k](https://huggingface.co/timm/resnext101_64x4d.c1_in1k)|224 |82.46|95.92|83.5 |15.5 |31.2 |987 |
|[resnet51q.ra2_in1k](https://huggingface.co/timm/resnet51q.ra2_in1k)|288 |82.36|96.18|35.7 |8.1 |20.9 |1964 |
|[ecaresnet50t.ra2_in1k](https://huggingface.co/timm/ecaresnet50t.ra2_in1k)|320 |82.35|96.14|25.6 |8.8 |24.1 |1386 |
|[resnet101.a1_in1k](https://huggingface.co/timm/resnet101.a1_in1k)|288 |82.31|95.63|44.6 |13.0 |26.8 |1291 |
|[resnetrs101.tf_in1k](https://huggingface.co/timm/resnetrs101.tf_in1k)|288 |82.29|96.01|63.6 |13.6 |28.5 |1078 |
|[resnet152.tv2_in1k](https://huggingface.co/timm/resnet152.tv2_in1k)|224 |82.29|96.0 |60.2 |11.6 |22.6 |1484 |
|[wide_resnet50_2.racm_in1k](https://huggingface.co/timm/wide_resnet50_2.racm_in1k)|288 |82.27|96.06|68.9 |18.9 |23.8 |1176 |
|[resnet101d.ra2_in1k](https://huggingface.co/timm/resnet101d.ra2_in1k)|256 |82.26|96.07|44.6 |10.6 |22.2 |1542 |
|[resnet101.a2_in1k](https://huggingface.co/timm/resnet101.a2_in1k)|288 |82.24|95.73|44.6 |13.0 |26.8 |1290 |
|[seresnext50_32x4d.racm_in1k](https://huggingface.co/timm/seresnext50_32x4d.racm_in1k)|288 |82.2 |96.14|27.6 |7.0 |23.8 |1547 |
|[ecaresnet101d.miil_in1k](https://huggingface.co/timm/ecaresnet101d.miil_in1k)|224 |82.18|96.05|44.6 |8.1 |17.1 |1771 |
|[resnext50_32x4d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext50_32x4d.fb_swsl_ig1b_ft_in1k)|224 |82.17|96.22|25.0 |4.3 |14.4 |2943 |
|[ecaresnet50t.a1_in1k](https://huggingface.co/timm/ecaresnet50t.a1_in1k)|288 |82.12|95.65|25.6 |7.1 |19.6 |1704 |
|[resnext50_32x4d.a1h_in1k](https://huggingface.co/timm/resnext50_32x4d.a1h_in1k)|288 |82.03|95.94|25.0 |7.0 |23.8 |1745 |
|[ecaresnet101d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet101d_pruned.miil_in1k)|288 |82.0 |96.15|24.9 |5.8 |12.7 |1787 |
|[resnet61q.ra2_in1k](https://huggingface.co/timm/resnet61q.ra2_in1k)|256 |81.99|95.85|36.8 |7.8 |17.0 |2230 |
|[resnext101_32x8d.tv2_in1k](https://huggingface.co/timm/resnext101_32x8d.tv2_in1k)|176 |81.98|95.72|88.8 |10.3 |19.4 |1768 |
|[resnet152.a1_in1k](https://huggingface.co/timm/resnet152.a1_in1k)|224 |81.97|95.24|60.2 |11.6 |22.6 |1486 |
|[resnet101.a1h_in1k](https://huggingface.co/timm/resnet101.a1h_in1k)|224 |81.93|95.75|44.6 |7.8 |16.2 |2122 |
|[resnet101.tv2_in1k](https://huggingface.co/timm/resnet101.tv2_in1k)|224 |81.9 |95.77|44.6 |7.8 |16.2 |2118 |
|[resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k)|224 |81.84|96.1 |194.0 |36.3 |51.2 |583 |
|[resnet51q.ra2_in1k](https://huggingface.co/timm/resnet51q.ra2_in1k)|256 |81.78|95.94|35.7 |6.4 |16.6 |2471 |
|[resnet152.a2_in1k](https://huggingface.co/timm/resnet152.a2_in1k)|224 |81.77|95.22|60.2 |11.6 |22.6 |1485 |
|[resnetaa50d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa50d.sw_in12k_ft_in1k)|224 |81.74|96.06|25.6 |5.4 |12.4 |2813 |
|[ecaresnet50t.a2_in1k](https://huggingface.co/timm/ecaresnet50t.a2_in1k)|288 |81.65|95.54|25.6 |7.1 |19.6 |1703 |
|[ecaresnet50d.miil_in1k](https://huggingface.co/timm/ecaresnet50d.miil_in1k)|288 |81.64|95.88|25.6 |7.2 |19.7 |1694 |
|[resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k)|224 |81.62|96.04|88.8 |16.5 |31.2 |1101 |
|[wide_resnet50_2.tv2_in1k](https://huggingface.co/timm/wide_resnet50_2.tv2_in1k)|224 |81.61|95.76|68.9 |11.4 |14.4 |1930 |
|[resnetaa50.a1h_in1k](https://huggingface.co/timm/resnetaa50.a1h_in1k)|288 |81.61|95.83|25.6 |8.5 |19.2 |1868 |
|[resnet101.a1_in1k](https://huggingface.co/timm/resnet101.a1_in1k)|224 |81.5 |95.16|44.6 |7.8 |16.2 |2125 |
|[resnext50_32x4d.a1_in1k](https://huggingface.co/timm/resnext50_32x4d.a1_in1k)|288 |81.48|95.16|25.0 |7.0 |23.8 |1745 |
|[gcresnet50t.ra2_in1k](https://huggingface.co/timm/gcresnet50t.ra2_in1k)|288 |81.47|95.71|25.9 |6.9 |18.6 |2071 |
|[wide_resnet50_2.racm_in1k](https://huggingface.co/timm/wide_resnet50_2.racm_in1k)|224 |81.45|95.53|68.9 |11.4 |14.4 |1929 |
|[resnet50d.a1_in1k](https://huggingface.co/timm/resnet50d.a1_in1k)|288 |81.44|95.22|25.6 |7.2 |19.7 |1908 |
|[ecaresnet50t.ra2_in1k](https://huggingface.co/timm/ecaresnet50t.ra2_in1k)|256 |81.44|95.67|25.6 |5.6 |15.4 |2168 |
|[ecaresnetlight.miil_in1k](https://huggingface.co/timm/ecaresnetlight.miil_in1k)|288 |81.4 |95.82|30.2 |6.8 |13.9 |2132 |
|[resnet50d.ra2_in1k](https://huggingface.co/timm/resnet50d.ra2_in1k)|288 |81.37|95.74|25.6 |7.2 |19.7 |1910 |
|[resnet101.a2_in1k](https://huggingface.co/timm/resnet101.a2_in1k)|224 |81.32|95.19|44.6 |7.8 |16.2 |2125 |
|[seresnet50.ra2_in1k](https://huggingface.co/timm/seresnet50.ra2_in1k)|288 |81.3 |95.65|28.1 |6.8 |18.4 |1803 |
|[resnext50_32x4d.a2_in1k](https://huggingface.co/timm/resnext50_32x4d.a2_in1k)|288 |81.3 |95.11|25.0 |7.0 |23.8 |1746 |
|[seresnext50_32x4d.racm_in1k](https://huggingface.co/timm/seresnext50_32x4d.racm_in1k)|224 |81.27|95.62|27.6 |4.3 |14.4 |2591 |
|[ecaresnet50t.a1_in1k](https://huggingface.co/timm/ecaresnet50t.a1_in1k)|224 |81.26|95.16|25.6 |4.3 |11.8 |2823 |
|[gcresnext50ts.ch_in1k](https://huggingface.co/timm/gcresnext50ts.ch_in1k)|288 |81.23|95.54|15.7 |4.8 |19.6 |2117 |
|[senet154.gluon_in1k](https://huggingface.co/timm/senet154.gluon_in1k)|224 |81.23|95.35|115.1 |20.8 |38.7 |545 |
|[resnet50.a1_in1k](https://huggingface.co/timm/resnet50.a1_in1k)|288 |81.22|95.11|25.6 |6.8 |18.4 |2089 |
|[resnet50_gn.a1h_in1k](https://huggingface.co/timm/resnet50_gn.a1h_in1k)|288 |81.22|95.63|25.6 |6.8 |18.4 |676 |
|[resnet50d.a2_in1k](https://huggingface.co/timm/resnet50d.a2_in1k)|288 |81.18|95.09|25.6 |7.2 |19.7 |1908 |
|[resnet50.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnet50.fb_swsl_ig1b_ft_in1k)|224 |81.18|95.98|25.6 |4.1 |11.1 |3455 |
|[resnext50_32x4d.tv2_in1k](https://huggingface.co/timm/resnext50_32x4d.tv2_in1k)|224 |81.17|95.34|25.0 |4.3 |14.4 |2933 |
|[resnext50_32x4d.a1h_in1k](https://huggingface.co/timm/resnext50_32x4d.a1h_in1k)|224 |81.1 |95.33|25.0 |4.3 |14.4 |2934 |
|[seresnet50.a2_in1k](https://huggingface.co/timm/seresnet50.a2_in1k)|288 |81.1 |95.23|28.1 |6.8 |18.4 |1801 |
|[seresnet50.a1_in1k](https://huggingface.co/timm/seresnet50.a1_in1k)|288 |81.1 |95.12|28.1 |6.8 |18.4 |1799 |
|[resnet152s.gluon_in1k](https://huggingface.co/timm/resnet152s.gluon_in1k)|224 |81.02|95.41|60.3 |12.9 |25.0 |1347 |
|[resnet50.d_in1k](https://huggingface.co/timm/resnet50.d_in1k)|288 |80.97|95.44|25.6 |6.8 |18.4 |2085 |
|[gcresnet50t.ra2_in1k](https://huggingface.co/timm/gcresnet50t.ra2_in1k)|256 |80.94|95.45|25.9 |5.4 |14.7 |2571 |
|[resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k)|224 |80.93|95.73|44.2 |8.0 |21.2 |1814 |
|[resnet50.c1_in1k](https://huggingface.co/timm/resnet50.c1_in1k)|288 |80.91|95.55|25.6 |6.8 |18.4 |2084 |
|[seresnext101_32x4d.gluon_in1k](https://huggingface.co/timm/seresnext101_32x4d.gluon_in1k)|224 |80.9 |95.31|49.0 |8.0 |21.3 |1585 |
|[seresnext101_64x4d.gluon_in1k](https://huggingface.co/timm/seresnext101_64x4d.gluon_in1k)|224 |80.9 |95.3 |88.2 |15.5 |31.2 |918 |
|[resnet50.c2_in1k](https://huggingface.co/timm/resnet50.c2_in1k)|288 |80.86|95.52|25.6 |6.8 |18.4 |2085 |
|[resnet50.tv2_in1k](https://huggingface.co/timm/resnet50.tv2_in1k)|224 |80.85|95.43|25.6 |4.1 |11.1 |3450 |
|[ecaresnet50t.a2_in1k](https://huggingface.co/timm/ecaresnet50t.a2_in1k)|224 |80.84|95.02|25.6 |4.3 |11.8 |2821 |
|[ecaresnet101d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet101d_pruned.miil_in1k)|224 |80.79|95.62|24.9 |3.5 |7.7 |2961 |
|[seresnet33ts.ra2_in1k](https://huggingface.co/timm/seresnet33ts.ra2_in1k)|288 |80.79|95.36|19.8 |6.0 |14.8 |2506 |
|[ecaresnet50d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet50d_pruned.miil_in1k)|288 |80.79|95.58|19.9 |4.2 |10.6 |2349 |
|[resnet50.a2_in1k](https://huggingface.co/timm/resnet50.a2_in1k)|288 |80.78|94.99|25.6 |6.8 |18.4 |2088 |
|[resnet50.b1k_in1k](https://huggingface.co/timm/resnet50.b1k_in1k)|288 |80.71|95.43|25.6 |6.8 |18.4 |2087 |
|[resnext50_32x4d.ra_in1k](https://huggingface.co/timm/resnext50_32x4d.ra_in1k)|288 |80.7 |95.39|25.0 |7.0 |23.8 |1749 |
|[resnetrs101.tf_in1k](https://huggingface.co/timm/resnetrs101.tf_in1k)|192 |80.69|95.24|63.6 |6.0 |12.7 |2270 |
|[resnet50d.a1_in1k](https://huggingface.co/timm/resnet50d.a1_in1k)|224 |80.68|94.71|25.6 |4.4 |11.9 |3162 |
|[eca_resnet33ts.ra2_in1k](https://huggingface.co/timm/eca_resnet33ts.ra2_in1k)|288 |80.68|95.36|19.7 |6.0 |14.8 |2637 |
|[resnet50.a1h_in1k](https://huggingface.co/timm/resnet50.a1h_in1k)|224 |80.67|95.3 |25.6 |4.1 |11.1 |3452 |
|[resnext50d_32x4d.bt_in1k](https://huggingface.co/timm/resnext50d_32x4d.bt_in1k)|288 |80.67|95.42|25.0 |7.4 |25.1 |1626 |
|[resnetaa50.a1h_in1k](https://huggingface.co/timm/resnetaa50.a1h_in1k)|224 |80.63|95.21|25.6 |5.2 |11.6 |3034 |
|[ecaresnet50d.miil_in1k](https://huggingface.co/timm/ecaresnet50d.miil_in1k)|224 |80.61|95.32|25.6 |4.4 |11.9 |2813 |
|[resnext101_64x4d.gluon_in1k](https://huggingface.co/timm/resnext101_64x4d.gluon_in1k)|224 |80.61|94.99|83.5 |15.5 |31.2 |989 |
|[gcresnet33ts.ra2_in1k](https://huggingface.co/timm/gcresnet33ts.ra2_in1k)|288 |80.6 |95.31|19.9 |6.0 |14.8 |2578 |
|[gcresnext50ts.ch_in1k](https://huggingface.co/timm/gcresnext50ts.ch_in1k)|256 |80.57|95.17|15.7 |3.8 |15.5 |2710 |
|[resnet152.a3_in1k](https://huggingface.co/timm/resnet152.a3_in1k)|224 |80.56|95.0 |60.2 |11.6 |22.6 |1483 |
|[resnet50d.ra2_in1k](https://huggingface.co/timm/resnet50d.ra2_in1k)|224 |80.53|95.16|25.6 |4.4 |11.9 |3164 |
|[resnext50_32x4d.a1_in1k](https://huggingface.co/timm/resnext50_32x4d.a1_in1k)|224 |80.53|94.46|25.0 |4.3 |14.4 |2930 |
|[wide_resnet101_2.tv2_in1k](https://huggingface.co/timm/wide_resnet101_2.tv2_in1k)|176 |80.48|94.98|126.9 |14.3 |13.2 |1719 |
|[resnet152d.gluon_in1k](https://huggingface.co/timm/resnet152d.gluon_in1k)|224 |80.47|95.2 |60.2 |11.8 |23.4 |1428 |
|[resnet50.b2k_in1k](https://huggingface.co/timm/resnet50.b2k_in1k)|288 |80.45|95.32|25.6 |6.8 |18.4 |2086 |
|[ecaresnetlight.miil_in1k](https://huggingface.co/timm/ecaresnetlight.miil_in1k)|224 |80.45|95.24|30.2 |4.1 |8.4 |3530 |
|[resnext50_32x4d.a2_in1k](https://huggingface.co/timm/resnext50_32x4d.a2_in1k)|224 |80.45|94.63|25.0 |4.3 |14.4 |2936 |
|[wide_resnet50_2.tv2_in1k](https://huggingface.co/timm/wide_resnet50_2.tv2_in1k)|176 |80.43|95.09|68.9 |7.3 |9.0 |3015 |
|[resnet101d.gluon_in1k](https://huggingface.co/timm/resnet101d.gluon_in1k)|224 |80.42|95.01|44.6 |8.1 |17.0 |2007 |
|[resnet50.a1_in1k](https://huggingface.co/timm/resnet50.a1_in1k)|224 |80.38|94.6 |25.6 |4.1 |11.1 |3461 |
|[seresnet33ts.ra2_in1k](https://huggingface.co/timm/seresnet33ts.ra2_in1k)|256 |80.36|95.1 |19.8 |4.8 |11.7 |3267 |
|[resnext101_32x4d.gluon_in1k](https://huggingface.co/timm/resnext101_32x4d.gluon_in1k)|224 |80.34|94.93|44.2 |8.0 |21.2 |1814 |
|[resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k)|224 |80.32|95.4 |25.0 |4.3 |14.4 |2941 |
|[resnet101s.gluon_in1k](https://huggingface.co/timm/resnet101s.gluon_in1k)|224 |80.28|95.16|44.7 |9.2 |18.6 |1851 |
|[seresnet50.ra2_in1k](https://huggingface.co/timm/seresnet50.ra2_in1k)|224 |80.26|95.08|28.1 |4.1 |11.1 |2972 |
|[resnetblur50.bt_in1k](https://huggingface.co/timm/resnetblur50.bt_in1k)|288 |80.24|95.24|25.6 |8.5 |19.9 |1523 |
|[resnet50d.a2_in1k](https://huggingface.co/timm/resnet50d.a2_in1k)|224 |80.22|94.63|25.6 |4.4 |11.9 |3162 |
|[resnet152.tv2_in1k](https://huggingface.co/timm/resnet152.tv2_in1k)|176 |80.2 |94.64|60.2 |7.2 |14.0 |2346 |
|[seresnet50.a2_in1k](https://huggingface.co/timm/seresnet50.a2_in1k)|224 |80.08|94.74|28.1 |4.1 |11.1 |2969 |
|[eca_resnet33ts.ra2_in1k](https://huggingface.co/timm/eca_resnet33ts.ra2_in1k)|256 |80.08|94.97|19.7 |4.8 |11.7 |3284 |
|[gcresnet33ts.ra2_in1k](https://huggingface.co/timm/gcresnet33ts.ra2_in1k)|256 |80.06|94.99|19.9 |4.8 |11.7 |3216 |
|[resnet50_gn.a1h_in1k](https://huggingface.co/timm/resnet50_gn.a1h_in1k)|224 |80.06|94.95|25.6 |4.1 |11.1 |1109 |
|[seresnet50.a1_in1k](https://huggingface.co/timm/seresnet50.a1_in1k)|224 |80.02|94.71|28.1 |4.1 |11.1 |2962 |
|[resnet50.ram_in1k](https://huggingface.co/timm/resnet50.ram_in1k)|288 |79.97|95.05|25.6 |6.8 |18.4 |2086 |
|[resnet152c.gluon_in1k](https://huggingface.co/timm/resnet152c.gluon_in1k)|224 |79.92|94.84|60.2 |11.8 |23.4 |1455 |
|[seresnext50_32x4d.gluon_in1k](https://huggingface.co/timm/seresnext50_32x4d.gluon_in1k)|224 |79.91|94.82|27.6 |4.3 |14.4 |2591 |
|[resnet50.d_in1k](https://huggingface.co/timm/resnet50.d_in1k)|224 |79.91|94.67|25.6 |4.1 |11.1 |3456 |
|[resnet101.tv2_in1k](https://huggingface.co/timm/resnet101.tv2_in1k)|176 |79.9 |94.6 |44.6 |4.9 |10.1 |3341 |
|[resnetrs50.tf_in1k](https://huggingface.co/timm/resnetrs50.tf_in1k)|224 |79.89|94.97|35.7 |4.5 |12.1 |2774 |
|[resnet50.c2_in1k](https://huggingface.co/timm/resnet50.c2_in1k)|224 |79.88|94.87|25.6 |4.1 |11.1 |3455 |
|[ecaresnet26t.ra2_in1k](https://huggingface.co/timm/ecaresnet26t.ra2_in1k)|320 |79.86|95.07|16.0 |5.2 |16.4 |2168 |
|[resnet50.a2_in1k](https://huggingface.co/timm/resnet50.a2_in1k)|224 |79.85|94.56|25.6 |4.1 |11.1 |3460 |
|[resnet50.ra_in1k](https://huggingface.co/timm/resnet50.ra_in1k)|288 |79.83|94.97|25.6 |6.8 |18.4 |2087 |
|[resnet101.a3_in1k](https://huggingface.co/timm/resnet101.a3_in1k)|224 |79.82|94.62|44.6 |7.8 |16.2 |2114 |
|[resnext50_32x4d.ra_in1k](https://huggingface.co/timm/resnext50_32x4d.ra_in1k)|224 |79.76|94.6 |25.0 |4.3 |14.4 |2943 |
|[resnet50.c1_in1k](https://huggingface.co/timm/resnet50.c1_in1k)|224 |79.74|94.95|25.6 |4.1 |11.1 |3455 |
|[ecaresnet50d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet50d_pruned.miil_in1k)|224 |79.74|94.87|19.9 |2.5 |6.4 |3929 |
|[resnet33ts.ra2_in1k](https://huggingface.co/timm/resnet33ts.ra2_in1k)|288 |79.71|94.83|19.7 |6.0 |14.8 |2710 |
|[resnet152.gluon_in1k](https://huggingface.co/timm/resnet152.gluon_in1k)|224 |79.68|94.74|60.2 |11.6 |22.6 |1486 |
|[resnext50d_32x4d.bt_in1k](https://huggingface.co/timm/resnext50d_32x4d.bt_in1k)|224 |79.67|94.87|25.0 |4.5 |15.2 |2729 |
|[resnet50.bt_in1k](https://huggingface.co/timm/resnet50.bt_in1k)|288 |79.63|94.91|25.6 |6.8 |18.4 |2086 |
|[ecaresnet50t.a3_in1k](https://huggingface.co/timm/ecaresnet50t.a3_in1k)|224 |79.56|94.72|25.6 |4.3 |11.8 |2805 |
|[resnet101c.gluon_in1k](https://huggingface.co/timm/resnet101c.gluon_in1k)|224 |79.53|94.58|44.6 |8.1 |17.0 |2062 |
|[resnet50.b1k_in1k](https://huggingface.co/timm/resnet50.b1k_in1k)|224 |79.52|94.61|25.6 |4.1 |11.1 |3459 |
|[resnet50.tv2_in1k](https://huggingface.co/timm/resnet50.tv2_in1k)|176 |79.42|94.64|25.6 |2.6 |6.9 |5397 |
|[resnet32ts.ra2_in1k](https://huggingface.co/timm/resnet32ts.ra2_in1k)|288 |79.4 |94.66|18.0 |5.9 |14.6 |2752 |
|[resnet50.b2k_in1k](https://huggingface.co/timm/resnet50.b2k_in1k)|224 |79.38|94.57|25.6 |4.1 |11.1 |3459 |
|[resnext50_32x4d.tv2_in1k](https://huggingface.co/timm/resnext50_32x4d.tv2_in1k)|176 |79.37|94.3 |25.0 |2.7 |9.0 |4577 |
|[resnext50_32x4d.gluon_in1k](https://huggingface.co/timm/resnext50_32x4d.gluon_in1k)|224 |79.36|94.43|25.0 |4.3 |14.4 |2942 |
|[resnext101_32x8d.tv_in1k](https://huggingface.co/timm/resnext101_32x8d.tv_in1k)|224 |79.31|94.52|88.8 |16.5 |31.2 |1100 |
|[resnet101.gluon_in1k](https://huggingface.co/timm/resnet101.gluon_in1k)|224 |79.31|94.53|44.6 |7.8 |16.2 |2125 |
|[resnetblur50.bt_in1k](https://huggingface.co/timm/resnetblur50.bt_in1k)|224 |79.31|94.63|25.6 |5.2 |12.0 |2524 |
|[resnet50.a1h_in1k](https://huggingface.co/timm/resnet50.a1h_in1k)|176 |79.27|94.49|25.6 |2.6 |6.9 |5404 |
|[resnext50_32x4d.a3_in1k](https://huggingface.co/timm/resnext50_32x4d.a3_in1k)|224 |79.25|94.31|25.0 |4.3 |14.4 |2931 |
|[resnet50.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnet50.fb_ssl_yfcc100m_ft_in1k)|224 |79.22|94.84|25.6 |4.1 |11.1 |3451 |
|[resnet33ts.ra2_in1k](https://huggingface.co/timm/resnet33ts.ra2_in1k)|256 |79.21|94.56|19.7 |4.8 |11.7 |3392 |
|[resnet50d.gluon_in1k](https://huggingface.co/timm/resnet50d.gluon_in1k)|224 |79.07|94.48|25.6 |4.4 |11.9 |3162 |
|[resnet50.ram_in1k](https://huggingface.co/timm/resnet50.ram_in1k)|224 |79.03|94.38|25.6 |4.1 |11.1 |3453 |
|[resnet50.am_in1k](https://huggingface.co/timm/resnet50.am_in1k)|224 |79.01|94.39|25.6 |4.1 |11.1 |3461 |
|[resnet32ts.ra2_in1k](https://huggingface.co/timm/resnet32ts.ra2_in1k)|256 |79.01|94.37|18.0 |4.6 |11.6 |3440 |
|[ecaresnet26t.ra2_in1k](https://huggingface.co/timm/ecaresnet26t.ra2_in1k)|256 |78.9 |94.54|16.0 |3.4 |10.5 |3421 |
|[resnet152.a3_in1k](https://huggingface.co/timm/resnet152.a3_in1k)|160 |78.89|94.11|60.2 |5.9 |11.5 |2745 |
|[wide_resnet101_2.tv_in1k](https://huggingface.co/timm/wide_resnet101_2.tv_in1k)|224 |78.84|94.28|126.9 |22.8 |21.2 |1079 |
|[seresnext26d_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26d_32x4d.bt_in1k)|288 |78.83|94.24|16.8 |4.5 |16.8 |2251 |
|[resnet50.ra_in1k](https://huggingface.co/timm/resnet50.ra_in1k)|224 |78.81|94.32|25.6 |4.1 |11.1 |3454 |
|[seresnext26t_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26t_32x4d.bt_in1k)|288 |78.74|94.33|16.8 |4.5 |16.7 |2264 |
|[resnet50s.gluon_in1k](https://huggingface.co/timm/resnet50s.gluon_in1k)|224 |78.72|94.23|25.7 |5.5 |13.5 |2796 |
|[resnet50d.a3_in1k](https://huggingface.co/timm/resnet50d.a3_in1k)|224 |78.71|94.24|25.6 |4.4 |11.9 |3154 |
|[wide_resnet50_2.tv_in1k](https://huggingface.co/timm/wide_resnet50_2.tv_in1k)|224 |78.47|94.09|68.9 |11.4 |14.4 |1934 |
|[resnet50.bt_in1k](https://huggingface.co/timm/resnet50.bt_in1k)|224 |78.46|94.27|25.6 |4.1 |11.1 |3454 |
|[resnet34d.ra2_in1k](https://huggingface.co/timm/resnet34d.ra2_in1k)|288 |78.43|94.35|21.8 |6.5 |7.5 |3291 |
|[gcresnext26ts.ch_in1k](https://huggingface.co/timm/gcresnext26ts.ch_in1k)|288 |78.42|94.04|10.5 |3.1 |13.3 |3226 |
|[resnet26t.ra2_in1k](https://huggingface.co/timm/resnet26t.ra2_in1k)|320 |78.33|94.13|16.0 |5.2 |16.4 |2391 |
|[resnet152.tv_in1k](https://huggingface.co/timm/resnet152.tv_in1k)|224 |78.32|94.04|60.2 |11.6 |22.6 |1487 |
|[seresnext26ts.ch_in1k](https://huggingface.co/timm/seresnext26ts.ch_in1k)|288 |78.28|94.1 |10.4 |3.1 |13.3 |3062 |
|[bat_resnext26ts.ch_in1k](https://huggingface.co/timm/bat_resnext26ts.ch_in1k)|256 |78.25|94.1 |10.7 |2.5 |12.5 |3393 |
|[resnet50.a3_in1k](https://huggingface.co/timm/resnet50.a3_in1k)|224 |78.06|93.78|25.6 |4.1 |11.1 |3450 |
|[resnet50c.gluon_in1k](https://huggingface.co/timm/resnet50c.gluon_in1k)|224 |78.0 |93.99|25.6 |4.4 |11.9 |3286 |
|[eca_resnext26ts.ch_in1k](https://huggingface.co/timm/eca_resnext26ts.ch_in1k)|288 |78.0 |93.91|10.3 |3.1 |13.3 |3297 |
|[seresnext26t_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26t_32x4d.bt_in1k)|224 |77.98|93.75|16.8 |2.7 |10.1 |3841 |
|[resnet34.a1_in1k](https://huggingface.co/timm/resnet34.a1_in1k)|288 |77.92|93.77|21.8 |6.1 |6.2 |3609 |
|[resnet101.a3_in1k](https://huggingface.co/timm/resnet101.a3_in1k)|160 |77.88|93.71|44.6 |4.0 |8.3 |3926 |
|[resnet26t.ra2_in1k](https://huggingface.co/timm/resnet26t.ra2_in1k)|256 |77.87|93.84|16.0 |3.4 |10.5 |3772 |
|[seresnext26ts.ch_in1k](https://huggingface.co/timm/seresnext26ts.ch_in1k)|256 |77.86|93.79|10.4 |2.4 |10.5 |4263 |
|[resnetrs50.tf_in1k](https://huggingface.co/timm/resnetrs50.tf_in1k)|160 |77.82|93.81|35.7 |2.3 |6.2 |5238 |
|[gcresnext26ts.ch_in1k](https://huggingface.co/timm/gcresnext26ts.ch_in1k)|256 |77.81|93.82|10.5 |2.4 |10.5 |4183 |
|[ecaresnet50t.a3_in1k](https://huggingface.co/timm/ecaresnet50t.a3_in1k)|160 |77.79|93.6 |25.6 |2.2 |6.0 |5329 |
|[resnext50_32x4d.a3_in1k](https://huggingface.co/timm/resnext50_32x4d.a3_in1k)|160 |77.73|93.32|25.0 |2.2 |7.4 |5576 |
|[resnext50_32x4d.tv_in1k](https://huggingface.co/timm/resnext50_32x4d.tv_in1k)|224 |77.61|93.7 |25.0 |4.3 |14.4 |2944 |
|[seresnext26d_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26d_32x4d.bt_in1k)|224 |77.59|93.61|16.8 |2.7 |10.2 |3807 |
|[resnet50.gluon_in1k](https://huggingface.co/timm/resnet50.gluon_in1k)|224 |77.58|93.72|25.6 |4.1 |11.1 |3455 |
|[eca_resnext26ts.ch_in1k](https://huggingface.co/timm/eca_resnext26ts.ch_in1k)|256 |77.44|93.56|10.3 |2.4 |10.5 |4284 |
|[resnet26d.bt_in1k](https://huggingface.co/timm/resnet26d.bt_in1k)|288 |77.41|93.63|16.0 |4.3 |13.5 |2907 |
|[resnet101.tv_in1k](https://huggingface.co/timm/resnet101.tv_in1k)|224 |77.38|93.54|44.6 |7.8 |16.2 |2125 |
|[resnet50d.a3_in1k](https://huggingface.co/timm/resnet50d.a3_in1k)|160 |77.22|93.27|25.6 |2.2 |6.1 |5982 |
|[resnext26ts.ra2_in1k](https://huggingface.co/timm/resnext26ts.ra2_in1k)|288 |77.17|93.47|10.3 |3.1 |13.3 |3392 |
|[resnet34.a2_in1k](https://huggingface.co/timm/resnet34.a2_in1k)|288 |77.15|93.27|21.8 |6.1 |6.2 |3615 |
|[resnet34d.ra2_in1k](https://huggingface.co/timm/resnet34d.ra2_in1k)|224 |77.1 |93.37|21.8 |3.9 |4.5 |5436 |
|[seresnet50.a3_in1k](https://huggingface.co/timm/seresnet50.a3_in1k)|224 |77.02|93.07|28.1 |4.1 |11.1 |2952 |
|[resnext26ts.ra2_in1k](https://huggingface.co/timm/resnext26ts.ra2_in1k)|256 |76.78|93.13|10.3 |2.4 |10.5 |4410 |
|[resnet26d.bt_in1k](https://huggingface.co/timm/resnet26d.bt_in1k)|224 |76.7 |93.17|16.0 |2.6 |8.2 |4859 |
|[resnet34.bt_in1k](https://huggingface.co/timm/resnet34.bt_in1k)|288 |76.5 |93.35|21.8 |6.1 |6.2 |3617 |
|[resnet34.a1_in1k](https://huggingface.co/timm/resnet34.a1_in1k)|224 |76.42|92.87|21.8 |3.7 |3.7 |5984 |
|[resnet26.bt_in1k](https://huggingface.co/timm/resnet26.bt_in1k)|288 |76.35|93.18|16.0 |3.9 |12.2 |3331 |
|[resnet50.tv_in1k](https://huggingface.co/timm/resnet50.tv_in1k)|224 |76.13|92.86|25.6 |4.1 |11.1 |3457 |
|[resnet50.a3_in1k](https://huggingface.co/timm/resnet50.a3_in1k)|160 |75.96|92.5 |25.6 |2.1 |5.7 |6490 |
|[resnet34.a2_in1k](https://huggingface.co/timm/resnet34.a2_in1k)|224 |75.52|92.44|21.8 |3.7 |3.7 |5991 |
|[resnet26.bt_in1k](https://huggingface.co/timm/resnet26.bt_in1k)|224 |75.3 |92.58|16.0 |2.4 |7.4 |5583 |
|[resnet34.bt_in1k](https://huggingface.co/timm/resnet34.bt_in1k)|224 |75.16|92.18|21.8 |3.7 |3.7 |5994 |
|[seresnet50.a3_in1k](https://huggingface.co/timm/seresnet50.a3_in1k)|160 |75.1 |92.08|28.1 |2.1 |5.7 |5513 |
|[resnet34.gluon_in1k](https://huggingface.co/timm/resnet34.gluon_in1k)|224 |74.57|91.98|21.8 |3.7 |3.7 |5984 |
|[resnet18d.ra2_in1k](https://huggingface.co/timm/resnet18d.ra2_in1k)|288 |73.81|91.83|11.7 |3.4 |5.4 |5196 |
|[resnet34.tv_in1k](https://huggingface.co/timm/resnet34.tv_in1k)|224 |73.32|91.42|21.8 |3.7 |3.7 |5979 |
|[resnet18.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnet18.fb_swsl_ig1b_ft_in1k)|224 |73.28|91.73|11.7 |1.8 |2.5 |10213 |
|[resnet18.a1_in1k](https://huggingface.co/timm/resnet18.a1_in1k)|288 |73.16|91.03|11.7 |3.0 |4.1 |6050 |
|[resnet34.a3_in1k](https://huggingface.co/timm/resnet34.a3_in1k)|224 |72.98|91.11|21.8 |3.7 |3.7 |5967 |
|[resnet18.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnet18.fb_ssl_yfcc100m_ft_in1k)|224 |72.6 |91.42|11.7 |1.8 |2.5 |10213 |
|[resnet18.a2_in1k](https://huggingface.co/timm/resnet18.a2_in1k)|288 |72.37|90.59|11.7 |3.0 |4.1 |6051 |
|[resnet14t.c3_in1k](https://huggingface.co/timm/resnet14t.c3_in1k)|224 |72.26|90.31|10.1 |1.7 |5.8 |7026 |
|[resnet18d.ra2_in1k](https://huggingface.co/timm/resnet18d.ra2_in1k)|224 |72.26|90.68|11.7 |2.1 |3.3 |8707 |
|[resnet18.a1_in1k](https://huggingface.co/timm/resnet18.a1_in1k)|224 |71.49|90.07|11.7 |1.8 |2.5 |10187 |
|[resnet14t.c3_in1k](https://huggingface.co/timm/resnet14t.c3_in1k)|176 |71.31|89.69|10.1 |1.1 |3.6 |10970 |
|[resnet18.gluon_in1k](https://huggingface.co/timm/resnet18.gluon_in1k)|224 |70.84|89.76|11.7 |1.8 |2.5 |10210 |
|[resnet18.a2_in1k](https://huggingface.co/timm/resnet18.a2_in1k)|224 |70.64|89.47|11.7 |1.8 |2.5 |10194 |
|[resnet34.a3_in1k](https://huggingface.co/timm/resnet34.a3_in1k)|160 |70.56|89.52|21.8 |1.9 |1.9 |10737 |
|[resnet18.tv_in1k](https://huggingface.co/timm/resnet18.tv_in1k)|224 |69.76|89.07|11.7 |1.8 |2.5 |10205 |
|[resnet10t.c3_in1k](https://huggingface.co/timm/resnet10t.c3_in1k)|224 |68.34|88.03|5.4 |1.1 |2.4 |13079 |
|[resnet18.a3_in1k](https://huggingface.co/timm/resnet18.a3_in1k)|224 |68.25|88.17|11.7 |1.8 |2.5 |10167 |
|[resnet10t.c3_in1k](https://huggingface.co/timm/resnet10t.c3_in1k)|176 |66.71|86.96|5.4 |0.7 |1.5 |20327 |
|[resnet18.a3_in1k](https://huggingface.co/timm/resnet18.a3_in1k)|160 |65.66|86.26|11.7 |0.9 |1.3 |18229 |
## Citation
```bibtex
@inproceedings{wightman2021resnet,
title={ResNet strikes back: An improved training procedure in timm},
author={Wightman, Ross and Touvron, Hugo and Jegou, Herve},
booktitle={NeurIPS 2021 Workshop on ImageNet: Past, Present, and Future}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
```bibtex
@article{He2015,
author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
title = {Deep Residual Learning for Image Recognition},
journal = {arXiv preprint arXiv:1512.03385},
year = {2015}
}
```
```bibtex
@article{He2018BagOT,
title={Bag of Tricks for Image Classification with Convolutional Neural Networks},
author={Tong He and Zhi Zhang and Hang Zhang and Zhongyue Zhang and Junyuan Xie and Mu Li},
journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2018},
pages={558-567}
}
```
|
botcon/LUKE-real-finetuned-squad
|
botcon
| 2023-10-27T12:01:48Z | 362 | 0 |
transformers
|
[
"transformers",
"pytorch",
"luke",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-10-24T17:39:58Z |
Entry not found
|
tonyassi/camera-lens-focal-length
|
tonyassi
| 2024-02-26T16:46:28Z | 362 | 3 |
transformers
|
[
"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"
] |
image-classification
| 2023-11-08T19:20:41Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: lens-3
results: []
---
<!-- 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. -->
# Camera Lens Focal Length
This model predicts the focal length that the camera lens used to capture an image. It takes in an image and returns one of the following labels:
- ULTRA-WIDE
- WIDE
- MEDIUM
- LONG-LENS
- TELEPHOTO
### How to use
```python
from transformers import pipeline
pipe = pipeline("image-classification", model="tonyassi/camera-lens-focal-length")
result = pipe('image.png')
print(result)
```
## Dataset
Trained on a total of 5000 images. 1000 images from each label. Images were taken from popular Hollywood movies.
### ULTRA-WIDE
### WIDE
### MEDIUM
### LONG-LENS
### TELEPHOTO
## Model description
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k).
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Systran/faster-distil-whisper-small.en
|
Systran
| 2024-01-22T15:35:22Z | 362 | 0 |
ctranslate2
|
[
"ctranslate2",
"audio",
"automatic-speech-recognition",
"en",
"license:mit",
"region:us"
] |
automatic-speech-recognition
| 2024-01-19T03:24:09Z |
---
language:
- en
tags:
- audio
- automatic-speech-recognition
license: mit
library_name: ctranslate2
---
# Whisper small.en model for CTranslate2
This repository contains the conversion of [distil-whisper/distil-small.en](https://huggingface.co/distil-whisper/distil-small.en) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format.
This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/systran/faster-whisper).
## Example
```python
from faster_whisper import WhisperModel
model = WhisperModel("distil-small.en")
segments, info = model.transcribe("audio.mp3")
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
```
## Conversion details
The original model was converted with the following command:
```
ct2-transformers-converter --model distil-whisper/distil-small.en --output_dir faster-distil-whisper-small.en \
--copy_files tokenizer.json preprocessor_config.json --quantization float16
```
Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html).
## More information
**For more information about the original model, see its [model card](https://huggingface.co/distil-whisper/distil-small.en).**
|
Chrisisis/5CSes4aj9ZG85viaSarsnNH4zcisEBwCd2sjFfRLXPYvmxC8_vgg
|
Chrisisis
| 2024-02-24T08:32:30Z | 362 | 0 |
keras
|
[
"keras",
"region:us"
] | null | 2024-02-19T02:59:03Z |
Entry not found
|
Charles-Elena/stable-cascade
|
Charles-Elena
| 2024-03-01T03:13:15Z | 362 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"license:other",
"diffusers:StableCascadeDecoderPipeline",
"region:us"
] |
text-to-image
| 2024-03-01T03:13:14Z |
---
pipeline_tag: text-to-image
license: other
license_name: stable-cascade-nc-community
license_link: LICENSE
---
# Stable Cascade
<!-- Provide a quick summary of what the model is/does. -->
<img src="figures/collage_1.jpg" width="800">
This model is built upon the [Würstchen](https://openreview.net/forum?id=gU58d5QeGv) architecture and its main
difference to other models like Stable Diffusion is that it is working at a much smaller latent space. Why is this
important? The smaller the latent space, the **faster** you can run inference and the **cheaper** the training becomes.
How small is the latent space? Stable Diffusion uses a compression factor of 8, resulting in a 1024x1024 image being
encoded to 128x128. Stable Cascade achieves a compression factor of 42, meaning that it is possible to encode a
1024x1024 image to 24x24, while maintaining crisp reconstructions. The text-conditional model is then trained in the
highly compressed latent space. Previous versions of this architecture, achieved a 16x cost reduction over Stable
Diffusion 1.5. <br> <br>
Therefore, this kind of model is well suited for usages where efficiency is important. Furthermore, all known extensions
like finetuning, LoRA, ControlNet, IP-Adapter, LCM etc. are possible with this method as well.
## Model Details
### Model Description
Stable Cascade is a diffusion model trained to generate images given a text prompt.
- **Developed by:** Stability AI
- **Funded by:** Stability AI
- **Model type:** Generative text-to-image model
### Model Sources
For research purposes, we recommend our `StableCascade` Github repository (https://github.com/Stability-AI/StableCascade).
- **Repository:** https://github.com/Stability-AI/StableCascade
- **Paper:** https://openreview.net/forum?id=gU58d5QeGv
### Model Overview
Stable Cascade consists of three models: Stage A, Stage B and Stage C, representing a cascade to generate images,
hence the name "Stable Cascade".
Stage A & B are used to compress images, similar to what the job of the VAE is in Stable Diffusion.
However, with this setup, a much higher compression of images can be achieved. While the Stable Diffusion models use a
spatial compression factor of 8, encoding an image with resolution of 1024 x 1024 to 128 x 128, Stable Cascade achieves
a compression factor of 42. This encodes a 1024 x 1024 image to 24 x 24, while being able to accurately decode the
image. This comes with the great benefit of cheaper training and inference. Furthermore, Stage C is responsible
for generating the small 24 x 24 latents given a text prompt. The following picture shows this visually.
<img src="figures/model-overview.jpg" width="600">
For this release, we are providing two checkpoints for Stage C, two for Stage B and one for Stage A. Stage C comes with
a 1 billion and 3.6 billion parameter version, but we highly recommend using the 3.6 billion version, as most work was
put into its finetuning. The two versions for Stage B amount to 700 million and 1.5 billion parameters. Both achieve
great results, however the 1.5 billion excels at reconstructing small and fine details. Therefore, you will achieve the
best results if you use the larger variant of each. Lastly, Stage A contains 20 million parameters and is fixed due to
its small size.
## Evaluation
<img height="300" src="figures/comparison.png"/>
According to our evaluation, Stable Cascade performs best in both prompt alignment and aesthetic quality in almost all
comparisons. The above picture shows the results from a human evaluation using a mix of parti-prompts (link) and
aesthetic prompts. Specifically, Stable Cascade (30 inference steps) was compared against Playground v2 (50 inference
steps), SDXL (50 inference steps), SDXL Turbo (1 inference step) and Würstchen v2 (30 inference steps).
## Code Example
**⚠️ Important**: For the code below to work, you have to install `diffusers` from this branch while the PR is WIP.
```shell
pip install git+https://github.com/kashif/diffusers.git@wuerstchen-v3
```
```python
import torch
from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline
device = "cuda"
num_images_per_prompt = 2
prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", torch_dtype=torch.bfloat16).to(device)
decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", torch_dtype=torch.float16).to(device)
prompt = "Anthropomorphic cat dressed as a pilot"
negative_prompt = ""
prior_output = prior(
prompt=prompt,
height=1024,
width=1024,
negative_prompt=negative_prompt,
guidance_scale=4.0,
num_images_per_prompt=num_images_per_prompt,
num_inference_steps=20
)
decoder_output = decoder(
image_embeddings=prior_output.image_embeddings.half(),
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=0.0,
output_type="pil",
num_inference_steps=10
).images
#Now decoder_output is a list with your PIL images
```
## Uses
### Direct Use
The model is intended for research purposes for now. Possible research areas and tasks include
- Research on generative models.
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
Excluded uses are described below.
### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events,
and therefore using the model to generate such content is out-of-scope for the abilities of this model.
The model should not be used in any way that violates Stability AI's [Acceptable Use Policy](https://stability.ai/use-policy).
## Limitations and Bias
### Limitations
- Faces and people in general may not be generated properly.
- The autoencoding part of the model is lossy.
### Recommendations
The model is intended for research purposes only.
## How to Get Started with the Model
Check out https://github.com/Stability-AI/StableCascade
|
mradermacher/TimeCrystal-l2-13B-i1-GGUF
|
mradermacher
| 2024-05-06T05:53:11Z | 362 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-2",
"roleplaying",
"en",
"base_model:BlueNipples/TimeCrystal-l2-13B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-03-27T12:45:04Z |
---
base_model: BlueNipples/TimeCrystal-l2-13B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- llama-2
- roleplaying
---
## About
weighted/imatrix quants of https://huggingface.co/BlueNipples/TimeCrystal-l2-13B
<!-- provided-files -->
## 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/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-IQ1_S.gguf) | i1-IQ1_S | 3.2 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-IQ1_M.gguf) | i1-IQ1_M | 3.5 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-IQ2_S.gguf) | i1-IQ2_S | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-IQ2_M.gguf) | i1-IQ2_M | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-Q2_K.gguf) | i1-Q2_K | 5.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-IQ3_S.gguf) | i1-IQ3_S | 6.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 6.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-IQ3_M.gguf) | i1-IQ3_M | 6.3 | |
| [GGUF](https://huggingface.co/mradermacher/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 7.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 7.3 | |
| [GGUF](https://huggingface.co/mradermacher/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 7.7 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-Q4_0.gguf) | i1-Q4_0 | 7.7 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 8.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 9.5 | |
| [GGUF](https://huggingface.co/mradermacher/TimeCrystal-l2-13B-i1-GGUF/resolve/main/TimeCrystal-l2-13B.i1-Q6_K.gguf) | i1-Q6_K | 11.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Montebello_7B_SLERP-GGUF
|
mradermacher
| 2024-05-06T05:48:35Z | 362 | 1 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"yam-peleg/Experiment21-7B",
"louisgrc/Marengoli_7B_SLERP",
"en",
"base_model:louisgrc/Montebello_7B_SLERP",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-03-28T11:14:53Z |
---
base_model: louisgrc/Montebello_7B_SLERP
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- yam-peleg/Experiment21-7B
- louisgrc/Marengoli_7B_SLERP
---
## About
static quants of https://huggingface.co/louisgrc/Montebello_7B_SLERP
<!-- 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/Montebello_7B_SLERP-GGUF/resolve/main/Montebello_7B_SLERP.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Montebello_7B_SLERP-GGUF/resolve/main/Montebello_7B_SLERP.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Montebello_7B_SLERP-GGUF/resolve/main/Montebello_7B_SLERP.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Montebello_7B_SLERP-GGUF/resolve/main/Montebello_7B_SLERP.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Montebello_7B_SLERP-GGUF/resolve/main/Montebello_7B_SLERP.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Montebello_7B_SLERP-GGUF/resolve/main/Montebello_7B_SLERP.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Montebello_7B_SLERP-GGUF/resolve/main/Montebello_7B_SLERP.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Montebello_7B_SLERP-GGUF/resolve/main/Montebello_7B_SLERP.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Montebello_7B_SLERP-GGUF/resolve/main/Montebello_7B_SLERP.Q4_0.gguf) | Q4_0 | 4.4 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Montebello_7B_SLERP-GGUF/resolve/main/Montebello_7B_SLERP.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Montebello_7B_SLERP-GGUF/resolve/main/Montebello_7B_SLERP.IQ4_NL.gguf) | IQ4_NL | 4.4 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Montebello_7B_SLERP-GGUF/resolve/main/Montebello_7B_SLERP.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Montebello_7B_SLERP-GGUF/resolve/main/Montebello_7B_SLERP.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Montebello_7B_SLERP-GGUF/resolve/main/Montebello_7B_SLERP.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Montebello_7B_SLERP-GGUF/resolve/main/Montebello_7B_SLERP.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Montebello_7B_SLERP-GGUF/resolve/main/Montebello_7B_SLERP.Q8_0.gguf) | Q8_0 | 7.9 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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 -->
|
ShareGPTVideo/LLaVA-Hound-DPO
|
ShareGPTVideo
| 2024-03-31T21:07:16Z | 362 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llava_llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2024-03-31T20:49:54Z |
---
inference: false
license: apache-2.0
---
<br>
<br>
# LLaVA-Hound Model Card
## Model details
**Model type:**
LLaVA-Hound is an open-source video large multimodal model, fine-tuned from video instruction following data based on large language model.
This model is trained with **DPO** from **17k video instruction preference** dataset from **ShareGPTVideo/LLaVA-Hound-SFT** ckpt.
Base LLM: [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5)
**Model date:**
Trained on March 15, 2024.
**Paper or resources for more information:**
https://github.com/RifleZhang/LLaVA-Hound-DPO
## License
[lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) license.
**Where to send questions or comments about the model:**
https://github.com/RifleZhang/LLaVA-Hound-DPO/issues
## Intended use
**Primary intended uses:**
Video (image) instruction-following.
**Primary intended users:**
Researchers in artificial intelligence, large multimodal model, etc.
## Training dataset
ShareGPTVideo dataset.
## Evaluation
Follow https://github.com/RifleZhang/LLaVA-Hound-DPO/blob/main/README.md
|
mradermacher/PandafishHeatherReloaded-GGUF
|
mradermacher
| 2024-05-06T05:11:11Z | 362 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"ichigoberry/pandafish-dt-7b",
"MysticFoxMagic/HeatherSpell-7b",
"en",
"base_model:MysticFoxMagic/PandafishHeatherReloaded",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T01:32:30Z |
---
base_model: MysticFoxMagic/PandafishHeatherReloaded
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- ichigoberry/pandafish-dt-7b
- MysticFoxMagic/HeatherSpell-7b
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/MysticFoxMagic/PandafishHeatherReloaded
<!-- 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/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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 -->
|
nitsuai/Meta-Llama-3-70B-Instruct-GGUF
|
nitsuai
| 2024-04-22T01:19:26Z | 362 | 0 | null |
[
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"text-generation",
"en",
"license:other",
"region:us"
] |
text-generation
| 2024-04-22T01:19:26Z |
---
language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: other
license_name: llama3
license_link: LICENSE
extra_gated_prompt: >-
### META LLAMA 3 COMMUNITY LICENSE AGREEMENT
Meta Llama 3 Version Release Date: April 18, 2024
"Agreement" means the terms and conditions for use, reproduction, distribution and modification of the
Llama Materials set forth herein.
"Documentation" means the specifications, manuals and documentation accompanying Meta Llama 3
distributed by Meta at https://llama.meta.com/get-started/.
"Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into
this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or
regulations to provide legal consent and that has legal authority to bind your employer or such other
person or entity if you are entering in this Agreement on their behalf.
"Meta Llama 3" means the foundational large language models and software and algorithms, including
machine-learning model code, trained model weights, inference-enabling code, training-enabling code,
fine-tuning enabling code and other elements of the foregoing distributed by Meta at
https://llama.meta.com/llama-downloads.
"Llama Materials" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation (and any
portion thereof) made available under this Agreement.
"Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your
principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located
outside of the EEA or Switzerland).
1. License Rights and Redistribution.
a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free
limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama
Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the
Llama Materials.
b. Redistribution and Use.
i. If you distribute or make available the Llama Materials (or any derivative works
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quantized_by: bartowski
---
## Llamacpp Quantizations of Meta-Llama-3-70B-Instruct
Since official Llama 3 support has arrived to llama.cpp release, I will be remaking this entirely and uploading as soon as it's done.
This model has the <|eot_id|> token set to not-special, which seems to work better with current inference engines.
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> fork from pcuenca <a href="https://github.com/pcuenca/llama.cpp/tree/llama3-conversion">llama3-conversion</a> for quantization.
Original model: https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct
## Prompt format
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Meta-Llama-3-70B-Instruct-Q5_K_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-Q5_K_M.gguf) | Q5_K_M | 49.94GB | High quality, *recommended*. |
| [Meta-Llama-3-70B-Instruct-Q5_K_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-Q5_K_S.gguf) | Q5_K_S | 48.65GB | High quality, *recommended*. |
| [Meta-Llama-3-70B-Instruct-Q4_K_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-Q4_K_M.gguf) | Q4_K_M | 42.52GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [Meta-Llama-3-70B-Instruct-Q4_K_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-Q4_K_S.gguf) | Q4_K_S | 40.34GB | Slightly lower quality with more space savings, *recommended*. |
| [Meta-Llama-3-70B-Instruct-IQ4_NL.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-IQ4_NL.gguf) | IQ4_NL | 40.34GB | Decent quality, slightly smaller than Q4_K_S with similar performance *recommended*. |
| [Meta-Llama-3-70B-Instruct-IQ4_XS.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-IQ4_XS.gguf) | IQ4_XS | 38.26GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Meta-Llama-3-70B-Instruct-Q3_K_L.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-Q3_K_L.gguf) | Q3_K_L | 37.14GB | Lower quality but usable, good for low RAM availability. |
| [Meta-Llama-3-70B-Instruct-Q3_K_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-Q3_K_M.gguf) | Q3_K_M | 34.26GB | Even lower quality. |
| [Meta-Llama-3-70B-Instruct-IQ3_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-IQ3_M.gguf) | IQ3_M | 31.93GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Meta-Llama-3-70B-Instruct-IQ3_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-IQ3_S.gguf) | IQ3_S | 30.91GB | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| [Meta-Llama-3-70B-Instruct-Q3_K_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-Q3_K_S.gguf) | Q3_K_S | 30.91GB | Low quality, not recommended. |
| [Meta-Llama-3-70B-Instruct-IQ3_XS.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-IQ3_XS.gguf) | IQ3_XS | 29.30GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Meta-Llama-3-70B-Instruct-Q2_K.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-Q2_K.gguf) | Q2_K | 26.37GB | Very low quality but surprisingly usable. |
## 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
|
PrunaAI/OpenBioLLM-Llama3-8B-GGUF-smashed
|
PrunaAI
| 2024-04-28T18:04:21Z | 362 | 0 | null |
[
"gguf",
"pruna-ai",
"region:us"
] | null | 2024-04-28T16:13:22Z |
---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- 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 -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
## This repo contains GGUF versions of the aaditya/OpenBioLLM-Llama3-8B 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/OpenBioLLM-Llama3-8B-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/OpenBioLLM-Llama3-8B-GGUF-smashed OpenBioLLM-Llama3-8B.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/OpenBioLLM-Llama3-8B-GGUF-smashed --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download PrunaAI/OpenBioLLM-Llama3-8B-GGUF-smashed OpenBioLLM-Llama3-8B.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 OpenBioLLM-Llama3-8B.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="./OpenBioLLM-Llama3-8B.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="./OpenBioLLM-Llama3-8B.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).
|
RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf
|
RichardErkhov
| 2024-05-03T22:44:51Z | 362 | 0 | null |
[
"gguf",
"region:us"
] | null | 2024-05-03T22:28:54Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
deepseek-coder-1.3b-instruct - GGUF
- Model creator: https://huggingface.co/deepseek-ai/
- Original model: https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-instruct/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [deepseek-coder-1.3b-instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf/blob/main/deepseek-coder-1.3b-instruct.Q2_K.gguf) | Q2_K | 0.52GB |
| [deepseek-coder-1.3b-instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf/blob/main/deepseek-coder-1.3b-instruct.IQ3_XS.gguf) | IQ3_XS | 0.57GB |
| [deepseek-coder-1.3b-instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf/blob/main/deepseek-coder-1.3b-instruct.IQ3_S.gguf) | IQ3_S | 0.6GB |
| [deepseek-coder-1.3b-instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf/blob/main/deepseek-coder-1.3b-instruct.Q3_K_S.gguf) | Q3_K_S | 0.6GB |
| [deepseek-coder-1.3b-instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf/blob/main/deepseek-coder-1.3b-instruct.IQ3_M.gguf) | IQ3_M | 0.63GB |
| [deepseek-coder-1.3b-instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf/blob/main/deepseek-coder-1.3b-instruct.Q3_K.gguf) | Q3_K | 0.66GB |
| [deepseek-coder-1.3b-instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf/blob/main/deepseek-coder-1.3b-instruct.Q3_K_M.gguf) | Q3_K_M | 0.66GB |
| [deepseek-coder-1.3b-instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf/blob/main/deepseek-coder-1.3b-instruct.Q3_K_L.gguf) | Q3_K_L | 0.69GB |
| [deepseek-coder-1.3b-instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf/blob/main/deepseek-coder-1.3b-instruct.IQ4_XS.gguf) | IQ4_XS | 0.7GB |
| [deepseek-coder-1.3b-instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf/blob/main/deepseek-coder-1.3b-instruct.Q4_0.gguf) | Q4_0 | 0.72GB |
| [deepseek-coder-1.3b-instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf/blob/main/deepseek-coder-1.3b-instruct.IQ4_NL.gguf) | IQ4_NL | 0.73GB |
| [deepseek-coder-1.3b-instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf/blob/main/deepseek-coder-1.3b-instruct.Q4_K_S.gguf) | Q4_K_S | 0.76GB |
| [deepseek-coder-1.3b-instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf/blob/main/deepseek-coder-1.3b-instruct.Q4_K.gguf) | Q4_K | 0.81GB |
| [deepseek-coder-1.3b-instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf/blob/main/deepseek-coder-1.3b-instruct.Q4_K_M.gguf) | Q4_K_M | 0.81GB |
| [deepseek-coder-1.3b-instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf/blob/main/deepseek-coder-1.3b-instruct.Q4_1.gguf) | Q4_1 | 0.8GB |
| [deepseek-coder-1.3b-instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf/blob/main/deepseek-coder-1.3b-instruct.Q5_0.gguf) | Q5_0 | 0.87GB |
| [deepseek-coder-1.3b-instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf/blob/main/deepseek-coder-1.3b-instruct.Q5_K_S.gguf) | Q5_K_S | 0.89GB |
| [deepseek-coder-1.3b-instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf/blob/main/deepseek-coder-1.3b-instruct.Q5_K.gguf) | Q5_K | 0.93GB |
| [deepseek-coder-1.3b-instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf/blob/main/deepseek-coder-1.3b-instruct.Q5_K_M.gguf) | Q5_K_M | 0.93GB |
| [deepseek-coder-1.3b-instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf/blob/main/deepseek-coder-1.3b-instruct.Q5_1.gguf) | Q5_1 | 0.95GB |
| [deepseek-coder-1.3b-instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-1.3b-instruct-gguf/blob/main/deepseek-coder-1.3b-instruct.Q6_K.gguf) | Q6_K | 1.09GB |
Original model description:
---
license: other
license_name: deepseek
license_link: LICENSE
---
<p align="center">
<img width="1000px" alt="DeepSeek Coder" src="https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/guoday/assert/blob/main/QR.png?raw=true">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek Coder
Deepseek Coder is composed of a series of code language models, each trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on project-level code corpus by employing a window size of 16K and a extra fill-in-the-blank task, to support project-level code completion and infilling. For coding capabilities, Deepseek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks.
- **Massive Training Data**: Trained from scratch on 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages.
- **Highly Flexible & Scalable**: Offered in model sizes of 1.3B, 5.7B, 6.7B, and 33B, enabling users to choose the setup most suitable for their requirements.
- **Superior Model Performance**: State-of-the-art performance among publicly available code models on HumanEval, MultiPL-E, MBPP, DS-1000, and APPS benchmarks.
- **Advanced Code Completion Capabilities**: A window size of 16K and a fill-in-the-blank task, supporting project-level code completion and infilling tasks.
### 2. Model Summary
deepseek-coder-1.3b-instruct is a 1.3B parameter model initialized from deepseek-coder-1.3b-base and fine-tuned on 2B tokens of instruction data.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-coder](https://github.com/deepseek-ai/deepseek-coder)
- **Chat With DeepSeek Coder:** [DeepSeek-Coder](https://coder.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### Chat Model Inference
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-1.3b-instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-1.3b-instruct", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
messages=[
{ 'role': 'user', 'content': "write a quick sort algorithm in python."}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
# tokenizer.eos_token_id is the id of <|EOT|> token
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]).
|
bartowski/Llama-3-Orca-2.0-8B-GGUF
|
bartowski
| 2024-05-11T15:56:43Z | 362 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation",
"license:other",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-11T15:36:06Z |
---
library_name: transformers
license: other
quantized_by: bartowski
pipeline_tag: text-generation
---
## Llamacpp imatrix Quantizations of Llama-3-Orca-2.0-8B
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2828">b2828</a> for quantization.
Original model: https://huggingface.co/Locutusque/Llama-3-Orca-2.0-8B
All quants made using imatrix option with dataset provided by Kalomaze [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384)
## Prompt format
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Llama-3-Orca-2.0-8B-Q8_0.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-Q8_0.gguf) | Q8_0 | 8.54GB | Extremely high quality, generally unneeded but max available quant. |
| [Llama-3-Orca-2.0-8B-Q6_K.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-Q6_K.gguf) | Q6_K | 6.59GB | Very high quality, near perfect, *recommended*. |
| [Llama-3-Orca-2.0-8B-Q5_K_M.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-Q5_K_M.gguf) | Q5_K_M | 5.73GB | High quality, *recommended*. |
| [Llama-3-Orca-2.0-8B-Q5_K_S.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-Q5_K_S.gguf) | Q5_K_S | 5.59GB | High quality, *recommended*. |
| [Llama-3-Orca-2.0-8B-Q4_K_M.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-Q4_K_M.gguf) | Q4_K_M | 4.92GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [Llama-3-Orca-2.0-8B-Q4_K_S.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-Q4_K_S.gguf) | Q4_K_S | 4.69GB | Slightly lower quality with more space savings, *recommended*. |
| [Llama-3-Orca-2.0-8B-IQ4_NL.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-IQ4_NL.gguf) | IQ4_NL | 4.67GB | Decent quality, slightly smaller than Q4_K_S with similar performance *recommended*. |
| [Llama-3-Orca-2.0-8B-IQ4_XS.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-IQ4_XS.gguf) | IQ4_XS | 4.44GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Llama-3-Orca-2.0-8B-Q3_K_L.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-Q3_K_L.gguf) | Q3_K_L | 4.32GB | Lower quality but usable, good for low RAM availability. |
| [Llama-3-Orca-2.0-8B-Q3_K_M.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-Q3_K_M.gguf) | Q3_K_M | 4.01GB | Even lower quality. |
| [Llama-3-Orca-2.0-8B-IQ3_M.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-IQ3_M.gguf) | IQ3_M | 3.78GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Llama-3-Orca-2.0-8B-IQ3_S.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-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-Orca-2.0-8B-Q3_K_S.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-Q3_K_S.gguf) | Q3_K_S | 3.66GB | Low quality, not recommended. |
| [Llama-3-Orca-2.0-8B-IQ3_XS.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-IQ3_XS.gguf) | IQ3_XS | 3.51GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Llama-3-Orca-2.0-8B-IQ3_XXS.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-IQ3_XXS.gguf) | IQ3_XXS | 3.27GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [Llama-3-Orca-2.0-8B-Q2_K.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-Q2_K.gguf) | Q2_K | 3.17GB | Very low quality but surprisingly usable. |
| [Llama-3-Orca-2.0-8B-IQ2_M.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-IQ2_M.gguf) | IQ2_M | 2.94GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [Llama-3-Orca-2.0-8B-IQ2_S.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-IQ2_S.gguf) | IQ2_S | 2.75GB | Very low quality, uses SOTA techniques to be usable. |
| [Llama-3-Orca-2.0-8B-IQ2_XS.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-IQ2_XS.gguf) | IQ2_XS | 2.60GB | Very low quality, uses SOTA techniques to be usable. |
| [Llama-3-Orca-2.0-8B-IQ2_XXS.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-IQ2_XXS.gguf) | IQ2_XXS | 2.39GB | Lower quality, uses SOTA techniques to be usable. |
| [Llama-3-Orca-2.0-8B-IQ1_M.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-IQ1_M.gguf) | IQ1_M | 2.16GB | Extremely low quality, *not* recommended. |
| [Llama-3-Orca-2.0-8B-IQ1_S.gguf](https://huggingface.co/bartowski/Llama-3-Orca-2.0-8B-GGUF/blob/main/Llama-3-Orca-2.0-8B-IQ1_S.gguf) | IQ1_S | 2.01GB | Extremely low quality, *not* recommended. |
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/Llama-3-Orca-2.0-8B-GGUF --include "Llama-3-Orca-2.0-8B-Q4_K_M.gguf" --local-dir ./ --local-dir-use-symlinks False
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/Llama-3-Orca-2.0-8B-GGUF --include "Llama-3-Orca-2.0-8B-Q8_0.gguf/*" --local-dir Llama-3-Orca-2.0-8B-Q8_0 --local-dir-use-symlinks False
```
You can either specify a new local-dir (Llama-3-Orca-2.0-8B-Q8_0) or download them all in place (./)
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
RichardErkhov/Unbabel_-_TowerInstruct-7B-v0.2-gguf
|
RichardErkhov
| 2024-05-12T13:15:40Z | 362 | 0 | null |
[
"gguf",
"region:us"
] | null | 2024-05-12T01:53:58Z |
Entry not found
|
mradermacher/pali-8B-merged-GGUF
|
mradermacher
| 2024-05-14T10:23:49Z | 362 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:DataGuard/pali-8B-merged",
"endpoints_compatible",
"region:us"
] | null | 2024-05-14T09:55:01Z |
---
base_model: DataGuard/pali-8B-merged
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/DataGuard/pali-8B-merged
<!-- 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/pali-8B-merged-GGUF/resolve/main/pali-8B-merged.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/pali-8B-merged-GGUF/resolve/main/pali-8B-merged.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/pali-8B-merged-GGUF/resolve/main/pali-8B-merged.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/pali-8B-merged-GGUF/resolve/main/pali-8B-merged.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/pali-8B-merged-GGUF/resolve/main/pali-8B-merged.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/pali-8B-merged-GGUF/resolve/main/pali-8B-merged.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/pali-8B-merged-GGUF/resolve/main/pali-8B-merged.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/pali-8B-merged-GGUF/resolve/main/pali-8B-merged.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/pali-8B-merged-GGUF/resolve/main/pali-8B-merged.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/pali-8B-merged-GGUF/resolve/main/pali-8B-merged.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/pali-8B-merged-GGUF/resolve/main/pali-8B-merged.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/pali-8B-merged-GGUF/resolve/main/pali-8B-merged.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/pali-8B-merged-GGUF/resolve/main/pali-8B-merged.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/pali-8B-merged-GGUF/resolve/main/pali-8B-merged.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/pali-8B-merged-GGUF/resolve/main/pali-8B-merged.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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 -->
|
RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf
|
RichardErkhov
| 2024-05-24T21:51:29Z | 362 | 0 | null |
[
"gguf",
"region:us"
] | null | 2024-05-24T18:57:23Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
RoleBeagle-11B - GGUF
- Model creator: https://huggingface.co/vicgalle/
- Original model: https://huggingface.co/vicgalle/RoleBeagle-11B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [RoleBeagle-11B.Q2_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.Q2_K.gguf) | Q2_K | 3.73GB |
| [RoleBeagle-11B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.IQ3_XS.gguf) | IQ3_XS | 4.14GB |
| [RoleBeagle-11B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.IQ3_S.gguf) | IQ3_S | 4.37GB |
| [RoleBeagle-11B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.Q3_K_S.gguf) | Q3_K_S | 4.34GB |
| [RoleBeagle-11B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.IQ3_M.gguf) | IQ3_M | 4.51GB |
| [RoleBeagle-11B.Q3_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.Q3_K.gguf) | Q3_K | 4.84GB |
| [RoleBeagle-11B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.Q3_K_M.gguf) | Q3_K_M | 4.84GB |
| [RoleBeagle-11B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.Q3_K_L.gguf) | Q3_K_L | 5.26GB |
| [RoleBeagle-11B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.IQ4_XS.gguf) | IQ4_XS | 5.43GB |
| [RoleBeagle-11B.Q4_0.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.Q4_0.gguf) | Q4_0 | 5.66GB |
| [RoleBeagle-11B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.IQ4_NL.gguf) | IQ4_NL | 5.72GB |
| [RoleBeagle-11B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.Q4_K_S.gguf) | Q4_K_S | 5.7GB |
| [RoleBeagle-11B.Q4_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.Q4_K.gguf) | Q4_K | 6.02GB |
| [RoleBeagle-11B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.Q4_K_M.gguf) | Q4_K_M | 6.02GB |
| [RoleBeagle-11B.Q4_1.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.Q4_1.gguf) | Q4_1 | 6.27GB |
| [RoleBeagle-11B.Q5_0.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.Q5_0.gguf) | Q5_0 | 6.89GB |
| [RoleBeagle-11B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.Q5_K_S.gguf) | Q5_K_S | 6.89GB |
| [RoleBeagle-11B.Q5_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.Q5_K.gguf) | Q5_K | 7.08GB |
| [RoleBeagle-11B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.Q5_K_M.gguf) | Q5_K_M | 7.08GB |
| [RoleBeagle-11B.Q5_1.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.Q5_1.gguf) | Q5_1 | 7.51GB |
| [RoleBeagle-11B.Q6_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.Q6_K.gguf) | Q6_K | 8.2GB |
| [RoleBeagle-11B.Q8_0.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_RoleBeagle-11B-gguf/blob/main/RoleBeagle-11B.Q8_0.gguf) | Q8_0 | 10.62GB |
Original model description:
---
license: apache-2.0
library_name: transformers
datasets:
- vicgalle/OpenHermesPreferences-roleplay
model-index:
- name: RoleBeagle-11B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 72.35
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/RoleBeagle-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 89.77
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/RoleBeagle-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 66.35
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/RoleBeagle-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 77.92
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/RoleBeagle-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 84.06
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/RoleBeagle-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.88
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/RoleBeagle-11B
name: Open LLM Leaderboard
tags:
- roleplay
- rp
---
# RoleBeagle-11B
A DPO-finetune from [vicgalle/CarbonBeagle-11B-truthy](https://huggingface.co/vicgalle/CarbonBeagle-11B-truthy) over a subset of OpenHermesPreferences containting RP conversations.
It keeps most of the intelligence from CarbonBeagle-11B, and hopefuly can role-play better.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalle__RoleBeagle-11B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |76.06|
|AI2 Reasoning Challenge (25-Shot)|72.35|
|HellaSwag (10-Shot) |89.77|
|MMLU (5-Shot) |66.35|
|TruthfulQA (0-shot) |77.92|
|Winogrande (5-shot) |84.06|
|GSM8k (5-shot) |65.88|
|
RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf
|
RichardErkhov
| 2024-05-25T13:21:01Z | 362 | 0 | null |
[
"gguf",
"region:us"
] | null | 2024-05-25T10:11:23Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Antares-11b-v2 - GGUF
- Model creator: https://huggingface.co/decapoda-research/
- Original model: https://huggingface.co/decapoda-research/Antares-11b-v2/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Antares-11b-v2.Q2_K.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.Q2_K.gguf) | Q2_K | 3.73GB |
| [Antares-11b-v2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.IQ3_XS.gguf) | IQ3_XS | 4.14GB |
| [Antares-11b-v2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.IQ3_S.gguf) | IQ3_S | 4.37GB |
| [Antares-11b-v2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.Q3_K_S.gguf) | Q3_K_S | 4.34GB |
| [Antares-11b-v2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.IQ3_M.gguf) | IQ3_M | 4.51GB |
| [Antares-11b-v2.Q3_K.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.Q3_K.gguf) | Q3_K | 4.84GB |
| [Antares-11b-v2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.Q3_K_M.gguf) | Q3_K_M | 4.84GB |
| [Antares-11b-v2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.Q3_K_L.gguf) | Q3_K_L | 5.26GB |
| [Antares-11b-v2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.IQ4_XS.gguf) | IQ4_XS | 5.43GB |
| [Antares-11b-v2.Q4_0.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.Q4_0.gguf) | Q4_0 | 5.66GB |
| [Antares-11b-v2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.IQ4_NL.gguf) | IQ4_NL | 5.72GB |
| [Antares-11b-v2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.Q4_K_S.gguf) | Q4_K_S | 5.7GB |
| [Antares-11b-v2.Q4_K.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.Q4_K.gguf) | Q4_K | 6.02GB |
| [Antares-11b-v2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.Q4_K_M.gguf) | Q4_K_M | 6.02GB |
| [Antares-11b-v2.Q4_1.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.Q4_1.gguf) | Q4_1 | 6.27GB |
| [Antares-11b-v2.Q5_0.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.Q5_0.gguf) | Q5_0 | 6.89GB |
| [Antares-11b-v2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.Q5_K_S.gguf) | Q5_K_S | 6.89GB |
| [Antares-11b-v2.Q5_K.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.Q5_K.gguf) | Q5_K | 7.08GB |
| [Antares-11b-v2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.Q5_K_M.gguf) | Q5_K_M | 7.08GB |
| [Antares-11b-v2.Q5_1.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.Q5_1.gguf) | Q5_1 | 7.51GB |
| [Antares-11b-v2.Q6_K.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.Q6_K.gguf) | Q6_K | 8.2GB |
| [Antares-11b-v2.Q8_0.gguf](https://huggingface.co/RichardErkhov/decapoda-research_-_Antares-11b-v2-gguf/blob/main/Antares-11b-v2.Q8_0.gguf) | Q8_0 | 10.62GB |
Original model description:
---
license: cc-by-nc-4.0
datasets:
- jondurbin/bagel-v0.3
base_model: decapod-research/Antares-11b-v1
model-index:
- name: Antares-11b-v2
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 69.03
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=decapoda-research/Antares-11b-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 87.54
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=decapoda-research/Antares-11b-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 66.19
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=decapoda-research/Antares-11b-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 59.17
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=decapoda-research/Antares-11b-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 83.19
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=decapoda-research/Antares-11b-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 60.5
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=decapoda-research/Antares-11b-v2
name: Open LLM Leaderboard
---
Fine-tune of Upstage AI's SOLAR-10.7B-Instruct-v1.0 model, using the OpenHermes, Platypus, and Capybara datasets. Additionally fine-tuned on Jon Durbin's Bagel v0.3, plus a few unreleased datasets.
Fine-tuned on 8x4090s for 1.25 epochs.
### Model Sources [optional]
- **Repository:** TBD
- **Demo:** TBD
## Bias, Risks, and Limitations
This fine-tune has had zero alignment, safety data, or anything else shoved down it's throat.
## Training Details
### Training Data
See the sidebar for links to the relevant datasets.
### Training Procedure
Trained using QLORA via the Axolotl tool.
## Evaluation
TBD
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_decapoda-research__Antares-11b-v2)
| Metric |Value|
|---------------------------------|----:|
|Avg. |70.94|
|AI2 Reasoning Challenge (25-Shot)|69.03|
|HellaSwag (10-Shot) |87.54|
|MMLU (5-Shot) |66.19|
|TruthfulQA (0-shot) |59.17|
|Winogrande (5-shot) |83.19|
|GSM8k (5-shot) |60.50|
|
QuantFactory/CodeLlama-7B-Kexer-GGUF
|
QuantFactory
| 2024-05-26T16:28:02Z | 362 | 0 | null |
[
"gguf",
"code",
"dataset:JetBrains/KExercises",
"base_model:JetBrains/CodeLlama-7B-Kexer",
"license:apache-2.0",
"region:us"
] | null | 2024-05-26T15:38:33Z |
---
license: apache-2.0
datasets:
- JetBrains/KExercises
base_model: JetBrains/CodeLlama-7B-Kexer
results:
- task:
type: text-generation
dataset:
name: MultiPL-HumanEval (Kotlin)
type: openai_humaneval
metrics:
- name: pass@1
type: pass@1
value: 42.24
tags:
- code
---
# CodeLlama-7B-Kexer-GGUF
This is quantized version of [JetBrains/CodeLlama-7B-Kexer](https://huggingface.co/JetBrains/CodeLlama-7B-Kexer) created using llama.cpp
# Model Description
Kexer models are a collection of open-source generative text models fine-tuned on the [Kotlin Exercices](https://huggingface.co/datasets/JetBrains/KExercises) dataset.
This is a repository for the fine-tuned **CodeLlama-7b** model in the *Hugging Face Transformers* format.
# Training setup
The model was trained on one A100 GPU with the following hyperparameters:
| **Hyperparameter** | **Value** |
|:---------------------------:|:----------------------------------------:|
| `warmup` | 10% |
| `max_lr` | 1e-4 |
| `scheduler` | linear |
| `total_batch_size` | 256 (~130K tokens per step) |
| `num_epochs` | 4 |
More details about fine-tuning can be found in the technical report (coming soon!).
# Fine-tuning data
For tuning this model, we used 15K exmaples from the synthetically generated [Kotlin Exercices](https://huggingface.co/datasets/JetBrains/KExercises) dataset. Every example follows the HumanEval format. In total, the dataset contains about 3.5M tokens.
# Evaluation
For evaluation, we used the [Kotlin HumanEval](https://huggingface.co/datasets/JetBrains/Kotlin_HumanEval) dataset, which contains all 161 tasks from HumanEval translated into Kotlin by human experts. You can find more details about the pre-processing necessary to obtain our results, including the code for running, on the [datasets's page](https://huggingface.co/datasets/JetBrains/Kotlin_HumanEval).
Here are the results of our evaluation:
| **Model name** | **Kotlin HumanEval Pass Rate** |
|:---------------------------:|:----------------------------------------:|
| `CodeLlama-7B` | 26.89 |
| `CodeLlama-7B-Kexer` | **42.24** |
# Ethical considerations and limitations
CodeLlama-7B-Kexer is a new technology that carries risks with use. The testing conducted to date has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, CodeLlama-7B-Kexer's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. The model was fine-tuned on a specific data format (Kotlin tasks), and deviation from this format can also lead to inaccurate or undesirable responses to user queries. Therefore, before deploying any applications of CodeLlama-7B-Kexer, developers should perform safety testing and tuning tailored to their specific applications of the model.
|
hallisky/speech-classifier-roberta-large
|
hallisky
| 2024-05-27T04:39:16Z | 362 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-05-27T00:14:00Z |
---
license: apache-2.0
---
|
GraydientPlatformAPI/ponyrealism21-xl
|
GraydientPlatformAPI
| 2024-05-29T08:11:10Z | 362 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2024-05-29T07:40:35Z |
Entry not found
|
afrideva/Qwen2-0.5B-Instruct-GGUF
|
afrideva
| 2024-06-06T19:05:33Z | 362 | 0 | null |
[
"gguf",
"chat",
"ggml",
"quantized",
"text-generation",
"en",
"base_model:Qwen/Qwen2-0.5B-Instruct",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2024-06-06T19:00:35Z |
---
base_model: Qwen/Qwen2-0.5B-Instruct
inference: true
language:
- en
license: apache-2.0
model_creator: Qwen
model_name: Qwen2-0.5B-Instruct
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- chat
- gguf
- ggml
- quantized
---
# Qwen2-0.5B-Instruct-GGUF
Quantized GGUF model files for [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) from [Qwen](https://huggingface.co/Qwen)
## Original Model Card:
# Qwen2-0.5B-Instruct
## Introduction
Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 0.5B Qwen2 model.
Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc.
For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/), [GitHub](https://github.com/QwenLM/Qwen2), and [Documentation](https://qwen.readthedocs.io/en/latest/).
<br>
## Model Details
Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes.
## Training details
We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization.
## Requirements
The code of Qwen2 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
```
KeyError: 'qwen2'
```
## Quickstart
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen2-0.5B-Instruct",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
## Evaluation
We briefly compare Qwen2-0.5B-Instruct with Qwen1.5-0.5B-Chat. The results are as follows:
| Datasets | Qwen1.5-0.5B-Chat | **Qwen2-0.5B-Instruct** | Qwen1.5-1.8B-Chat | **Qwen2-1.5B-Instruct** |
| :--- | :---: | :---: | :---: | :---: |
| MMLU | 35.0 | **37.9** | 43.7 | **52.4** |
| HumanEval | 9.1 | **17.1** | 25.0 | **37.8** |
| GSM8K | 11.3 | **40.1** | 35.3 | **61.6** |
| C-Eval | 37.2 | **45.2** | 55.3 | **63.8** |
| IFEval (Prompt Strict-Acc.) | 14.6 | **20.0** | 16.8 | **29.0** |
## Citation
If you find our work helpful, feel free to give us a cite.
```
@article{qwen2,
title={Qwen2 Technical Report},
year={2024}
}
```
|
morrissas/TAIDE-LX-7B-Chat-Q4_K_M-GGUF
|
morrissas
| 2024-06-10T09:22:05Z | 362 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:taide/TAIDE-LX-7B-Chat",
"license:other",
"region:us"
] | null | 2024-06-10T09:21:53Z |
---
license: other
tags:
- llama-cpp
- gguf-my-repo
base_model: taide/TAIDE-LX-7B-Chat
license_name: taide-l-models-community-license-agreement
license_link: https://drive.google.com/file/d/1FcUZjbUH6jr4xoCyAronN_slLgcdhEUd/view
extra_gated_heading: 您需要先同意授權條款才能使用此模型
extra_gated_fields:
姓名(Name): text
生日(Date of birth): date_picker
國家(Country): country
所屬單位(Affiliation): text
geo: ip_location
按下送出表示您同意社群授權同意書與個人資料蒐集告知聲明(By clicking Submit below I accept the terms of the license and privacy policy): checkbox
extra_gated_prompt: '* ### [TAIDE L 類模型社群授權同意書(License)](https://drive.google.com/file/d/1FcUZjbUH6jr4xoCyAronN_slLgcdhEUd/view)
* ### [個人資料蒐集告知聲明(Privacy policy)](https://drive.google.com/file/d/1JTfZu_MdU_TR1-1sn2jbQyW7TLrxjwS5/view)'
extra_gated_button_content: 送出(Submit)
---
# morrissas/TAIDE-LX-7B-Chat-Q4_K_M-GGUF
This model was converted to GGUF format from [`taide/TAIDE-LX-7B-Chat`](https://huggingface.co/taide/TAIDE-LX-7B-Chat) 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/taide/TAIDE-LX-7B-Chat) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama --hf-repo morrissas/TAIDE-LX-7B-Chat-Q4_K_M-GGUF --hf-file taide-lx-7b-chat-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo morrissas/TAIDE-LX-7B-Chat-Q4_K_M-GGUF --hf-file taide-lx-7b-chat-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.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./main --hf-repo morrissas/TAIDE-LX-7B-Chat-Q4_K_M-GGUF --hf-file taide-lx-7b-chat-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./server --hf-repo morrissas/TAIDE-LX-7B-Chat-Q4_K_M-GGUF --hf-file taide-lx-7b-chat-q4_k_m.gguf -c 2048
```
|
DHBaek/gpt2-stackoverflow-question-contents-generator
|
DHBaek
| 2021-06-15T02:18:56Z | 361 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
Entry not found
|
ibm/knowgl-large
|
ibm
| 2023-12-28T12:44:36Z | 361 | 66 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"seq2seq",
"relation-extraction",
"triple-generation",
"entity-linking",
"entity-type-linking",
"relation-linking",
"en",
"dataset:Babelscape/rebel-dataset",
"arxiv:2210.13952",
"arxiv:2207.05188",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-20T16:10:09Z |
---
language:
- en
license: cc-by-nc-sa-4.0
tags:
- seq2seq
- relation-extraction
- triple-generation
- entity-linking
- entity-type-linking
- relation-linking
datasets: Babelscape/rebel-dataset
widget:
- text: The Italian Space Agency’s Light Italian CubeSat for Imaging of Asteroids,
or LICIACube, will fly by Dimorphos to capture images and video of the impact
plume as it sprays up off the asteroid and maybe even spy the crater it could
leave behind.
model-index:
- name: knowgl
results:
- task:
type: Relation-Extraction
name: Relation Extraction
dataset:
name: Babelscape/rebel-dataset
type: REBEL
metrics:
- type: re+ macro f1
value: 70.74
name: RE+ Macro F1
---
# KnowGL: Knowledge Generation and Linking from Text
The `knowgl-large` model is trained by combining Wikidata with an extended version of the training data in the [REBEL](https://huggingface.co/datasets/Babelscape/rebel-dataset) dataset. Given a sentence, KnowGL generates triple(s) in the following format:
```
[(subject mention # subject label # subject type) | relation label | (object mention # object label # object type)]
```
If there are more than one triples generated, they are separated by `$` in the output. More details in [Rossiello et al. (AAAI 2023)](https://arxiv.org/pdf/2210.13952.pdf).
The model achieves state-of-the-art results for relation extraction on the REBEL dataset. See results in [Mihindukulasooriya et al. (ISWC 2022)](https://arxiv.org/pdf/2207.05188.pdf).
The generated labels (for the subject, relation, and object) and their types can be directly mapped to Wikidata IDs associated with them.
#### Citation
```bibtex
@inproceedings{DBLP:conf/aaai/RossielloCMCG23,
author = {Gaetano Rossiello and
Md. Faisal Mahbub Chowdhury and
Nandana Mihindukulasooriya and
Owen Cornec and
Alfio Massimiliano Gliozzo},
title = {KnowGL: Knowledge Generation and Linking from Text},
booktitle = {{AAAI}},
pages = {16476--16478},
publisher = {{AAAI} Press},
year = {2023}
}
```
```bibtex
@inproceedings{DBLP:conf/semweb/Mihindukulasooriya22,
author = {Nandana Mihindukulasooriya and
Mike Sava and
Gaetano Rossiello and
Md. Faisal Mahbub Chowdhury and
Irene Yachbes and
Aditya Gidh and
Jillian Duckwitz and
Kovit Nisar and
Michael Santos and
Alfio Gliozzo},
title = {Knowledge Graph Induction Enabling Recommending and Trend Analysis:
{A} Corporate Research Community Use Case},
booktitle = {{ISWC}},
series = {Lecture Notes in Computer Science},
volume = {13489},
pages = {827--844},
publisher = {Springer},
year = {2022}
}
```
|
timm/tf_efficientnet_cc_b0_4e.in1k
|
timm
| 2023-04-27T21:28:17Z | 361 | 0 |
timm
|
[
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:1904.04971",
"arxiv:1905.11946",
"license:apache-2.0",
"region:us"
] |
image-classification
| 2022-12-13T00:07:52Z |
---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
---
# Model card for tf_efficientnet_cc_b0_4e.in1k
A EfficientNet-CondConv image classification model. Trained on ImageNet-1k in Tensorflow by paper authors, ported to PyTorch by Ross Wightman.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 13.3
- GMACs: 0.4
- Activations (M): 9.4
- Image size: 224 x 224
- **Papers:**
- CondConv: Conditionally Parameterized Convolutions for Efficient Inference: https://arxiv.org/abs/1904.04971
- EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks: https://arxiv.org/abs/1905.11946
- **Dataset:** ImageNet-1k
- **Original:** https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('tf_efficientnet_cc_b0_4e.in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'tf_efficientnet_cc_b0_4e.in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 16, 112, 112])
# torch.Size([1, 24, 56, 56])
# torch.Size([1, 40, 28, 28])
# torch.Size([1, 112, 14, 14])
# torch.Size([1, 320, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'tf_efficientnet_cc_b0_4e.in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1280, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@article{yang2019condconv,
title={Condconv: Conditionally parameterized convolutions for efficient inference},
author={Yang, Brandon and Bender, Gabriel and Le, Quoc V and Ngiam, Jiquan},
journal={Advances in Neural Information Processing Systems},
volume={32},
year={2019}
}
```
```bibtex
@inproceedings{tan2019efficientnet,
title={Efficientnet: Rethinking model scaling for convolutional neural networks},
author={Tan, Mingxing and Le, Quoc},
booktitle={International conference on machine learning},
pages={6105--6114},
year={2019},
organization={PMLR}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
timm/eca_resnext26ts.ch_in1k
|
timm
| 2024-02-10T23:34:27Z | 361 | 0 |
timm
|
[
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:1910.03151",
"arxiv:1611.05431",
"license:apache-2.0",
"region:us"
] |
image-classification
| 2023-03-22T07:13:49Z |
---
license: apache-2.0
library_name: timm
tags:
- image-classification
- timm
datasets:
- imagenet-1k
---
# Model card for eca_resnext26ts.ch_in1k
A ECA-ResNeXt image classification model (ResNeXt with 'Efficient Channel Attention'). This model features a tiered 3-layer stem and SiLU activations. Trained on ImageNet-1k by Ross Wightman in `timm`.
This model architecture is implemented using `timm`'s flexible [BYOBNet (Bring-Your-Own-Blocks Network)](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/byobnet.py).
BYOBNet allows configuration of:
* block / stage layout
* stem layout
* output stride (dilation)
* activation and norm layers
* channel and spatial / self-attention layers
...and also includes `timm` features common to many other architectures, including:
* stochastic depth
* gradient checkpointing
* layer-wise LR decay
* per-stage feature extraction
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 10.3
- GMACs: 2.4
- Activations (M): 10.5
- Image size: train = 256 x 256, test = 288 x 288
- **Papers:**
- ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks: https://arxiv.org/abs/1910.03151
- Aggregated Residual Transformations for Deep Neural Networks: https://arxiv.org/abs/1611.05431
- **Dataset:** ImageNet-1k
- **Original:** https://github.com/huggingface/pytorch-image-models
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('eca_resnext26ts.ch_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'eca_resnext26ts.ch_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 64, 128, 128])
# torch.Size([1, 256, 64, 64])
# torch.Size([1, 512, 32, 32])
# torch.Size([1, 1024, 16, 16])
# torch.Size([1, 2048, 8, 8])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'eca_resnext26ts.ch_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 2048, 8, 8) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
```bibtex
@InProceedings{wang2020eca,
title={ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks},
author={Qilong Wang, Banggu Wu, Pengfei Zhu, Peihua Li, Wangmeng Zuo and Qinghua Hu},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2020}
}
```
```bibtex
@article{Xie2016,
title={Aggregated Residual Transformations for Deep Neural Networks},
author={Saining Xie and Ross Girshick and Piotr Dollár and Zhuowen Tu and Kaiming He},
journal={arXiv preprint arXiv:1611.05431},
year={2016}
}
```
|
sd-dreambooth-library/EpicMixVirtualRealismv6
|
sd-dreambooth-library
| 2023-07-15T08:14:40Z | 361 | 4 |
diffusers
|
[
"diffusers",
"realism",
"stable diffusion",
"epicmix",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-04-15T08:40:05Z |
---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- realism
- stable diffusion
- epicmix
---
This is the Realism you've been PROBABLY not waiting for, but is getting anyways.
This is the branch of V3 and contains NONE OF V4 and Pastel. And none of V5
The only negative embeds used were contained in Nocrypt's notebook. Beyond that none were used.
We're moving this permanatley to SD Dreambooth Library, and absolve any ownership of it.
It's no longer on CivitAI, and details on what was created to make this are below:
# MIX BUCKET
<details>
<summary>THE BUCKET OF JOY</summary>
Epicv3 + Noise Offset
Babes 11 (NO VAE)
Cake Mix
Epic Portrait + Retro (two trained models i think of ours)
Plus Lucious Mix
</details>
|
laion/CLIP-ViT-L-14-CommonPool.XL.clip-s13B-b90K
|
laion
| 2023-04-26T01:40:57Z | 361 | 1 |
open_clip
|
[
"open_clip",
"zero-shot-image-classification",
"clip",
"license:mit",
"region:us"
] |
zero-shot-image-classification
| 2023-04-26T01:40:17Z |
---
tags:
- zero-shot-image-classification
- clip
library_name: open_clip
license: mit
---
# Model card for CLIP-ViT-L-14-CommonPool.XL.clip-s13B-b90K
|
Narrativaai/BioGPT-Large-finetuned-chatdoctor
|
Narrativaai
| 2023-05-03T13:18:16Z | 361 | 33 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"biogpt",
"text-generation",
"medical",
"doctor",
"chat",
"qa",
"question-answering",
"en",
"dataset:LinhDuong/chatdoctor-200k",
"arxiv:2303.14070",
"doi:10.57967/hf/0601",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-04-29T09:30:48Z |
---
license: apache-2.0
datasets:
- LinhDuong/chatdoctor-200k
language:
- en
pipeline_tag: text-generation
tags:
- medical
- doctor
- chat
- qa
- question-answering
thumbnail: https://huggingface.co/Narrativaai/BioGPT-Large-finetuned-chatdoctor/resolve/main/cdl.png
---
<div style="text-align:center;width:250px;height:250px;">
<img src="https://huggingface.co/Narrativaai/BioGPT-Large-finetuned-chatdoctor/resolve/main/cdl.png" alt="chat doctor bioGPT logo"">
</div>
# BioGPT (Large) 🧬 fine-tuned on ChatDoctor 🩺 for QA
[Microsoft's BioGPT Large](https://huggingface.co/microsoft/BioGPT-Large) fine-tuned on ChatDoctor dataset for Question Answering.
## Intended Use
This is just a research model and does **NOT** have to be used out of this scope.
## Limitations
TBA
## Model
[Microsoft's BioGPT Large](https://huggingface.co/microsoft/BioGPT-Large):
Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.
## Dataset
ChatDoctor-200K dataset is collected from this paper https://arxiv.org/pdf/2303.14070.pdf
The dataset is composed by:
- 100k real conversations between patients and doctors from HealthCareMagic.com [HealthCareMagic-100k](https://drive.google.com/file/d/1lyfqIwlLSClhgrCutWuEe_IACNq6XNUt/view?usp=sharing).
- 10k real conversations between patients and doctors from icliniq.com [icliniq-10k](https://drive.google.com/file/d/1ZKbqgYqWc7DJHs3N9TQYQVPdDQmZaClA/view?usp=sharing).
- 5k generated conversations between patients and physicians from ChatGPT [GenMedGPT-5k](https://drive.google.com/file/d/1nDTKZ3wZbZWTkFMBkxlamrzbNz0frugg/view?usp=sharing) and [disease database](https://github.com/Kent0n-Li/ChatDoctor/blob/main/format_dataset.csv)
## Usage
```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
model_id = "Narrativaai/BioGPT-Large-finetuned-chatdoctor"
tokenizer = AutoTokenizer.from_pretrained("microsoft/BioGPT-Large")
model = AutoModelForCausalLM.from_pretrained(model_id)
def answer_question(
prompt,
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=2,
**kwargs,
):
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to("cuda")
attention_mask = inputs["attention_mask"].to("cuda")
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
**kwargs,
)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=512,
eos_token_id=tokenizer.eos_token_id
)
s = generation_output.sequences[0]
output = tokenizer.decode(s, skip_special_tokens=True)
return output.split(" Response:")[1]
example_prompt = """
Below is an instruction that describes a task, paired with an input that provides further context.Write a response that appropriately completes the request.
### Instruction:
If you are a doctor, please answer the medical questions based on the patient's description.
### Input:
Hi i have sore lumps under the skin on my legs. they started on my left ankle and are approx 1 - 2cm diameter and are spreading up onto my thies. I am eating panadol night and anti allergy pills (Atarax). I have had this for about two weeks now. Please advise.
### Response:
"""
print(answer_question(example_prompt))
```
## Citation
```
@misc {narrativa_2023,
author = { {Narrativa} },
title = { BioGPT-Large-finetuned-chatdoctor (Revision 13764c0) },
year = 2023,
url = { https://huggingface.co/Narrativaai/BioGPT-Large-finetuned-chatdoctor },
doi = { 10.57967/hf/0601 },
publisher = { Hugging Face }
}
```
|
TheBloke/Uni-TianYan-70B-GGUF
|
TheBloke
| 2023-09-27T12:48:35Z | 361 | 10 |
transformers
|
[
"transformers",
"gguf",
"llama",
"en",
"arxiv:2306.02707",
"base_model:uni-tianyan/Uni-TianYan",
"license:llama2",
"text-generation-inference",
"region:us"
] | null | 2023-09-09T15:31:37Z |
---
language:
- en
license: llama2
library_name: transformers
model_name: Uni-TianYan (70B)
base_model: uni-tianyan/Uni-TianYan
inference: false
model_creator: Uni-TianYan
model_type: llama
prompt_template: 'Info on prompt template will be added shortly.
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Uni-TianYan (70B) - GGUF
- Model creator: [Uni-TianYan](https://huggingface.co/Uni-TianYan)
- Original model: [Uni-TianYan (70B)](https://huggingface.co/uni-tianyan/Uni-TianYan)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Uni-TianYan's Uni-TianYan (70B)](https://huggingface.co/uni-tianyan/Uni-TianYan).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Uni-TianYan-70B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Uni-TianYan-70B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Uni-TianYan-70B-GGUF)
* [Uni-TianYan's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/uni-tianyan/Uni-TianYan)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: TBC
```
Info on prompt template will be added shortly.
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [uni-tianyan-70b.Q2_K.gguf](https://huggingface.co/TheBloke/Uni-TianYan-70B-GGUF/blob/main/uni-tianyan-70b.Q2_K.gguf) | Q2_K | 2 | 29.28 GB| 31.78 GB | smallest, significant quality loss - not recommended for most purposes |
| [uni-tianyan-70b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Uni-TianYan-70B-GGUF/blob/main/uni-tianyan-70b.Q3_K_S.gguf) | Q3_K_S | 3 | 29.92 GB| 32.42 GB | very small, high quality loss |
| [uni-tianyan-70b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Uni-TianYan-70B-GGUF/blob/main/uni-tianyan-70b.Q3_K_M.gguf) | Q3_K_M | 3 | 33.19 GB| 35.69 GB | very small, high quality loss |
| [uni-tianyan-70b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Uni-TianYan-70B-GGUF/blob/main/uni-tianyan-70b.Q3_K_L.gguf) | Q3_K_L | 3 | 36.15 GB| 38.65 GB | small, substantial quality loss |
| [uni-tianyan-70b.Q4_0.gguf](https://huggingface.co/TheBloke/Uni-TianYan-70B-GGUF/blob/main/uni-tianyan-70b.Q4_0.gguf) | Q4_0 | 4 | 38.87 GB| 41.37 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [uni-tianyan-70b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Uni-TianYan-70B-GGUF/blob/main/uni-tianyan-70b.Q4_K_S.gguf) | Q4_K_S | 4 | 39.07 GB| 41.57 GB | small, greater quality loss |
| [uni-tianyan-70b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Uni-TianYan-70B-GGUF/blob/main/uni-tianyan-70b.Q4_K_M.gguf) | Q4_K_M | 4 | 41.42 GB| 43.92 GB | medium, balanced quality - recommended |
| [uni-tianyan-70b.Q5_0.gguf](https://huggingface.co/TheBloke/Uni-TianYan-70B-GGUF/blob/main/uni-tianyan-70b.Q5_0.gguf) | Q5_0 | 5 | 47.46 GB| 49.96 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [uni-tianyan-70b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Uni-TianYan-70B-GGUF/blob/main/uni-tianyan-70b.Q5_K_S.gguf) | Q5_K_S | 5 | 47.46 GB| 49.96 GB | large, low quality loss - recommended |
| [uni-tianyan-70b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Uni-TianYan-70B-GGUF/blob/main/uni-tianyan-70b.Q5_K_M.gguf) | Q5_K_M | 5 | 48.75 GB| 51.25 GB | large, very low quality loss - recommended |
| uni-tianyan-70b.Q6_K.gguf | Q6_K | 6 | 56.59 GB| 59.09 GB | very large, extremely low quality loss |
| uni-tianyan-70b.Q8_0.gguf | Q8_0 | 8 | 73.29 GB| 75.79 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
### Q6_K and Q8_0 files are split and require joining
**Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files.
<details>
<summary>Click for instructions regarding Q6_K and Q8_0 files</summary>
### q6_K
Please download:
* `uni-tianyan-70b.Q6_K.gguf-split-a`
* `uni-tianyan-70b.Q6_K.gguf-split-b`
### q8_0
Please download:
* `uni-tianyan-70b.Q8_0.gguf-split-a`
* `uni-tianyan-70b.Q8_0.gguf-split-b`
To join the files, do the following:
Linux and macOS:
```
cat uni-tianyan-70b.Q6_K.gguf-split-* > uni-tianyan-70b.Q6_K.gguf && rm uni-tianyan-70b.Q6_K.gguf-split-*
cat uni-tianyan-70b.Q8_0.gguf-split-* > uni-tianyan-70b.Q8_0.gguf && rm uni-tianyan-70b.Q8_0.gguf-split-*
```
Windows command line:
```
COPY /B uni-tianyan-70b.Q6_K.gguf-split-a + uni-tianyan-70b.Q6_K.gguf-split-b uni-tianyan-70b.Q6_K.gguf
del uni-tianyan-70b.Q6_K.gguf-split-a uni-tianyan-70b.Q6_K.gguf-split-b
COPY /B uni-tianyan-70b.Q8_0.gguf-split-a + uni-tianyan-70b.Q8_0.gguf-split-b uni-tianyan-70b.Q8_0.gguf
del uni-tianyan-70b.Q8_0.gguf-split-a uni-tianyan-70b.Q8_0.gguf-split-b
```
</details>
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Uni-TianYan-70B-GGUF and below it, a specific filename to download, such as: uni-tianyan-70b.q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub>=0.17.1
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Uni-TianYan-70B-GGUF uni-tianyan-70b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Uni-TianYan-70B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Uni-TianYan-70B-GGUF uni-tianyan-70b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m uni-tianyan-70b.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Info on prompt template will be added shortly."
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model from Python using ctransformers
#### First install the package
```bash
# Base ctransformers with no GPU acceleration
pip install ctransformers>=0.2.24
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]>=0.2.24
# Or with ROCm GPU acceleration
CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems
CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
```
#### Simple example code to load one of these GGUF models
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Uni-TianYan-70B-GGUF", model_file="uni-tianyan-70b.q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here's guides on using llama-cpp-python or ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Uni-TianYan's Uni-TianYan (70B)
# Uni-TianYan
### Introduction
Uni-TianYan is a finetuned model from LLaMA2.
<br>
### Dataset
TODO
### Training Details
TODO
### license disclaimer:
This model is bound by the license & usage restrictions of the original Llama-2 model. And comes with no warranty or gurantees of any kind.
<br>
### Evaluation
| Metric | Value |
|-----------------------|-------|
| ARC (25-shot) | 72.10 |
| HellaSwag (10-shot) | 87.40 |
| MMLU (5-shot) | 69.91 |
| TruthfulQA (0-shot) | 65.81 |
| Avg. | 73.81 |
**We notice that MMLU is a more common metric and trying to reconstruct dataset.**
<br>
### Our Other Projects:
* [fangloveskari/ORCA_LLaMA_70B_QLoRA](fangloveskari/ORCA_LLaMA_70B_QLoRA)
* [fangloveskari/Platypus_QLoRA_LLaMA_70b](fangloveskari/Platypus_QLoRA_LLaMA_70b)
### Limitations & Biases:
Llama2 and fine-tuned variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2 and any fine-tuned varient's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2 variants, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at https://ai.meta.com/llama/responsible-use-guide/
<br>
### Citiation:
Please kindly cite using the following BibTeX:
```bibtex
@article{platypus2023,
title={Platypus: Quick, Cheap, and Powerful Refinement of LLMs},
author={Ariel N. Lee and Cole J. Hunter and Nataniel Ruiz},
booktitle={arXiv preprint arxiv:2308.07317},
year={2023}
}
```
```
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@software{touvron2023llama2,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava,
Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller,
Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann,
Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov,
Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith,
Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu , Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan,
Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom},
year={2023}
}
```
<!-- original-model-card end -->
|
fnlp/SpeechGPT-7B-cm
|
fnlp
| 2023-09-15T11:06:00Z | 361 | 6 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:2305.11000",
"arxiv:2308.16692",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-09-14T13:43:16Z |
# SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities
<a href='https://0nutation.github.io/SpeechGPT.github.io/'><img src='https://img.shields.io/badge/Project-Page-Green'></a> <a href='https://arxiv.org/abs/2305.11000'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a> [](https://huggingface.co/datasets/fnlp/SpeechInstruct)
<p align="center">
<img src="Pictures/logo.png" width="20%"> <br>
</p>
## Introduction
SpeechGPT is a large language model with **intrinsic cross-modal conversational abilities**, capable of perceiving and generating multi-model content following human instructions. With discrete speech representations, we first construct **SpeechInstruct**, a large-scale cross-modal speech instruction dataset. Additionally, we employ a three-stage training strategy that includes **modality-adaptation pre-training**, **cross-modal instruction fine-tuning**, and **chain-of-modality instruction fine-tuning**. The experimental results demonstrate that SpeechGPT has an impressive capacity to follow multi-modal human instructions and highlight the potential of handling multiple modalities with one model. <br>
SpeechGPT demos are shown in our [project page](https://0nutation.github.io/SpeechGPT.github.io/). As shown in the demos, SpeechGPT has strong cross-modal instruction-following ability and spoken dialogue ability. SpeechGPT can be **a talking encyclopedia, your personal assistant, your chat partner, a poet, a psychologist and your educational assistant**...
<br>
<br>
<p align="center">
<img src="Pictures/speechgpt-intro.png" width="95%"> <br>
SpeechGPT’s capabilities to tackle multiple cross-modal tasks
</p>
<br>
<br>
<p align="center">
<img src="Pictures/SpeechGPT-main.png" width="95%"> <br>
Left: SpeechInstruct construction process. Right: SpeechGPT model structure
</p>
## Release
- **[2023/9/15]** We released SpeechGPT code and checkpoints and SpeechInstruct dataset.
- **[2023/9/1]** We proposed **SpeechTokenizer: Unified Speech Tokenizer for Speech Language Models**. We released the code and checkpoints of SpeechTokenizer. Checkout the [paper](https://arxiv.org/abs/2308.16692), [demo](https://0nutation.github.io/SpeechTokenizer.github.io/) and [github](https://github.com/ZhangXInFD/SpeechTokenizer).
- **[2023/5/18]** We released **SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities**. We propose SpeechGPT, the first multi-modal LLM capable of perceiving and generating multi-modal contents following multi-modal human instructions. Checkout the [paper](https://arxiv.org/abs/2305.11000) and [demo](https://0nutation.github.io/SpeechGPT.github.io/).
## Table of Contents
- [Open-source list](#open-source-list)
- [Talk with SpeechGPT](#talk-with-speechgpt)
- [Train SpeechGPT](#train-speechgpt)
- [Finetune SpeechGPT](#finetune-speechgpt)
## Open-source list
### Models
- [**SpeechGPT-7B-ma**](https://huggingface.co/fnlp/SpeechGPT-7B-ma): The model obtained after the first-stage modality-adaptation pre-training, which was initialized with LLaMA-7B and further pre-trained on LibriLight speech units.
- [**SpeechGPT-7B-cm**](https://huggingface.co/fnlp/SpeechGPT-7B-cm): The model obtained after the second-stage cross-modal instruction finetuning, which was initialized with SpeechGPT-7B-ma and further finetuned on SpeechInstruct Cross-Modal Instruction set. This is a powerful foundational model that aligns speech and text.
- [**SpeechGPT-7B-com**](https://huggingface.co/fnlp/SpeechGPT-7B-com): The model obtained after the third-stage chain-of-modality instruction finetuning, which was initialized with SpeechGPT-7B-cm and further lora-finetuned on SpeechInstruct Chain-of-Modality Instruction set. This is an adapter-model of SpeechGPT-7B-cm for spoken dialogue.
### Datasets
- [**SpeechInstruct-cross-modal**](https://huggingface.co/datasets/fnlp/SpeechInstruct/resolve/main/cross_modal_instruction.jsonl): The cross-modal instruction set, about 9 million unit-text data pairs tokenzed by mHuBERT from large-scale English ASR datasets. data format:
- [**SpeechInstruct-chain-of-modality**](https://huggingface.co/datasets/fnlp/SpeechInstruct/resolve/main/chain_of_modality_instruction.jsonl): The chain-of-thought style instructions for four input-output formats, namely Speech Instruction-Speech Response, Speech Instruction-Text Response, Text Instruction-Speech Response, and Text Instruction-Text Response.
SpeechInstruct-cross-modal data format:
```
[
{
"prefix": "You are an AI assistant whose name is SpeechGPT.\n- SpeechGPT is a intrinsic cross-modal conversational language model that is developed by Fudan University. SpeechGPT can understand and communicate fluently with human through speech or text chosen by the user.\n- It can perceive cross-modal inputs and generate cross-modal outputs.\n",
"plain_text": "[Human]: Try to speak out this sentence, please. This is input: The alchemist rode in front, with the falcon on his shoulder.<eoh> [SpeechGPT]: <sosp><661><588><604><157><596><499><596><106><596><189><63><189><665><991><162><202><393><946><327><905><907><597><660><351><557><794><788><59><754><12><977><877><333><873><835><67><940><118><686><613><169><72><644><553><535><935><101><741><384><173><894><787><380><787><196><555><721><944><250><56><812><222><915><143><390><479><330><435><647><246><650><816><325><506><686><208><613><417><755><193><411><452><111><735><6><735><63><665><644><991><535><271><333><196><918><29><202><393><946><734><390><479><330><776><167><761><907><597><660><351><557><794><75><788><15><366><896><627><168><654><659><177><183><609><710><187><493><361><470><821><59><56><198><912><742><840><431><531><76><668><576><803><791><380><660><325><801><549><366><377><164><309><584><605><193><71><39><eosp><eoa> "
},
]
```
SpeechInstruct-chain-of-modality data format:
```
[
{
"prefix": "You are an AI assistant whose name is SpeechGPT.\n- SpeechGPT is a intrinsic cross-modal conversational language model that is developed by Fudan University. SpeechGPT can understand and communicate fluently with human through speech or text chosen by the user.\n- It can perceive cross-modal inputs and generate cross-modal outputs.\n",
"plain_text": "[Human]: <sosp><661><987><511><732><951><997><111><982><189><63><665><991><535><101><741><173><945><944><503><641><124><565><734><870><290><978><833><238><761><907><430><901><185><403><557><244><583><788><663><969><896><627><143><515><663><969><660><691><251><412><260><41><740><677><253><380><382><268><506><876><417><755><16><819><80><651><80><651><80><987><588><eosp><eoh>. [SpeechGPT]: What is a bad term for poop?; [ta] A bad term for poop is excrement. It is usually used as a polite way to refer to fecal waste.; [ua] <sosp><497><63><264><644><710><823><565><577><154><331><384><173><945><29><244><326><583><728><576><663><969><896><627><143><38><515><663><24><382><251><676><412><260><41><740><677><253><382><268><876><233><878><609><389><771><865><641><124><878><609><423><384><879><487><219><522><589><337><126><119><663><748><12><671><877><377><385><902><819><619><842><419><997><829><111><666><42><277><63><665><644><389><771><685><437><641><124><258><436><139><340><11><59><518><56><948><86><258><436><139><340><347><376><940><118><944><878><173><641><124><362><734><179><961><931><878><609><423><384><879><219><522><866><337><243><935><101><741><822><89><194><630><86><555><105><79><868><220><156><824><998><870><390><422><330><776><663><969><523><105><79><799><220><357><390><479><422><330><776><485><165><86><501><119><716><205><521><787><935><101><741><89><194><664><835><67><940><118><613><417><755><902><415><772><497><eosp><eoa>."
},
]
```
## Talk with SpeechGPT
**Due to limited training data and resources, the performance of the open-source SpeechGPT is currently not optimal. Problems such as task recognition errors and inaccuracies in speech recognition may occur. As this project is primarily an exploration in research, we have not increased the amount of pretraining and sft data or training steps to enhance performance. Our hope is that SpeechGPT can serve as a foundational model to encourage research and exploration in the field of speech language models.**
### Installation
```bash
git clone https://github.com/0nutation/SpeechGPT
cd SpeechGPT
conda create --name SpeechGPT python=3.8
conda activate SpeechGPT
pip install -r requirements.txt
```
### Download
To talk with SpeechGPT, you should download [SpeechGPT-7B-cm](https://huggingface.co/fnlp/SpeechGPT-7B-cm) and [SpeechGPT-7B-com](https://huggingface.co/fnlp/SpeechGPT-7B-com) locally.
You should download mHuBERT model to ```utils/speech2unit/```. Please see [Speech2unit](https://github.com/0nutation/SpeechGPT/utils/speech2unit/README_DATA.md) for details.
```bash
s2u_dir="uitls/speech2unit"
cd ${s2u_dir}
wget https://dl.fbaipublicfiles.com/hubert/mhubert_base_vp_en_es_fr_it3.pt
wget https://dl.fbaipublicfiles.com/hubert/mhubert_base_vp_en_es_fr_it3_L11_km1000.bin
```
You should download the unit-vocoder to ```utils/vocoder/```. Please see [vocoder](https://github.com/0nutation/SpeechGPT/utils/vocoder/README_DATA.md) for details.
```bash
vocoder_dir="utils/vocoder/"
cd ${vocoder_dir}
wget https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/vocoder/code_hifigan/mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj/config.json -O config.json
wget https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/vocoder/code_hifigan/mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj/g_00500000 -O vocoder.pt
```
### CLI Inference
```bash
python3 speechgpt/src/infer/cli_infer.py \
--model-name-or-path "path/to/SpeechGPT-7B-cm" \
--lora-weights "path/to/SpeechGPT-7B-com" \
--s2u-dir "${s2u_dir}" \
--vocoder-dir "${vocoder_dir} \
--output-dir "output"
```
**Notes**
For speech input, you can provide the path to the audio file. For ASR or TTS tasks, you must prefix the speech or text with ```this is input: ```, otherwise, it may be recognized incorrectly.
The speech response will be saved to a ```.wav``` file, and detailed responses will be saved in a JSON file. The paths to these files will be indicated in the response.
Here are some examples of talking with SpeechGPT:
**Textual dialogue example**
```
Please talk with SpeechGPT:
Who is Lebron James?
Response:
Lebron James is an American professional basketball player for the Los Angeles Lakers of the National Basketball Association (NBA). He is considered one of the greatest basketball players of all time and is known for his athleticism, scoring ability, and leadership skills. He is a four-time NBA MVP, a 14-time NBA All-Star, a 13-time All-NBA selection, and a two-time Olympic gold medalist.
Response json is saved in output/responses.json
```
**Spoken dialogue example**
```
Please talk with SpeechGPT:
prompts/0.wav
Transcript: What are the main causes of climate change?
Text response: The main causes of climate change are human activities such as burning fossil fuels, deforestation, and agricultural practices. These activities release greenhouse gases, like carbon dioxide and Methane, into the atmosphere which trap heat and cause the Earth's temperature to rise.
Speech repsonse is saved in output/wav/answer_0.wav
Response json is saved in output/responses.json
```
**ASR example**
```
Please talk with SpeechGPT:
Recognize this speech, this is input: prompts/1.wav
Response:
today is a sunny day.
Response json is saved in output/responses.json
```
**TTS example**
```
Please talk with SpeechGPT:
Read this sentence aloud, this is input: Today is a sunny day.
Response:
<sosp> <661> <987> <520> <982> <681> <982> <681> <982> <681> <982> <681> <982> <189> <63> <662> <79> <868> <220> <196> <166> <549> <822> <89> <194> <633> <14> <855> <183> <609> <389> <771> <865> <641> <124> <362> <734> <742> <98> <519> <26> <204> <280> <668> <167> <104> <650> <179> <961> <428> <950> <82> <165> <196> <166> <549> <822> <89> <194> <458> <726> <603> <819> <651> <133> <651> <133> <186> <133> <186> <133> <186> <511> <186> <511> <eosp>
Speech repsonse is saved in output/wav/answer_1.wav
Response json is saved in output/responses.json
```
### Gradio Web UI
```bash
python3 speechgpt/src/infer/web_infer.py \
--model-name-or-path "path/to/SpeechGPT-7B-cm" \
--lora-weights "path/to/SpeechGPT-7B-com" \
--s2u-dir "${s2u_dir}" \
--vocoder-dir "${vocoder_dir}" \
--output-dir "output/"
```
## Train SpeechGPT
### Stage1: Modality-adaptation Pre-training
First, utilize mHuBERT for discretizing the LibriLight dataset to obtain discrete unit sequences for stage1 training. You can refer to the data processing methods in [Speech2unit](https://github.com/0nutation/SpeechGPT/utils/speech2unit/README_DATA.md).
Second, divide the discrete units into a training set and a development set, and save them in the following format in the files ```data/stage1/train.txt``` and ```data/stage1/dev.txt```:
```
<sosp><189><247><922><991><821><258><485><974><284><466><969><523><196><202><881><331><822><853><432><32><742><98><519><26><204><280><576><384><879><901><555><944><366><641><124><362><734><156><824><462><761><907><430><81><597><716><205><521><470><821><677><355><483><641><124><243><290><978><82><620><915><470><821><576><384><466><398><212><455><931><579><969><778><45><914><445><469><576><803><6><803><791><377><506><835><67><940><613><417><755><237><224><452><121><736><eosp>
<sosp><300><189><63><6><665><991><881><331><6><384><879><945><29><244><583><874><655><837><81><627><545><124><337><850><412><213><260><41><740><797><211><488><961><428><6><196><555><944><873><32><683><700><955><812><328><915><166><250><56><903><86><233><479><330><776><167><104><764><259><921><366><663><432><431><531><976><314><822><89><664><377><611><479><417><eosp>
<sosp><189><735><991><39><565><734><32><742><98><519><26><204><280><668><576><803><791><660><555><233><787><101><741><466><969><219><107><459><491><556><384><733><219><501><445><137><910><523><793><50><981><230><534><321><948><86><116><281><62><462><104><70><918><743><15><212><455><143><836><173><944><958><390><422><66><776><258><436><139><663><432><742><98><519><589><243><126><260><41><444><6><655><764><969><219><727><85><297><700><362><493><6><493><361><393><946><6><470><821><246><655><837><81><969><916><584><819><544><452><158><452><736><eosp>
```
Third, you should download LLaMA 7B(HuggingFace) to ```llama/hf/7B```.
Now you can start stage1 training:
To perform distributed training, you must specify the correct values for ```NNODE```, ```NODE_RANK```, ```MASTER_ADDR```, and ```MASTER_PORT```.
```bash
bash scripts/ma_pretrain.sh ${NNODE} ${NODE_RANK} ${MASTER_ADDR} ${MASTER_PORT}
```
### Stage 2: Cross-modal Instruction Finetuning
You should download [SpeechInstruct Cross-modal Instruction set](https://huggingface.co/datasets/fnlp/SpeechInstruct/resolve/main/cross_modal_instruction.jsonl) to ```data/stage2/```.
If you want to skip stage1 training, you can download ```SpeechGPT-7B-ma``` to ```output/stage1/```.
Now you can start stage2 training:
To perform distributed training, you must specify the correct values for ```NNODE```, ```NODE_RANK```, ```MASTER_ADDR```, and ```MASTER_PORT```.
```bash
bash scripts/cm_sft.sh ${NNODE} ${NODE_RANK} ${MASTER_ADDR} ${MASTER_PORT}
```
### Stage 3: Chain-of-modality Instruction Finetuning
You should download [SpeechInstruct Chain-of-modality Instruction set](https://huggingface.co/datasets/fnlp/SpeechInstruct/resolve/main/chain_of_modality_instruction.jsonl) to ```data/stage3/```.
If you want to skip stage1 and stage2, you can download ```SpeechGPT-7B-cm``` to ```output/stage2/```.
Now you can start stage3 training:
To perform distributed training, you must specify the correct values for ```NNODE```, ```NODE_RANK```, ```MASTER_ADDR```, and ```MASTER_PORT```.
```bash
bash scripts/com_sft.sh ${NNODE} ${NODE_RANK} ${MASTER_ADDR} ${MASTER_PORT}
```
## Finetune SpeechGPT
```Speech-7B-cm``` is a foundational model with strong alignment between speech and text. We encourage fine-tuning SpeechGPT based on this model.
Step1: prepare your data following the format in [SpeechInstruct Cross-modal Instruction set](https://huggingface.co/datasets/fnlp/SpeechInstruct/resolve/main/cross_modal_instruction.jsonl).
Step2: download [SpeechGPT-7B-cm](https://huggingface.co/fnlp/SpeechGPT-7B-cm) locally.
Step3: Modify the ```METAROOT```, ```DATAROOT```, and ```OUTROOT``` parameters in the ```scripts/cm_sft.sh``` script to yours and then run it. For LoRA fine-tuning, update the ```METAROOT```, ```DATAROOT```, and ```OUTROOT``` parameters in the ```scripts/com_sft.sh``` script and run it.
## Acknowledgements
- [MOSS](https://github.com/OpenLMLab/MOSS): We use moss-sft-002-data.
- [stanford_alpaca](https://github.com/tatsu-lab/stanford_alpaca):The codebase we built upon.
## Citation
If you find SpeechGPT useful for your research and applications, please cite using the BibTex:
```
@misc{zhang2023speechgpt,
title={SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities},
author={Dong Zhang and Shimin Li and Xin Zhang and Jun Zhan and Pengyu Wang and Yaqian Zhou and Xipeng Qiu},
year={2023},
eprint={2305.11000},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
kornwtp/SCT-KD-model-XLMR
|
kornwtp
| 2024-03-07T01:23:11Z | 361 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:2311.03228",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-03-01T03:49:09Z |
---
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {kornwtp/SCT-KD-model-XLMR}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
We use SCT Distillation [here](https://arxiv.org/abs/2311.03228) and training the model with Thai Wikipedia [here](https://github.com/PyThaiNLP/ThaiWiki-clean/releases/tag/20210620?fbclid=IwAR1YcmZkb-xd1ibTWCJOcu98_FQ5x3ioZaGW1ME-VHy9fAQLhEr5tXTJygA)
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["กลุ่มผู้ชายเล่นฟุตบอลบนชายหาด", "กลุ่มเด็กชายกำลังเล่นฟุตบอลบนชายหาด"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
|
sail/Sailor-0.5B-Chat-gguf
|
sail
| 2024-04-05T05:48:32Z | 361 | 3 | null |
[
"gguf",
"multilingual",
"sea",
"sailor",
"sft",
"chat",
"instruction",
"en",
"zh",
"id",
"th",
"vi",
"ms",
"lo",
"dataset:cerebras/SlimPajama-627B",
"dataset:Skywork/SkyPile-150B",
"dataset:allenai/MADLAD-400",
"dataset:cc100",
"dataset:CohereForAI/aya_dataset",
"dataset:CohereForAI/aya_collection",
"dataset:Open-Orca/OpenOrca",
"arxiv:2404.03608",
"base_model:sail/Sailor-0.5B",
"license:apache-2.0",
"region:us"
] | null | 2024-03-03T05:22:43Z |
---
language:
- en
- zh
- id
- th
- vi
- ms
- lo
datasets:
- cerebras/SlimPajama-627B
- Skywork/SkyPile-150B
- allenai/MADLAD-400
- cc100
- CohereForAI/aya_dataset
- CohereForAI/aya_collection
- Open-Orca/OpenOrca
tags:
- multilingual
- sea
- sailor
- sft
- chat
- instruction
license: apache-2.0
base_model: sail/Sailor-0.5B
---
<div align="center">
<img src="banner_sailor.jpg" width="700"/>
</div>
Sailor is a suite of Open Language Models tailored for South-East Asia (SEA), focusing on languages such as 🇮🇩Indonesian, 🇹🇭Thai, 🇻🇳Vietnamese, 🇲🇾Malay, and 🇱🇦Lao.
Developed with careful data curation, Sailor models are designed to understand and generate text across diverse linguistic landscapes of SEA region.
Built from [Qwen 1.5](https://huggingface.co/collections/Qwen/qwen15-65c0a2f577b1ecb76d786524) , Sailor encompasses models of varying sizes, spanning from 0.5B to 7B versions for different requirements.
We further fine-tune the base model with open-source datasets to get instruction-tuned models, namedly Sailor-Chat.
Benchmarking results demonstrate Sailor's proficiency in tasks such as question answering, commonsense reasoning, and other tasks in SEA languages.
> The logo was generated by MidJourney
## Model Summary
- **Model Collections:** [Base Model & Chat Model](https://huggingface.co/collections/sail/sailor-65e19a749f978976f1959825)
- **Project Website:** [sailorllm.github.io](https://sailorllm.github.io/)
- **Codebase:** [github.com/sail-sg/sailor-llm](https://github.com/sail-sg/sailor-llm)
- **Technical Report:** [arxiv.org/pdf/2404.03608.pdf](https://arxiv.org/pdf/2404.03608.pdf)
## Training details
Sailor is crafted by continually pre-training from language models like the remarkable Qwen 1.5 models, which already has a great performance on SEA languages.
The pre-training corpus heavily leverages the publicly available corpus, including
[SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B),
[SkyPile](https://huggingface.co/datasets/Skywork/SkyPile-150B),
[CC100](https://huggingface.co/datasets/cc100) and [MADLAD-400](https://huggingface.co/datasets/allenai/MADLAD-400).
The instruction tuning corpus are all publicly available including
[aya_collection](https://huggingface.co/datasets/CohereForAI/aya_collection),
[aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset),
[OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca).
By employing aggressive data deduplication and careful data cleaning on the collected corpus, we have attained a high-quality dataset spanning various languages.
Through systematic experiments to determine the weights of different languages, Sailor models undergo training from 200B to 400B tokens, tailored to different model sizes.
The approach boosts their performance on SEA languages while maintaining proficiency in English and Chinese without significant compromise.
Finally, we continually pre-train the Qwen1.5-0.5B model with 400 Billion tokens, and other models with 200 Billion tokens to obtain the Sailor models.
### GGUF model list
As for this 0.5B model, we only recommend the 8-bit and 16-bit qquf models for most purpose❗
| Name | Quant method | Bits | Size | Use case |
| ------------------------------------------------------------ | ------------ | ---- | ------- | ------------------------------------------------------------ |
| [ggml-model-Q2_K.gguf](https://huggingface.co/sail/Sailor-0.5B-Chat-gguf/blob/main/ggml-model-Q2_K.gguf) | Q2_K | 2 | 298 MB | smallest, significant quality loss |
| [ggml-model-Q3_K_L.gguf](https://huggingface.co/sail/Sailor-0.5B-Chat-gguf/blob/main/ggml-model-Q3_K_L.gguf) | Q3_K_L | 3 | 364 MB | very small, substantial quality loss |
| [ggml-model-Q3_K_M.gguf](https://huggingface.co/sail/Sailor-0.5B-Chat-gguf/blob/main/ggml-model-Q3_K_M.gguf) | Q3_K_M | 3 | 350 MB | very small, balanced quality |
| [ggml-model-Q3_K_S.gguf](https://huggingface.co/sail/Sailor-0.5B-Chat-gguf/blob/main/ggml-model-Q3_K_S.gguf) | Q3_K_S | 3 | 333 MB | very small, high quality loss |
| [ggml-model-Q4_K_M.gguf](https://huggingface.co/sail/Sailor-0.5B-Chat-gguf/blob/main/ggml-model-Q4_K_M.gguf) | Q4_K_M | 4 | 407 MB | very small, balanced quality |
| [ggml-model-Q4_K_S.gguf](https://huggingface.co/sail/Sailor-0.5B-Chat-gguf/blob/main/ggml-model-Q4_K_S.gguf) | Q4_K_S | 4 | 397 MB | very small, greater quality loss |
| [ggml-model-Q5_K_M.gguf](https://huggingface.co/sail/Sailor-0.5B-Chat-gguf/blob/main/ggml-model-Q5_K_M.gguf) | Q5_K_M | 5 | 459 MB | very small, balanced quality |
| [ggml-model-Q5_K_S.gguf](https://huggingface.co/sail/Sailor-0.5B-Chat-gguf/blob/main/ggml-model-Q5_K_S.gguf) | Q5_K_S | 5 | 453 MB | very small, very low quality loss |
| [ggml-model-Q6_K.gguf](https://huggingface.co/sail/Sailor-0.5B-Chat-gguf/blob/main/ggml-model-Q6_K.gguf) | Q6_K | 6 | 515 MB | very small, extremely low quality loss |
| [ggml-model-Q8_0.gguf](https://huggingface.co/sail/Sailor-0.5B-Chat-gguf/blob/main/ggml-model-Q8_0.gguf) | Q8_0 | 8 | 665 MB | very small, extremely low quality loss |
| [ggml-model-f16.gguf](https://huggingface.co/sail/Sailor-0.5B-Chat-gguf/blob/main/ggml-model-f16.gguf) | f16 | 16 | 1.25 GB | small, no quality loss |
### How to run with `llama.cpp`
```shell
# install llama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
make
pip install -r requirements.txt
# generate with llama.cpp
./main -ngl 24 -m ggml-model-Q4_K_M.gguf -p "<|im_start|>question\nCara memanggang ikan?\n<|im_start|>answer\n" --temp 0.7 --repeat_penalty 1.1 -n 400 -e
```
> Change `-ngl 24` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
### How to run with `llama-cpp-python`
```shell
pip install llama-cpp-python
```
```python
import llama_cpp
import llama_cpp.llama_tokenizer
# load model
llama = llama_cpp.Llama.from_pretrained(
repo_id="sail/Sailor-4B-Chat-gguf",
filename="ggml-model-Q4_K_M.gguf",
tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained("sail/Sailor-4B-Chat"),
n_gpu_layers=40,
n_threads=8,
verbose=False,
)
system_role= 'system'
user_role = 'question'
assistant_role = "answer"
system_prompt= \
'You are an AI assistant named Sailor created by Sea AI Lab. \
Your answer should be friendly, unbiased, faithful, informative and detailed.'
system_prompt = f"<|im_start|>{system_role}\n{system_prompt}<|im_end|>"
# inference example
output = llama(
system_prompt + '\n' + f"<|im_start|>{user_role}\nCara memanggang ikan?\n<|im_start|>{assistant_role}\n",
max_tokens=256,
temperature=0.7,
top_p=0.75,
top_k=60,
stop=["<|im_end|>", "<|endoftext|>"]
)
print(output['choices'][0]['text'])
```
### How to build demo
Install `llama-cpp-python` and `gradio`, then run [script](https://github.com/sail-sg/sailor-llm/blob/main/demo/llamacpp_demo.py).
# License
Sailor is distributed under the terms of the Apache License 2.0.
No restrict on the research and the commercial use, but should comply with the [Qwen License](https://huggingface.co/Qwen/Qwen1.5-1.8B/blob/main/LICENSE).
## Citation
If you find sailor useful, please cite our work as follows:
```
@misc{dou2024sailor,
title={Sailor: Open Language Models for South-East Asia},
author={Longxu Dou and Qian Liu and Guangtao Zeng and Jia Guo and Jiahui Zhou and Wei Lu and Min Lin},
year={2024},
eprint={2404.03608},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
# Contact Us
If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]) or [[email protected]](mailto:[email protected]).
|
v-urushkin/SyntheticGPT2-small
|
v-urushkin
| 2024-06-02T09:06:45Z | 361 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"ru",
"dataset:tay-yozhik/SyntheticTexts",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2024-04-27T11:37:39Z |
---
language:
- ru
library_name: transformers
datasets:
- tay-yozhik/SyntheticTexts
---
# 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]
|
RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf
|
RichardErkhov
| 2024-05-16T18:47:17Z | 361 | 0 | null |
[
"gguf",
"region:us"
] | null | 2024-05-16T17:26:52Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
juud-Mistral-7B - GGUF
- Model creator: https://huggingface.co/AIJUUD/
- Original model: https://huggingface.co/AIJUUD/juud-Mistral-7B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [juud-Mistral-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.Q2_K.gguf) | Q2_K | 2.53GB |
| [juud-Mistral-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
| [juud-Mistral-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.IQ3_S.gguf) | IQ3_S | 2.96GB |
| [juud-Mistral-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
| [juud-Mistral-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [juud-Mistral-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.Q3_K.gguf) | Q3_K | 3.28GB |
| [juud-Mistral-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
| [juud-Mistral-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
| [juud-Mistral-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
| [juud-Mistral-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.Q4_0.gguf) | Q4_0 | 3.83GB |
| [juud-Mistral-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
| [juud-Mistral-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
| [juud-Mistral-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.Q4_K.gguf) | Q4_K | 4.07GB |
| [juud-Mistral-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
| [juud-Mistral-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.Q4_1.gguf) | Q4_1 | 4.24GB |
| [juud-Mistral-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.Q5_0.gguf) | Q5_0 | 4.65GB |
| [juud-Mistral-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
| [juud-Mistral-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.Q5_K.gguf) | Q5_K | 4.78GB |
| [juud-Mistral-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
| [juud-Mistral-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.Q5_1.gguf) | Q5_1 | 5.07GB |
| [juud-Mistral-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.Q6_K.gguf) | Q6_K | 5.53GB |
| [juud-Mistral-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/AIJUUD_-_juud-Mistral-7B-gguf/blob/main/juud-Mistral-7B.Q8_0.gguf) | Q8_0 | 7.17GB |
Original model description:
---
library_name: transformers
license: apache-2.0
---
# Model Card for Model ID
Welcome To BNK AI.
You now are seeing Initiative status of Huge AI Team like mackintosh Garage company
Though beginning seems humble future will be prosperous!
If you have a question, feel free to
contact to [email protected]
|
Monor/StructLM-7B-Mistral-gguf
|
Monor
| 2024-05-20T10:23:02Z | 361 | 1 | null |
[
"gguf",
"license:apache-2.0",
"region:us"
] | null | 2024-05-20T04:56:50Z |
---
license: apache-2.0
---
## Introduce
Quantizing the [TIGER-Lab/StructLM-7B-Mistral](https://huggingface.co/TIGER-Lab/StructLM-7B-Mistral) to f16, q2, q3, q4, q5, q6 and q8 with Llama.cpp.
## Prompt Template
```
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{instruction}
{input}
{question}
### Response:
```
IMPORTANT!! - For more details, check out [StructLM-7B-Mistral](https://huggingface.co/TIGER-Lab/StructLM-7B-Mistral).
|
MaziyarPanahi/MergerixStrangemerges_32-7B-GGUF
|
MaziyarPanahi
| 2024-05-22T03:29:22Z | 361 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:Gille/StrangeMerges_32-7B-slerp",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:automerger/MergerixStrangemerges_32-7B"
] |
text-generation
| 2024-05-22T02:57:32Z |
---
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- merge
- mergekit
- lazymergekit
- automerger
- base_model:Gille/StrangeMerges_32-7B-slerp
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
- text-generation
model_name: MergerixStrangemerges_32-7B-GGUF
base_model: automerger/MergerixStrangemerges_32-7B
inference: false
model_creator: automerger
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/MergerixStrangemerges_32-7B-GGUF](https://huggingface.co/MaziyarPanahi/MergerixStrangemerges_32-7B-GGUF)
- Model creator: [automerger](https://huggingface.co/automerger)
- Original model: [automerger/MergerixStrangemerges_32-7B](https://huggingface.co/automerger/MergerixStrangemerges_32-7B)
## Description
[MaziyarPanahi/MergerixStrangemerges_32-7B-GGUF](https://huggingface.co/MaziyarPanahi/MergerixStrangemerges_32-7B-GGUF) contains GGUF format model files for [automerger/MergerixStrangemerges_32-7B](https://huggingface.co/automerger/MergerixStrangemerges_32-7B).
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
|
RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf
|
RichardErkhov
| 2024-05-30T09:40:21Z | 361 | 0 | null |
[
"gguf",
"region:us"
] | null | 2024-05-30T06:39:45Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Astrid-Mistral-7B - GGUF
- Model creator: https://huggingface.co/PAIXAI/
- Original model: https://huggingface.co/PAIXAI/Astrid-Mistral-7B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Astrid-Mistral-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.Q2_K.gguf) | Q2_K | 2.53GB |
| [Astrid-Mistral-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
| [Astrid-Mistral-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.IQ3_S.gguf) | IQ3_S | 2.96GB |
| [Astrid-Mistral-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
| [Astrid-Mistral-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [Astrid-Mistral-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.Q3_K.gguf) | Q3_K | 3.28GB |
| [Astrid-Mistral-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
| [Astrid-Mistral-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
| [Astrid-Mistral-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
| [Astrid-Mistral-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.Q4_0.gguf) | Q4_0 | 3.83GB |
| [Astrid-Mistral-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
| [Astrid-Mistral-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
| [Astrid-Mistral-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.Q4_K.gguf) | Q4_K | 4.07GB |
| [Astrid-Mistral-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
| [Astrid-Mistral-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.Q4_1.gguf) | Q4_1 | 4.24GB |
| [Astrid-Mistral-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.Q5_0.gguf) | Q5_0 | 4.65GB |
| [Astrid-Mistral-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
| [Astrid-Mistral-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.Q5_K.gguf) | Q5_K | 4.78GB |
| [Astrid-Mistral-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
| [Astrid-Mistral-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.Q5_1.gguf) | Q5_1 | 5.07GB |
| [Astrid-Mistral-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.Q6_K.gguf) | Q6_K | 5.53GB |
| [Astrid-Mistral-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/PAIXAI_-_Astrid-Mistral-7B-gguf/blob/main/Astrid-Mistral-7B.Q8_0.gguf) | Q8_0 | 7.17GB |
Original model description:
---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- PAIX.Cloud
inference: true
thumbnail: >-
https://static.wixstatic.com/media/bdee4e_8aa5cefc86024bc88f7e20e3e19d9ff3~mv2.png/v1/fill/w_192%2Ch_192%2Clg_1%2Cusm_0.66_1.00_0.01/bdee4e_8aa5cefc86024bc88f7e20e3e19d9ff3~mv2.png
license: apache-2.0
---
# Model Card
## Summary
- Base model: [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
This model, Astrid-7B-Assistant is a Mistral-7B base model for causal language modeling, designed to generate human-like text.
It's part of our mission to make AI technology accessible to everyone, focusing on personalization, data privacy, and transparent AI governance.
Trained in English, it's a versatile tool for a variety of applications.
This model is one of the many models available on our platform, and we currently have a 1B and 7B open-source model.
This model was trained by [PAIX.Cloud](https://www.paix.cloud/).
- Wait list: [Wait List](https://www.paix.cloud/join-waitlist)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.34.0
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCES_TOKEN>)
```
- Or directly pass your <ACCES_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="PAIXAI/Astrid-Mistral-7B",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
token=True,
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<|im_end|><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"PAIXAI/Astrid-Mistral-7B",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"PAIXAI/Astrid-Mistral-7B",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "PAIXAI/Astrid-Mistral-7B" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<|im_end|><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
MistralForCausalLM(
(model): MistralModel(
(embed_tokens): Embedding(32002, 4096, padding_idx=0)
(layers): ModuleList(
(0-31): 32 x MistralDecoderLayer(
(self_attn): MistralAttention(
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(k_proj): Linear(in_features=4096, out_features=1024, bias=False)
(v_proj): Linear(in_features=4096, out_features=1024, bias=False)
(o_proj): Linear(in_features=4096, out_features=4096, bias=False)
(rotary_emb): MistralRotaryEmbedding()
)
(mlp): MistralMLP(
(gate_proj): Linear(in_features=4096, out_features=14336, bias=False)
(up_proj): Linear(in_features=4096, out_features=14336, bias=False)
(down_proj): Linear(in_features=14336, out_features=4096, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): MistralRMSNorm()
(post_attention_layernorm): MistralRMSNorm()
)
)
(norm): MistralRMSNorm()
)
(lm_head): Linear(in_features=4096, out_features=32002, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf
|
RichardErkhov
| 2024-05-31T14:51:36Z | 361 | 0 | null |
[
"gguf",
"region:us"
] | null | 2024-05-31T12:06:16Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
zephykor-ko-7b-chang - GGUF
- Model creator: https://huggingface.co/lcw99/
- Original model: https://huggingface.co/lcw99/zephykor-ko-7b-chang/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [zephykor-ko-7b-chang.Q2_K.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.Q2_K.gguf) | Q2_K | 2.6GB |
| [zephykor-ko-7b-chang.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.IQ3_XS.gguf) | IQ3_XS | 2.89GB |
| [zephykor-ko-7b-chang.IQ3_S.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.IQ3_S.gguf) | IQ3_S | 3.04GB |
| [zephykor-ko-7b-chang.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.Q3_K_S.gguf) | Q3_K_S | 3.02GB |
| [zephykor-ko-7b-chang.IQ3_M.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.IQ3_M.gguf) | IQ3_M | 3.13GB |
| [zephykor-ko-7b-chang.Q3_K.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.Q3_K.gguf) | Q3_K | 3.35GB |
| [zephykor-ko-7b-chang.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.Q3_K_M.gguf) | Q3_K_M | 3.35GB |
| [zephykor-ko-7b-chang.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.Q3_K_L.gguf) | Q3_K_L | 3.64GB |
| [zephykor-ko-7b-chang.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.IQ4_XS.gguf) | IQ4_XS | 3.76GB |
| [zephykor-ko-7b-chang.Q4_0.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.Q4_0.gguf) | Q4_0 | 3.91GB |
| [zephykor-ko-7b-chang.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.IQ4_NL.gguf) | IQ4_NL | 3.95GB |
| [zephykor-ko-7b-chang.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.Q4_K_S.gguf) | Q4_K_S | 3.94GB |
| [zephykor-ko-7b-chang.Q4_K.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.Q4_K.gguf) | Q4_K | 4.15GB |
| [zephykor-ko-7b-chang.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.Q4_K_M.gguf) | Q4_K_M | 4.15GB |
| [zephykor-ko-7b-chang.Q4_1.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.Q4_1.gguf) | Q4_1 | 4.33GB |
| [zephykor-ko-7b-chang.Q5_0.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.Q5_0.gguf) | Q5_0 | 4.75GB |
| [zephykor-ko-7b-chang.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.Q5_K_S.gguf) | Q5_K_S | 4.75GB |
| [zephykor-ko-7b-chang.Q5_K.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.Q5_K.gguf) | Q5_K | 4.87GB |
| [zephykor-ko-7b-chang.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.Q5_K_M.gguf) | Q5_K_M | 4.87GB |
| [zephykor-ko-7b-chang.Q5_1.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.Q5_1.gguf) | Q5_1 | 5.16GB |
| [zephykor-ko-7b-chang.Q6_K.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.Q6_K.gguf) | Q6_K | 5.63GB |
| [zephykor-ko-7b-chang.Q8_0.gguf](https://huggingface.co/RichardErkhov/lcw99_-_zephykor-ko-7b-chang-gguf/blob/main/zephykor-ko-7b-chang.Q8_0.gguf) | Q8_0 | 7.3GB |
Original model description:
---
language:
- ko
- en
---
* Under construction, be carefull.
|
mradermacher/PM-14B-10k-GGUF
|
mradermacher
| 2024-06-08T09:03:41Z | 361 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Antoinegg1/PM-14B-10k",
"endpoints_compatible",
"region:us"
] | null | 2024-06-08T08:14:36Z |
---
base_model: Antoinegg1/PM-14B-10k
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Antoinegg1/PM-14B-10k
<!-- 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/PM-14B-10k-GGUF/resolve/main/PM-14B-10k.Q2_K.gguf) | Q2_K | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/PM-14B-10k-GGUF/resolve/main/PM-14B-10k.IQ3_XS.gguf) | IQ3_XS | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/PM-14B-10k-GGUF/resolve/main/PM-14B-10k.IQ3_S.gguf) | IQ3_S | 6.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/PM-14B-10k-GGUF/resolve/main/PM-14B-10k.Q3_K_S.gguf) | Q3_K_S | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/PM-14B-10k-GGUF/resolve/main/PM-14B-10k.IQ3_M.gguf) | IQ3_M | 7.2 | |
| [GGUF](https://huggingface.co/mradermacher/PM-14B-10k-GGUF/resolve/main/PM-14B-10k.Q3_K_M.gguf) | Q3_K_M | 7.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/PM-14B-10k-GGUF/resolve/main/PM-14B-10k.Q3_K_L.gguf) | Q3_K_L | 7.9 | |
| [GGUF](https://huggingface.co/mradermacher/PM-14B-10k-GGUF/resolve/main/PM-14B-10k.IQ4_XS.gguf) | IQ4_XS | 8.0 | |
| [GGUF](https://huggingface.co/mradermacher/PM-14B-10k-GGUF/resolve/main/PM-14B-10k.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PM-14B-10k-GGUF/resolve/main/PM-14B-10k.Q4_K_M.gguf) | Q4_K_M | 9.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PM-14B-10k-GGUF/resolve/main/PM-14B-10k.Q5_K_S.gguf) | Q5_K_S | 10.1 | |
| [GGUF](https://huggingface.co/mradermacher/PM-14B-10k-GGUF/resolve/main/PM-14B-10k.Q5_K_M.gguf) | Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/PM-14B-10k-GGUF/resolve/main/PM-14B-10k.Q6_K.gguf) | Q6_K | 12.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/PM-14B-10k-GGUF/resolve/main/PM-14B-10k.Q8_0.gguf) | Q8_0 | 15.2 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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 -->
|
Ammartatox/mistralm-Q4_K_M-GGUF
|
Ammartatox
| 2024-06-25T16:12:24Z | 361 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:Ammartatox/mistralm",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-25T16:12:04Z |
---
base_model: Ammartatox/mistralm
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- llama-cpp
- gguf-my-repo
---
# Ammartatox/mistralm-Q4_K_M-GGUF
This model was converted to GGUF format from [`Ammartatox/mistralm`](https://huggingface.co/Ammartatox/mistralm) 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/Ammartatox/mistralm) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Ammartatox/mistralm-Q4_K_M-GGUF --hf-file mistralm-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Ammartatox/mistralm-Q4_K_M-GGUF --hf-file mistralm-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.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Ammartatox/mistralm-Q4_K_M-GGUF --hf-file mistralm-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Ammartatox/mistralm-Q4_K_M-GGUF --hf-file mistralm-q4_k_m.gguf -c 2048
```
|
ShreyaR/finetuned-roberta-depression
|
ShreyaR
| 2022-05-20T04:38:42Z | 360 | 6 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
widget:
- text: "I feel so low and numb, don't feel like doing anything. Just passing my days"
- text: "Sleep is my greatest and most comforting escape whenever I wake up these days. The literal very first emotion I feel is just misery and reminding myself of all my problems."
- text: "I went to a movie today. It was below my expectations but the day was fine."
- text: "The first day of work was a little hectic but met pretty good colleagues, we went for a team dinner party at the end of the day."
metrics:
- accuracy
model-index:
- name: finetuned-roberta-depression
results: []
---
<!-- 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-roberta-depression
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1385
- Accuracy: 0.9745
## 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
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0238 | 1.0 | 625 | 0.1385 | 0.9745 |
| 0.0333 | 2.0 | 1250 | 0.1385 | 0.9745 |
| 0.0263 | 3.0 | 1875 | 0.1385 | 0.9745 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
microsoft/unispeech-sat-base-plus-sd
|
microsoft
| 2021-12-17T18:40:56Z | 360 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech-sat",
"audio-frame-classification",
"speech",
"en",
"arxiv:1912.07875",
"arxiv:2106.06909",
"arxiv:2101.00390",
"arxiv:2110.05752",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- speech
---
# UniSpeech-SAT-Base for Speaker Diarization
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz.
The model was pre-trained on:
- 60,000 hours of [Libri-Light](https://arxiv.org/abs/1912.07875)
- 10,000 hours of [GigaSpeech](https://arxiv.org/abs/2106.06909)
- 24,000 hours of [VoxPopuli](https://arxiv.org/abs/2101.00390)
[Paper: UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER
AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752)
Authors: Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu
**Abstract**
*Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled data and avoids extensive human labeling. Recent years witness great successes in applying self-supervised learning in speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are introduced for enhancing the unsupervised speaker information extraction. First, we apply the multi-task learning to the current SSL framework, where we integrate the utterance-wise contrastive loss with the SSL objective function. Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where additional overlapped utterances are created unsupervisely and incorporate during training. We integrate the proposed methods into the HuBERT framework. Experiment results on SUPERB benchmark show that the proposed system achieves state-of-the-art performance in universal representation learning, especially for speaker identification oriented tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up training dataset to 94 thousand hours public audio data and achieve further performance improvement in all SUPERB tasks..*
The original model can be found under https://github.com/microsoft/UniSpeech/tree/main/UniSpeech-SAT.
# Fine-tuning details
The model is fine-tuned on the [LibriMix dataset](https://github.com/JorisCos/LibriMix) using just a linear layer for mapping the network outputs.
# Usage
## Speaker Diarization
```python
from transformers import Wav2Vec2FeatureExtractor, UniSpeechSatForAudioFrameClassification
from datasets import load_dataset
import torch
dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained('microsoft/unispeech-sat-base-plus-sd')
model = UniSpeechSatForAudioFrameClassification.from_pretrained('microsoft/unispeech-sat-base-plus-sd')
# audio file is decoded on the fly
inputs = feature_extractor(dataset[0]["audio"]["array"], return_tensors="pt")
logits = model(**inputs).logits
probabilities = torch.sigmoid(logits[0])
# labels is a one-hot array of shape (num_frames, num_speakers)
labels = (probabilities > 0.5).long()
```
# License
The official license can be found [here](https://github.com/microsoft/UniSpeech/blob/main/LICENSE)
|
pile-of-law/legalbert-large-1.7M-2
|
pile-of-law
| 2023-06-06T20:10:02Z | 360 | 51 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"legal",
"fill-mask",
"en",
"dataset:pile-of-law/pile-of-law",
"arxiv:1907.11692",
"arxiv:1810.04805",
"arxiv:2110.00976",
"arxiv:2207.00220",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-29T18:27:57Z |
---
language:
- en
datasets:
- pile-of-law/pile-of-law
pipeline_tag: fill-mask
tags:
- legal
---
# Pile of Law BERT large model 2 (uncased)
Pretrained model on English language legal and administrative text using the [RoBERTa](https://arxiv.org/abs/1907.11692) pretraining objective. This model was trained with the same setup as [pile-of-law/legalbert-large-1.7M-1](https://huggingface.co/pile-of-law/legalbert-large-1.7M-1), but with a different seed.
## Model description
Pile of Law BERT large 2 is a transformers model with the [BERT large model (uncased)](https://huggingface.co/bert-large-uncased) architecture pretrained on the [Pile of Law](https://huggingface.co/datasets/pile-of-law/pile-of-law), a dataset consisting of ~256GB of English language legal and administrative text for language model pretraining.
## Intended uses & limitations
You can use the raw model for masked language modeling or fine-tune it for a downstream task. Since this model was pretrained on a English language legal and administrative text corpus, legal downstream tasks will likely be more in-domain for this model.
## How to use
You can use the model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> pipe = pipeline(task='fill-mask', model='pile-of-law/legalbert-large-1.7M-2')
>>> pipe("An [MASK] is a request made after a trial by a party that has lost on one or more issues that a higher court review the decision to determine if it was correct.")
[{'sequence': 'an exception is a request made after a trial by a party that has lost on one or more issues that a higher court review the decision to determine if it was correct.',
'score': 0.5218929052352905,
'token': 4028,
'token_str': 'exception'},
{'sequence': 'an appeal is a request made after a trial by a party that has lost on one or more issues that a higher court review the decision to determine if it was correct.',
'score': 0.11434809118509293,
'token': 1151,
'token_str': 'appeal'},
{'sequence': 'an exclusion is a request made after a trial by a party that has lost on one or more issues that a higher court review the decision to determine if it was correct.',
'score': 0.06454459577798843,
'token': 5345,
'token_str': 'exclusion'},
{'sequence': 'an example is a request made after a trial by a party that has lost on one or more issues that a higher court review the decision to determine if it was correct.',
'score': 0.043593790382146835,
'token': 3677,
'token_str': 'example'},
{'sequence': 'an objection is a request made after a trial by a party that has lost on one or more issues that a higher court review the decision to determine if it was correct.',
'score': 0.03758585825562477,
'token': 3542,
'token_str': 'objection'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('pile-of-law/legalbert-large-1.7M-2')
model = BertModel.from_pretrained('pile-of-law/legalbert-large-1.7M-2')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('pile-of-law/legalbert-large-1.7M-2')
model = TFBertModel.from_pretrained('pile-of-law/legalbert-large-1.7M-2')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Limitations and bias
Please see Appendix G of the Pile of Law paper for copyright limitations related to dataset and model use.
This model can have biased predictions. In the following example where the model is used with a pipeline for masked language modeling, for the race descriptor of the criminal, the model predicts a higher score for "black" than "white".
```python
>>> from transformers import pipeline
>>> pipe = pipeline(task='fill-mask', model='pile-of-law/legalbert-large-1.7M-2')
>>> pipe("The transcript of evidence reveals that at approximately 7:30 a. m. on January 22, 1973, the prosecutrix was awakened in her home in DeKalb County by the barking of the family dog, and as she opened her eyes she saw a [MASK] man standing beside her bed with a gun.", targets=["black", "white"])
[{'sequence': 'the transcript of evidence reveals that at approximately 7 : 30 a. m. on january 22, 1973, the prosecutrix was awakened in her home in dekalb county by the barking of the family dog, and as she opened her eyes she saw a black man standing beside her bed with a gun.',
'score': 0.02685137465596199,
'token': 4311,
'token_str': 'black'},
{'sequence': 'the transcript of evidence reveals that at approximately 7 : 30 a. m. on january 22, 1973, the prosecutrix was awakened in her home in dekalb county by the barking of the family dog, and as she opened her eyes she saw a white man standing beside her bed with a gun.',
'score': 0.013632853515446186,
'token': 4249,
'token_str': 'white'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The Pile of Law BERT large model was pretrained on the Pile of Law, a dataset consisting of ~256GB of English language legal and administrative text for language model pretraining. The Pile of Law consists of 35 data sources, including legal analyses, court opinions and filings, government agency publications, contracts, statutes, regulations, casebooks, etc. We describe the data sources in detail in Appendix E of the Pile of Law paper. The Pile of Law dataset is placed under a CreativeCommons Attribution-NonCommercial-ShareAlike 4.0 International license.
## Training procedure
### Preprocessing
The model vocabulary consists of 29,000 tokens from a custom word-piece vocabulary fit to Pile of Law using the [HuggingFace WordPiece tokenizer](https://github.com/huggingface/tokenizers) and 3,000 randomly sampled legal terms from Black's Law Dictionary, for a vocabulary size of 32,000 tokens. The 80-10-10 masking, corruption, leave split, as in [BERT](https://arxiv.org/abs/1810.04805), is used, with a replication rate of 20 to create different masks for each context. To generate sequences, we use the [LexNLP sentence segmenter](https://github.com/LexPredict/lexpredict-lexnlp), which handles sentence segmentation for legal citations (which are often falsely mistaken as sentences). The input is formatted by filling sentences until they comprise 256 tokens, followed by a [SEP] token, and then filling sentences such that the entire span is under 512 tokens. If the next sentence in the series is too large, it is not added, and the remaining context length is filled with padding tokens.
### Pretraining
The model was trained on a SambaNova cluster, with 8 RDUs, for 1.7 million steps. We used a smaller learning rate of 5e-6 and batch size of 128, to mitigate training instability, potentially due to the diversity of sources in our training data. The masked language modeling (MLM) objective without NSP loss, as described in [RoBERTa](https://arxiv.org/abs/1907.11692), was used for pretraining. The model was pretrained with 512 length sequence lengths for all steps.
We trained two models with the same setup in parallel model training runs, with different random seeds. We selected the lowest log likelihood model, [pile-of-law/legalbert-large-1.7M-1](https://huggingface.co/pile-of-law/legalbert-large-1.7M-1), which we refer to as PoL-BERT-Large, for experiments, but also release the second model, [pile-of-law/legalbert-large-1.7M-2](https://huggingface.co/pile-of-law/legalbert-large-1.7M-2).
## Evaluation results
See the model card for [pile-of-law/legalbert-large-1.7M-1](https://huggingface.co/pile-of-law/legalbert-large-1.7M-1) for finetuning results on the CaseHOLD variant provided by the [LexGLUE paper](https://arxiv.org/abs/2110.00976).
### BibTeX entry and citation info
```bibtex
@misc{hendersonkrass2022pileoflaw,
url = {https://arxiv.org/abs/2207.00220},
author = {Henderson, Peter and Krass, Mark S. and Zheng, Lucia and Guha, Neel and Manning, Christopher D. and Jurafsky, Dan and Ho, Daniel E.},
title = {Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset},
publisher = {arXiv},
year = {2022}
}
```
|
AIMH/mental-roberta-large
|
AIMH
| 2023-02-27T19:11:40Z | 360 | 5 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:2110.15621",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-02-26T14:58:01Z |
---
license: cc-by-nc-4.0
---
[MentalBERT](https://arxiv.org/abs/2110.15621) is a model initialized with RoBERTa-large (`uncased_L-24_H-1024_A-16`) and trained with mental health-related posts collected from Reddit.
We follow the standard pretraining protocols of BERT and RoBERTa with [Huggingface’s Transformers library](https://github.com/huggingface/transformers).
We use four Nvidia Tesla v100 GPUs to train the two language models. We set the batch size to 8 per GPU, evaluate every 1,000 steps, and train for 312,000 iterations.
## Usage
Load the model via [Huggingface’s Transformers library](https://github.com/huggingface/transformers):
```
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("AIMH/mental-roberta-large")
model = AutoModel.from_pretrained("AIMH/mental-roberta-large")
```
To minimize the influence of worrying mask predictions, this model is gated. To download a gated model, you’ll need to be authenticated.
Know more about [gated models](https://huggingface.co/docs/hub/models-gated).
## Paper
[MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://arxiv.org/abs/2110.15621).
```
@inproceedings{ji2022mentalbert,
title = {{MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare}},
author = {Shaoxiong Ji and Tianlin Zhang and Luna Ansari and Jie Fu and Prayag Tiwari and Erik Cambria},
year = {2022},
booktitle = {Proceedings of LREC}
}
```
|
ytu-ce-cosmos/turkish-tiny-bert-uncased
|
ytu-ce-cosmos
| 2023-12-27T11:53:53Z | 360 | 6 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"pretraining",
"Turkish",
"turkish",
"fill-mask",
"tr",
"arxiv:2307.14134",
"license:mit",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-07-29T14:02:27Z |
---
widget:
- pipeline_tag: Fill-Mask
- text: gelirken bir litre [MASK] aldım.
example_title: ürün
pipeline_tag: fill-mask
tags:
- Turkish
- turkish
license: mit
language:
- tr
---
# turkish-tiny-bert-uncased
This is a Turkish Tiny uncased BERT model, developed to fill the gap for small-sized BERT models for Turkish. Since this model is uncased: it does not make a difference between turkish and Turkish.
#### ⚠ Uncased use requires manual lowercase conversion
**Don't** use the `do_lower_case = True` flag with the tokenizer. Instead, convert your text to lower case as follows:
```python
text.replace("I", "ı").lower()
```
This is due to a [known issue](https://github.com/huggingface/transformers/issues/6680) with the tokenizer.
Be aware that this model may exhibit biased predictions as it was trained primarily on crawled data, which inherently can contain various biases.
Other relevant information can be found in the [paper](https://arxiv.org/abs/2307.14134).
## Example Usage
```python
from transformers import AutoTokenizer, BertForMaskedLM
from transformers import pipeline
model = BertForMaskedLM.from_pretrained("ytu-ce-cosmos/turkish-tiny-bert-uncased")
# or
# model = BertForMaskedLM.from_pretrained("ytu-ce-cosmos/turkish-tiny-bert-uncased", from_tf = True)
tokenizer = AutoTokenizer.from_pretrained("ytu-ce-cosmos/turkish-tiny-bert-uncased")
unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer)
unmasker("gelirken bir litre [MASK] aldım.")
# [{'score': 0.202457457780838,
# 'token': 2417,
# 'token_str': 'su',
# 'sequence': 'gelirken bir litre su aldım.'},
# {'score': 0.09290537238121033,
# 'token': 11818,
# 'token_str': 'benzin',
# 'sequence': 'gelirken bir litre benzin aldım.'},
# {'score': 0.07785643637180328,
# 'token': 2026,
# 'token_str': '##den',
# 'sequence': 'gelirken bir litreden aldım.'},
# {'score': 0.06889808923006058,
# 'token': 2299,
# 'token_str': '##yi',
# 'sequence': 'gelirken bir litreyi aldım.'},
# {'score': 0.03152570128440857,
# 'token': 2647,
# 'token_str': '##ye',
# 'sequence': 'gelirken bir litreye aldım.'}]
```
# Acknowledgments
- Research supported with Cloud TPUs from [Google's TensorFlow Research Cloud](https://sites.research.google/trc/about/) (TFRC). Thanks for providing access to the TFRC ❤️
- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage 🤗
# Citations
```bibtex
@article{kesgin2023developing,
title={Developing and Evaluating Tiny to Medium-Sized Turkish BERT Models},
author={Kesgin, Himmet Toprak and Yuce, Muzaffer Kaan and Amasyali, Mehmet Fatih},
journal={arXiv preprint arXiv:2307.14134},
year={2023}
}
```
# License
MIT
|
TheBloke/qCammel-70-x-GGUF
|
TheBloke
| 2023-09-27T12:47:44Z | 360 | 3 |
transformers
|
[
"transformers",
"gguf",
"llama",
"pytorch",
"llama-2",
"qCammel-70",
"text-generation",
"en",
"arxiv:2305.12031",
"arxiv:2305.14314",
"arxiv:2302.70971",
"base_model:augtoma/qCammel-70-x",
"license:other",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-09-05T16:13:57Z |
---
language:
- en
license: other
library_name: transformers
tags:
- pytorch
- llama
- llama-2
- qCammel-70
model_name: qCammel 70
base_model: augtoma/qCammel-70-x
inference: false
model_creator: augtoma
model_type: llama
pipeline_tag: text-generation
prompt_template: 'A chat between a curious user and an artificial intelligence assistant.
The assistant gives helpful, detailed, and polite answers to the user''s questions.
USER: {prompt} ASSISTANT:
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# qCammel 70 - GGUF
- Model creator: [augtoma](https://huggingface.co/augtoma)
- Original model: [qCammel 70](https://huggingface.co/augtoma/qCammel-70-x)
<!-- description start -->
## Description
This repo contains GGUF format model files for [augtoma's qCammel 70](https://huggingface.co/augtoma/qCammel-70-x).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/qCammel-70-x-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/qCammel-70-x-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/qCammel-70-x-GGUF)
* [augtoma's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/augtoma/qCammel-70-x)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Vicuna
```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT:
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `other`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [augtoma's qCammel 70](https://huggingface.co/augtoma/qCammel-70-x).
<!-- licensing end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [qcammel-70-x.Q2_K.gguf](https://huggingface.co/TheBloke/qCammel-70-x-GGUF/blob/main/qcammel-70-x.Q2_K.gguf) | Q2_K | 2 | 29.28 GB| 31.78 GB | smallest, significant quality loss - not recommended for most purposes |
| [qcammel-70-x.Q3_K_S.gguf](https://huggingface.co/TheBloke/qCammel-70-x-GGUF/blob/main/qcammel-70-x.Q3_K_S.gguf) | Q3_K_S | 3 | 29.92 GB| 32.42 GB | very small, high quality loss |
| [qcammel-70-x.Q3_K_M.gguf](https://huggingface.co/TheBloke/qCammel-70-x-GGUF/blob/main/qcammel-70-x.Q3_K_M.gguf) | Q3_K_M | 3 | 33.19 GB| 35.69 GB | very small, high quality loss |
| [qcammel-70-x.Q3_K_L.gguf](https://huggingface.co/TheBloke/qCammel-70-x-GGUF/blob/main/qcammel-70-x.Q3_K_L.gguf) | Q3_K_L | 3 | 36.15 GB| 38.65 GB | small, substantial quality loss |
| [qcammel-70-x.Q4_0.gguf](https://huggingface.co/TheBloke/qCammel-70-x-GGUF/blob/main/qcammel-70-x.Q4_0.gguf) | Q4_0 | 4 | 38.87 GB| 41.37 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [qcammel-70-x.Q4_K_S.gguf](https://huggingface.co/TheBloke/qCammel-70-x-GGUF/blob/main/qcammel-70-x.Q4_K_S.gguf) | Q4_K_S | 4 | 39.07 GB| 41.57 GB | small, greater quality loss |
| [qcammel-70-x.Q4_K_M.gguf](https://huggingface.co/TheBloke/qCammel-70-x-GGUF/blob/main/qcammel-70-x.Q4_K_M.gguf) | Q4_K_M | 4 | 41.42 GB| 43.92 GB | medium, balanced quality - recommended |
| [qcammel-70-x.Q5_0.gguf](https://huggingface.co/TheBloke/qCammel-70-x-GGUF/blob/main/qcammel-70-x.Q5_0.gguf) | Q5_0 | 5 | 47.46 GB| 49.96 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [qcammel-70-x.Q5_K_S.gguf](https://huggingface.co/TheBloke/qCammel-70-x-GGUF/blob/main/qcammel-70-x.Q5_K_S.gguf) | Q5_K_S | 5 | 47.46 GB| 49.96 GB | large, low quality loss - recommended |
| [qcammel-70-x.Q5_K_M.gguf](https://huggingface.co/TheBloke/qCammel-70-x-GGUF/blob/main/qcammel-70-x.Q5_K_M.gguf) | Q5_K_M | 5 | 48.75 GB| 51.25 GB | large, very low quality loss - recommended |
| qcammel-70-x.Q6_K.gguf | Q6_K | 6 | 56.59 GB| 59.09 GB | very large, extremely low quality loss |
| qcammel-70-x.Q8_0.gguf | Q8_0 | 8 | 73.29 GB| 75.79 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
### Q6_K and Q8_0 files are split and require joining
**Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files.
<details>
<summary>Click for instructions regarding Q6_K and Q8_0 files</summary>
### q6_K
Please download:
* `qcammel-70-x.Q6_K.gguf-split-a`
* `qcammel-70-x.Q6_K.gguf-split-b`
### q8_0
Please download:
* `qcammel-70-x.Q8_0.gguf-split-a`
* `qcammel-70-x.Q8_0.gguf-split-b`
To join the files, do the following:
Linux and macOS:
```
cat qcammel-70-x.Q6_K.gguf-split-* > qcammel-70-x.Q6_K.gguf && rm qcammel-70-x.Q6_K.gguf-split-*
cat qcammel-70-x.Q8_0.gguf-split-* > qcammel-70-x.Q8_0.gguf && rm qcammel-70-x.Q8_0.gguf-split-*
```
Windows command line:
```
COPY /B qcammel-70-x.Q6_K.gguf-split-a + qcammel-70-x.Q6_K.gguf-split-b qcammel-70-x.Q6_K.gguf
del qcammel-70-x.Q6_K.gguf-split-a qcammel-70-x.Q6_K.gguf-split-b
COPY /B qcammel-70-x.Q8_0.gguf-split-a + qcammel-70-x.Q8_0.gguf-split-b qcammel-70-x.Q8_0.gguf
del qcammel-70-x.Q8_0.gguf-split-a qcammel-70-x.Q8_0.gguf-split-b
```
</details>
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/qCammel-70-x-GGUF and below it, a specific filename to download, such as: qcammel-70-x.q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub>=0.17.1
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/qCammel-70-x-GGUF qcammel-70-x.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/qCammel-70-x-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/qCammel-70-x-GGUF qcammel-70-x.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m qcammel-70-x.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model from Python using ctransformers
#### First install the package
```bash
# Base ctransformers with no GPU acceleration
pip install ctransformers>=0.2.24
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]>=0.2.24
# Or with ROCm GPU acceleration
CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems
CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
```
#### Simple example code to load one of these GGUF models
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/qCammel-70-x-GGUF", model_file="qcammel-70-x.q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here's guides on using llama-cpp-python or ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: augtoma's qCammel 70
# qCammel-70
qCammel-70 is a fine-tuned version of Llama-2 70B model, trained on a distilled dataset of 15,000 instructions using QLoRA. This model is optimized for academic medical knowledge and instruction-following capabilities.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept their License before downloading this model .*
The fine-tuning process applied to qCammel-70 involves a distilled dataset of 15,000 instructions and is trained with QLoRA,
**Variations** The original Llama 2 has parameter sizes of 7B, 13B, and 70B. This is the fine-tuned version of the 70B model.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** qCammel-70 is based on the Llama 2 architecture, an auto-regressive language model that uses a decoder only transformer architecture.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved
**Research Papers**
- [Clinical Camel: An Open-Source Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding](https://arxiv.org/abs/2305.12031)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/abs/2305.14314)
- [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.70971)
<!-- original-model-card end -->
|
Yntec/Dreamful3
|
Yntec
| 2023-10-09T16:55:15Z | 360 | 2 |
diffusers
|
[
"diffusers",
"safetensors",
"Anime",
"Portrait",
"3D",
"Hius",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-10-08T20:31:27Z |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- Anime
- Portrait
- 3D
- Hius
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
---
# Dreamful 3
Original page:
https://civitai.com/models/17754?modelVersionId=70414
A very beautiful anime tennis girl, long wavy hair, sky blue eyes, miniskirt, short smile, ( ( ( full round face ) ) ), mid - shot, cinematic wallpaper, highly detailed by stanley artgerm lau
|
TheBloke/WordWoven-13B-GGUF
|
TheBloke
| 2024-01-04T11:21:49Z | 360 | 2 |
transformers
|
[
"transformers",
"gguf",
"mixtral",
"base_model:Walmart-the-bag/WordWoven-13B",
"license:mit",
"text-generation-inference",
"region:us"
] | null | 2024-01-04T10:05:17Z |
---
base_model: Walmart-the-bag/WordWoven-13B
inference: false
license: mit
model_creator: wbag
model_name: WordWoven 13B
model_type: mixtral
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# WordWoven 13B - GGUF
- Model creator: [wbag](https://huggingface.co/Walmart-the-bag)
- Original model: [WordWoven 13B](https://huggingface.co/Walmart-the-bag/WordWoven-13B)
<!-- description start -->
## Description
This repo contains GGUF format model files for [wbag's WordWoven 13B](https://huggingface.co/Walmart-the-bag/WordWoven-13B).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/WordWoven-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/WordWoven-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/WordWoven-13B-GGUF)
* [wbag's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Walmart-the-bag/WordWoven-13B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [wordwoven-13b.Q2_K.gguf](https://huggingface.co/TheBloke/WordWoven-13B-GGUF/blob/main/wordwoven-13b.Q2_K.gguf) | Q2_K | 2 | 4.36 GB| 6.86 GB | smallest, significant quality loss - not recommended for most purposes |
| [wordwoven-13b.Q3_K_S.gguf](https://huggingface.co/TheBloke/WordWoven-13B-GGUF/blob/main/wordwoven-13b.Q3_K_S.gguf) | Q3_K_S | 3 | 5.59 GB| 8.09 GB | very small, high quality loss |
| [wordwoven-13b.Q3_K_M.gguf](https://huggingface.co/TheBloke/WordWoven-13B-GGUF/blob/main/wordwoven-13b.Q3_K_M.gguf) | Q3_K_M | 3 | 5.68 GB| 8.18 GB | very small, high quality loss |
| [wordwoven-13b.Q3_K_L.gguf](https://huggingface.co/TheBloke/WordWoven-13B-GGUF/blob/main/wordwoven-13b.Q3_K_L.gguf) | Q3_K_L | 3 | 5.76 GB| 8.26 GB | small, substantial quality loss |
| [wordwoven-13b.Q4_0.gguf](https://huggingface.co/TheBloke/WordWoven-13B-GGUF/blob/main/wordwoven-13b.Q4_0.gguf) | Q4_0 | 4 | 7.28 GB| 9.78 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [wordwoven-13b.Q4_K_S.gguf](https://huggingface.co/TheBloke/WordWoven-13B-GGUF/blob/main/wordwoven-13b.Q4_K_S.gguf) | Q4_K_S | 4 | 7.28 GB| 9.78 GB | small, greater quality loss |
| [wordwoven-13b.Q4_K_M.gguf](https://huggingface.co/TheBloke/WordWoven-13B-GGUF/blob/main/wordwoven-13b.Q4_K_M.gguf) | Q4_K_M | 4 | 7.30 GB| 9.80 GB | medium, balanced quality - recommended |
| [wordwoven-13b.Q5_0.gguf](https://huggingface.co/TheBloke/WordWoven-13B-GGUF/blob/main/wordwoven-13b.Q5_0.gguf) | Q5_0 | 5 | 8.87 GB| 11.37 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [wordwoven-13b.Q5_K_S.gguf](https://huggingface.co/TheBloke/WordWoven-13B-GGUF/blob/main/wordwoven-13b.Q5_K_S.gguf) | Q5_K_S | 5 | 8.87 GB| 11.37 GB | large, low quality loss - recommended |
| [wordwoven-13b.Q5_K_M.gguf](https://huggingface.co/TheBloke/WordWoven-13B-GGUF/blob/main/wordwoven-13b.Q5_K_M.gguf) | Q5_K_M | 5 | 8.88 GB| 11.38 GB | large, very low quality loss - recommended |
| [wordwoven-13b.Q6_K.gguf](https://huggingface.co/TheBloke/WordWoven-13B-GGUF/blob/main/wordwoven-13b.Q6_K.gguf) | Q6_K | 6 | 10.57 GB| 13.07 GB | very large, extremely low quality loss |
| [wordwoven-13b.Q8_0.gguf](https://huggingface.co/TheBloke/WordWoven-13B-GGUF/blob/main/wordwoven-13b.Q8_0.gguf) | Q8_0 | 8 | 13.69 GB| 16.19 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/WordWoven-13B-GGUF and below it, a specific filename to download, such as: wordwoven-13b.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/WordWoven-13B-GGUF wordwoven-13b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/WordWoven-13B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/WordWoven-13B-GGUF wordwoven-13b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m wordwoven-13b.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 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)
## How to run 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%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. 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="./wordwoven-13b.Q4_K_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(
"{prompt}", # 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="./wordwoven-13b.Q4_K_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."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: wbag's WordWoven 13B
# Model Description
This is the last model to test out MoE, made on 1xA100-80G (11 total minutes including download)
# Use
This is for instruction. It may give out false information whether its about coding, or specific questions.
# Benchmark/Evaluation
TODO (soon)
# License
### MIT
```
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
<!-- original-model-card end -->
|
internlm/internlm2-math-base-7b
|
internlm
| 2024-02-28T05:29:38Z | 360 | 2 |
transformers
|
[
"transformers",
"safetensors",
"internlm2",
"text-generation",
"math",
"conversational",
"custom_code",
"en",
"zh",
"license:other",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2024-01-22T12:37:47Z |
---
pipeline_tag: text-generation
license: other
language:
- en
- zh
tags:
- math
---
# InternLM-Math
<div align="center">
<img src="https://raw.githubusercontent.com/InternLM/InternLM/main/assets/logo.svg" width="200"/>
<div> </div>
<div align="center">
<b><font size="5">InternLM-Math</font></b>
<sup>
<a href="https://internlm.intern-ai.org.cn/">
<i><font size="4">HOT</font></i>
</a>
</sup>
<div> </div>
</div>
State-of-the-art bilingual open-sourced Math reasoning LLMs.
A **solver**, **prover**, **verifier**, **augmentor**.
[💻 Github](https://github.com/InternLM/InternLM-Math) [🤗 Demo](https://huggingface.co/spaces/internlm/internlm2-math-7b) [🤗 Checkpoints](https://huggingface.co/internlm/internlm2-math-7b) [](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM2-Math-7B) [<img src="https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/modelscope_logo.png" width="20px" /> ModelScope](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-math-7b/summary)
</div>
# News
- [2024.01.29] We add checkpoints from ModelScope. Tech report is on the way!
- [2024.01.26] We add checkpoints from OpenXLab, which ease Chinese users to download!
# Introduction
- **7B and 20B Chinese and English Math LMs with better than ChatGPT performances.** InternLM2-Math are continued pretrained from InternLM2-Base with ~100B high quality math-related tokens and SFT with ~2M bilingual math supervised data. We apply minhash and exact number match to decontaminate possible test set leakage.
- **Add Lean as a support language for math problem solving and math theorem proving.** We are exploring combining Lean 3 with InternLM-Math for verifiable math reasoning. InternLM-Math can generate Lean codes for simple math reasoning tasks like GSM8K or provide possible proof tactics based on Lean states.
- **Also can be viewed as a reward model, which supports the Outcome/Process/Lean Reward Model.** We supervise InternLM2-Math with various types of reward modeling data, to make InternLM2-Math can also verify chain-of-thought processes. We also add the ability to convert a chain-of-thought process into Lean 3 code.
- **A Math LM Augment Helper** and **Code Interpreter**. InternLM2-Math can help augment math reasoning problems and solve them using the code interpreter which makes you generate synthesis data quicker!
# Models
**InternLM2-Math-Base-7B** and **InternLM2-Math-Base-20B** are pretrained checkpoints. **InternLM2-Math-7B** and **InternLM2-Math-20B** are SFT checkpoints.
| Model |Model Type | Transformers(HF) |OpenXLab| ModelScope | Release Date |
|---|---|---|---|---|---|
| **InternLM2-Math-Base-7B** | Base| [🤗internlm/internlm2-math-base-7b](https://huggingface.co/internlm/internlm2-math-base-7b) |[](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM2-Math-Base-7B)| [<img src="https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/modelscope_logo.png" width="20px" /> internlm2-math-base-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-math-base-7b/summary)| 2024-01-23|
| **InternLM2-Math-Base-20B** | Base| [🤗internlm/internlm2-math-base-20b](https://huggingface.co/internlm/internlm2-math-base-20b) |[](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM2-Math-Base-20B)|[<img src="https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/modelscope_logo.png" width="20px" /> internlm2-math-base-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-math-base-20b/summary)| 2024-01-23|
| **InternLM2-Math-7B** | Chat| [🤗internlm/internlm2-math-7b](https://huggingface.co/internlm/internlm2-math-7b) |[](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM2-Math-7B)|[<img src="https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/modelscope_logo.png" width="20px" /> internlm2-math-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-math-7b/summary)| 2024-01-23|
| **InternLM2-Math-20B** | Chat| [🤗internlm/internlm2-math-20b](https://huggingface.co/internlm/internlm2-math-20b) |[](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM2-Math-20B)|[<img src="https://raw.githubusercontent.com/InternLM/InternLM-Math/main/assets/modelscope_logo.png" width="20px" /> internlm2-math-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-math-20b/summary)| 2024-01-23|
# Performance
## Pretrain Performance
We evaluate pretrain checkpoints based on greedy decoding with few-shot COT. Details of pretraining will be introduced in the tech report.
| Model | GSM8K | MATH |
|------------------------|---------|--------|
| Llama2-7B | 11.8 | 3.2 |
| Llemma-7B | 36.4 | 18.0 |
| InternLM2-Base-7B | 36.5 | 8.6 |
| **InternLM2-Math-Base-7B** | **49.2** | **21.5** |
| Minerva-8B | 16.2 | 14.1 |
| InternLM2-Base-20B | 54.6 | 13.7 |
| **InternLM2-Math-Base-20B** | **63.7** | **27.3** |
| Llemma-34B | 51.5 | 25.0 |
| Minerva-62B | 52.4 | 27.6 |
| Minerva-540B | 58.8 | 33.6 |
## SFT Peformance
All performance is based on greedy decoding with COT. We notice that the performance of Hungary has a big variance between our different checkpoints, while other performance is very stable. This may be due to the problem amount about Hungary.
| Model | Model Type | GSM8K | MATH | Hungary |
|------------------------|----------------------|--------|--------|---------|
| Qwen-7B-Chat | Genearl | 51.7 | 11.6 | - |
| DeepSeek-7B-Chat | General | 63.0 | 15.8 | 28.5 |
| InternLM2-Chat-7B | General | 70.7 | 23.0 | - |
| ChatGLM3-6B | General | 53.8 | 20.4 | 32 |
| MetaMath-Mistral-7B | Mathematics | 77.7 | 28.2 | 29 |
| MetaMath-Llemma-7B | Mathematics | 69.2 | 30.0 | - |
| **InternLM2-Math-7B** | Mathematics | **78.1** | **34.6** | **55** |
| InternLM2-Chat-20B | General | 79.6 | 31.9 | - |
| MetaMath-Llemma-34B | Mathematics | 75.8 | 34.8 | - |
| **InternLM2-Math-20B** | Mathematics | **82.6** | **37.7** | **66** |
| Qwen-72B | General | 78.9 | 35.2 | 52 |
| DeepSeek-67B | General | 84.1 | 32.6 | 58 |
| ChatGPT (GPT-3.5) | General | 80.8 | 34.1 | 41 |
| GPT4 (First version) | General | 92.0 | 42.5 | 68 |
# Inference
## LMDeploy
We suggest using [LMDeploy](https://github.com/InternLM/LMDeploy)(>=0.2.1) for inference.
```python
from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig
backend_config = TurbomindEngineConfig(model_name='internlm2-chat-7b', tp=1, cache_max_entry_count=0.3)
chat_template = ChatTemplateConfig(model_name='internlm2-chat-7b', system='', eosys='', meta_instruction='')
pipe = pipeline(model_path='internlm/internlm2-math-base-7b', chat_template_config=chat_template, backend_config=backend_config)
problem = '1+1='
result = pipe([problem], request_output_len=1024, top_k=1)
```
## Huggingface
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-math-base-7b", trust_remote_code=True)
# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-math-base-7b", trust_remote_code=True, torch_dtype=torch.float16).cuda()
model = model.eval()
response, history = model.chat(tokenizer, "1+1=", history=[], meta_instruction="")
print(response)
```
# Special usages
We list some instructions used in our SFT. You can use them to help you. You can use the other ways to prompt the model, but the following are recommended. InternLM2-Math may combine the following abilities but it is not guaranteed.
Translate proof problem to Lean:
Using Lean 3 to solve GSM8K problem:
Generate problem based on Lean 3 code:
Play 24 point game:
Augment a harder math problem:
| Description | Query |
| --- | --- |
| Solving question via chain-of-thought | {Question} |
| Solving question via Lean 3 | {Question}\nSolve this via Lean 3 |
| Outcome reward model | Given a question and an answer, check is it correct?\nQuestion:{Question}\nAnswer:{COT} |
| Process reward model | Given a question and an answer, check correctness of each step.\nQuestion:{Question}\nAnswer:{COT} |
| Reward model | Given a question and two answers, which one is better? \nQuestion:{Question}\nAnswer 1:{COT}\nAnswer 2:{COT} |
| Convert chain-of-thought to Lean 3 | Convert this answer into Lean3. Question:{Question}\nAnswer:{COT} |
| Convert Lean 3 to chain-of-thought | Convert this lean 3 code into a natural language problem with answers:\n{LEAN Code} |
| Translate question and chain-of-thought answer to a proof statement | Convert this question and answer into a proof format.\nQuestion:{Question}\nAnswer:{COT} |
| Translate proof problem to Lean 3 | Convert this natural langauge statement into a Lean 3 theorem statement:{Theorem} |
| Translate Lean 3 to proof problem | Convert this Lean 3 theorem statement into natural language:{STATEMENT} |
| Suggest a tactic based on Lean state | Given the Lean 3 tactic state, suggest a next tactic:\n{LEAN State} |
| Rephrase Problem | Describe this problem in another way. {Question} |
| Augment Problem | Please augment a new problem based on: {Question} |
| Augment a harder Problem | Increase the complexity of the problem: {Question} |
| Change specific numbers | Change specific numbers: {Question}|
| Introduce fractions or percentages | Introduce fractions or percentages: {Question}|
| Code Interpreter | [lagent](https://github.com/InternLM/InternLM/blob/main/agent/lagent.md) |
| In-context Learning | Question:{Question}\nAnswer:{COT}\n...Question:{Question}\nAnswer:{COT}|
# Fine-tune and others
Please refer to [InternLM](https://github.com/InternLM/InternLM/tree/main).
# Known issues
Our model is still under development and will be upgraded. There are some possible issues of InternLM-Math. If you find performances of some abilities are not great, welcome to open an issue.
- Jump the calculating step.
- Perform badly at Chinese fill-in-the-bank problems and English choice problems due to SFT data composition.
- Tend to generate Code Interpreter when facing Chinese problems due to SFT data composition.
- The reward model mode can be better leveraged with assigned token probabilities.
- Code switch due to SFT data composition.
- Some abilities of Lean can only be adapted to GSM8K-like problems (e.g. Convert chain-of-thought to Lean 3), and performance related to Lean is not guaranteed.
# Citation and Tech Report
To be appended.
|
Unbabel/TowerBase-13B-v0.1
|
Unbabel
| 2024-03-04T13:18:13Z | 360 | 4 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"translation",
"en",
"de",
"fr",
"zh",
"pt",
"nl",
"ru",
"ko",
"it",
"es",
"arxiv:2402.17733",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
translation
| 2024-01-26T17:43:54Z |
---
license: cc-by-nc-4.0
language:
- en
- de
- fr
- zh
- pt
- nl
- ru
- ko
- it
- es
metrics:
- comet
pipeline_tag: translation
---
# Model Card for TowerBase-13B-v0.1
## Model Details
### Model Description
TowerBase-13B is a language model that results from continuing the pretraining of Llama 2 on a mix of 20 billion tokens of monolingual data in ten different languages — English, Portuguese, Spanish, French, German, Dutch, Italian, Korean, Chinese, Russian — and bilingual data. TowerBase-13B-v0.1 is the first model in the series.
The resulting model shows improved performance on the supported languages, while maintaining Llama 2's capabilities on English. It is particularly well-suited for fine-tuning on translation and related tasks: check out [TowerInstruct](https://huggingface.co/Unbabel/TowerInstruct-13B-v0.1).
We will release more details in the upcoming technical report.
- **Developed by:** Unbabel, Instituto Superior Técnico, CentraleSupélec University of Paris-Saclay
- **Model type:** A 13B parameter model built on top of Llama 2 by continuing pretraining on multilingual data.
- **Language(s) (NLP):** English, Portuguese, Spanish, French, German, Dutch, Italian, Korean, Chinese, Russian
- **License:** CC-BY-NC-4.0, Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.
## Intended uses & limitations
The model is intended for research purposes in the 10 languages it supports.
The model is able to perform well on translation and related tasks (e.g., APE, GEC) on a few-shot regime.
It can also be fine-tuned to perform these tasks in a zero-shot fashion (see [TowerInstruct](https://huggingface.co/Unbabel/TowerInstruct-13B-v0.1), as well as other multilingual tasks.
### Out-of-Scope Use
The model is not guaranteed to perform well for languages other than the 10 languages it supports.
## Bias, Risks, and Limitations
TowerBase-v0.1 has not been aligned to human preferences, so the model may generate problematic outputs (e.g., hallucinations, harmful content, or false statements).
## Run the model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "Unbabel/TowerBase-13B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
text = "English: My name is TowerBase.\nPortuguese:"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
### Training Data
Filtered versions of [mc4](https://huggingface.co/datasets/mc4) and bilingual data from various sources (e.g., [OPUS](https://opus.nlpl.eu/)).
## Citation
```bibtex
@misc{tower_llm_2024,
title={Tower: An Open Multilingual Large Language Model for Translation-Related Tasks},
author={Duarte M. Alves and José Pombal and Nuno M. Guerreiro and Pedro H. Martins and João Alves and Amin Farajian and Ben Peters and Ricardo Rei and Patrick Fernandes and Sweta Agrawal and Pierre Colombo and José G. C. de Souza and André F. T. Martins},
year={2024},
eprint={2402.17733},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
bartowski/Code-Mistral-7B-GGUF
|
bartowski
| 2024-03-25T17:47:22Z | 360 | 2 | null |
[
"gguf",
"code",
"mathematics",
"text-generation",
"en",
"dataset:ajibawa-2023/Code-290k-ShareGPT",
"dataset:m-a-p/Code-Feedback",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:teknium/openhermes",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2024-03-25T17:34:21Z |
---
license: apache-2.0
datasets:
- ajibawa-2023/Code-290k-ShareGPT
- m-a-p/Code-Feedback
- microsoft/orca-math-word-problems-200k
- teknium/openhermes
language:
- en
tags:
- code
- mathematics
quantized_by: bartowski
pipeline_tag: text-generation
---
## Llamacpp Quantizations of Code-Mistral-7B
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2440">b2440</a> for quantization.
Original model: https://huggingface.co/ajibawa-2023/Code-Mistral-7B
Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Code-Mistral-7B-Q8_0.gguf](https://huggingface.co/bartowski/Code-Mistral-7B-GGUF/blob/main/Code-Mistral-7B-Q8_0.gguf) | Q8_0 | 7.69GB | Extremely high quality, generally unneeded but max available quant. |
| [Code-Mistral-7B-Q6_K.gguf](https://huggingface.co/bartowski/Code-Mistral-7B-GGUF/blob/main/Code-Mistral-7B-Q6_K.gguf) | Q6_K | 5.94GB | Very high quality, near perfect, *recommended*. |
| [Code-Mistral-7B-Q5_K_M.gguf](https://huggingface.co/bartowski/Code-Mistral-7B-GGUF/blob/main/Code-Mistral-7B-Q5_K_M.gguf) | Q5_K_M | 5.13GB | High quality, very usable. |
| [Code-Mistral-7B-Q5_K_S.gguf](https://huggingface.co/bartowski/Code-Mistral-7B-GGUF/blob/main/Code-Mistral-7B-Q5_K_S.gguf) | Q5_K_S | 4.99GB | High quality, very usable. |
| [Code-Mistral-7B-Q5_0.gguf](https://huggingface.co/bartowski/Code-Mistral-7B-GGUF/blob/main/Code-Mistral-7B-Q5_0.gguf) | Q5_0 | 4.99GB | High quality, older format, generally not recommended. |
| [Code-Mistral-7B-Q4_K_M.gguf](https://huggingface.co/bartowski/Code-Mistral-7B-GGUF/blob/main/Code-Mistral-7B-Q4_K_M.gguf) | Q4_K_M | 4.36GB | Good quality, similar to 4.25 bpw. |
| [Code-Mistral-7B-Q4_K_S.gguf](https://huggingface.co/bartowski/Code-Mistral-7B-GGUF/blob/main/Code-Mistral-7B-Q4_K_S.gguf) | Q4_K_S | 4.14GB | Slightly lower quality with small space savings. |
| [Code-Mistral-7B-IQ4_NL.gguf](https://huggingface.co/bartowski/Code-Mistral-7B-GGUF/blob/main/Code-Mistral-7B-IQ4_NL.gguf) | IQ4_NL | 4.15GB | Good quality, similar to Q4_K_S, new method of quanting, |
| [Code-Mistral-7B-IQ4_XS.gguf](https://huggingface.co/bartowski/Code-Mistral-7B-GGUF/blob/main/Code-Mistral-7B-IQ4_XS.gguf) | IQ4_XS | 3.94GB | Decent quality, new method with similar performance to Q4. |
| [Code-Mistral-7B-Q4_0.gguf](https://huggingface.co/bartowski/Code-Mistral-7B-GGUF/blob/main/Code-Mistral-7B-Q4_0.gguf) | Q4_0 | 4.10GB | Decent quality, older format, generally not recommended. |
| [Code-Mistral-7B-IQ3_M.gguf](https://huggingface.co/bartowski/Code-Mistral-7B-GGUF/blob/main/Code-Mistral-7B-IQ3_M.gguf) | IQ3_M | 3.28GB | Medium-low quality, new method with decent performance. |
| [Code-Mistral-7B-IQ3_S.gguf](https://huggingface.co/bartowski/Code-Mistral-7B-GGUF/blob/main/Code-Mistral-7B-IQ3_S.gguf) | IQ3_S | 3.18GB | Lower quality, new method with decent performance, recommended over Q3 quants. |
| [Code-Mistral-7B-Q3_K_L.gguf](https://huggingface.co/bartowski/Code-Mistral-7B-GGUF/blob/main/Code-Mistral-7B-Q3_K_L.gguf) | Q3_K_L | 3.82GB | Lower quality but usable, good for low RAM availability. |
| [Code-Mistral-7B-Q3_K_M.gguf](https://huggingface.co/bartowski/Code-Mistral-7B-GGUF/blob/main/Code-Mistral-7B-Q3_K_M.gguf) | Q3_K_M | 3.51GB | Even lower quality. |
| [Code-Mistral-7B-Q3_K_S.gguf](https://huggingface.co/bartowski/Code-Mistral-7B-GGUF/blob/main/Code-Mistral-7B-Q3_K_S.gguf) | Q3_K_S | 3.16GB | Low quality, not recommended. |
| [Code-Mistral-7B-Q2_K.gguf](https://huggingface.co/bartowski/Code-Mistral-7B-GGUF/blob/main/Code-Mistral-7B-Q2_K.gguf) | Q2_K | 2.71GB | Extremely low quality, *not* recommended.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
mradermacher/Genie-GGUF
|
mradermacher
| 2024-05-06T05:54:31Z | 360 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"dataset:Sadiah/Genie",
"base_model:Sadiah/Genie",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-03-26T17:56:13Z |
---
base_model: Sadiah/Genie
datasets:
- Sadiah/Genie
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags: []
---
## About
static quants of https://huggingface.co/Sadiah/Genie
<!-- 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/Genie-GGUF/resolve/main/Genie.Q2_K.gguf) | Q2_K | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/Genie-GGUF/resolve/main/Genie.IQ3_XS.gguf) | IQ3_XS | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Genie-GGUF/resolve/main/Genie.Q3_K_S.gguf) | Q3_K_S | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Genie-GGUF/resolve/main/Genie.IQ3_S.gguf) | IQ3_S | 3.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Genie-GGUF/resolve/main/Genie.IQ3_M.gguf) | IQ3_M | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Genie-GGUF/resolve/main/Genie.Q3_K_M.gguf) | Q3_K_M | 4.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Genie-GGUF/resolve/main/Genie.Q3_K_L.gguf) | Q3_K_L | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Genie-GGUF/resolve/main/Genie.IQ4_XS.gguf) | IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Genie-GGUF/resolve/main/Genie.Q4_0.gguf) | Q4_0 | 4.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Genie-GGUF/resolve/main/Genie.Q4_K_S.gguf) | Q4_K_S | 4.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Genie-GGUF/resolve/main/Genie.IQ4_NL.gguf) | IQ4_NL | 4.7 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Genie-GGUF/resolve/main/Genie.Q4_K_M.gguf) | Q4_K_M | 4.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Genie-GGUF/resolve/main/Genie.Q5_K_S.gguf) | Q5_K_S | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Genie-GGUF/resolve/main/Genie.Q5_K_M.gguf) | Q5_K_M | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/Genie-GGUF/resolve/main/Genie.Q6_K.gguf) | Q6_K | 6.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Genie-GGUF/resolve/main/Genie.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Noro-Hermes-3x7B-GGUF
|
mradermacher
| 2024-05-06T05:49:35Z | 360 | 2 |
transformers
|
[
"transformers",
"gguf",
"moe",
"frankenmoe",
"merge",
"mergekit",
"lazymergekit",
"NousResearch/Nous-Hermes-2-Mistral-7B-DPO",
"NeverSleep/Noromaid-7B-0.4-DPO",
"mistralai/Mistral-7B-Instruct-v0.2",
"en",
"base_model:ThomasComics/Noro-Hermes-3x7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-03-28T08:21:34Z |
---
base_model: ThomasComics/Noro-Hermes-3x7B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- NousResearch/Nous-Hermes-2-Mistral-7B-DPO
- NeverSleep/Noromaid-7B-0.4-DPO
- mistralai/Mistral-7B-Instruct-v0.2
---
## About
static quants of https://huggingface.co/ThomasComics/Noro-Hermes-3x7B
<!-- 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/Noro-Hermes-3x7B-GGUF/resolve/main/Noro-Hermes-3x7B.Q2_K.gguf) | Q2_K | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/Noro-Hermes-3x7B-GGUF/resolve/main/Noro-Hermes-3x7B.IQ3_XS.gguf) | IQ3_XS | 7.8 | |
| [GGUF](https://huggingface.co/mradermacher/Noro-Hermes-3x7B-GGUF/resolve/main/Noro-Hermes-3x7B.Q3_K_S.gguf) | Q3_K_S | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/Noro-Hermes-3x7B-GGUF/resolve/main/Noro-Hermes-3x7B.IQ3_S.gguf) | IQ3_S | 8.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Noro-Hermes-3x7B-GGUF/resolve/main/Noro-Hermes-3x7B.IQ3_M.gguf) | IQ3_M | 8.4 | |
| [GGUF](https://huggingface.co/mradermacher/Noro-Hermes-3x7B-GGUF/resolve/main/Noro-Hermes-3x7B.Q3_K_M.gguf) | Q3_K_M | 9.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Noro-Hermes-3x7B-GGUF/resolve/main/Noro-Hermes-3x7B.Q3_K_L.gguf) | Q3_K_L | 9.9 | |
| [GGUF](https://huggingface.co/mradermacher/Noro-Hermes-3x7B-GGUF/resolve/main/Noro-Hermes-3x7B.IQ4_XS.gguf) | IQ4_XS | 10.3 | |
| [GGUF](https://huggingface.co/mradermacher/Noro-Hermes-3x7B-GGUF/resolve/main/Noro-Hermes-3x7B.Q4_0.gguf) | Q4_0 | 10.7 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Noro-Hermes-3x7B-GGUF/resolve/main/Noro-Hermes-3x7B.Q4_K_S.gguf) | Q4_K_S | 10.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Noro-Hermes-3x7B-GGUF/resolve/main/Noro-Hermes-3x7B.IQ4_NL.gguf) | IQ4_NL | 10.8 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Noro-Hermes-3x7B-GGUF/resolve/main/Noro-Hermes-3x7B.Q4_K_M.gguf) | Q4_K_M | 11.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Noro-Hermes-3x7B-GGUF/resolve/main/Noro-Hermes-3x7B.Q5_K_S.gguf) | Q5_K_S | 13.0 | |
| [GGUF](https://huggingface.co/mradermacher/Noro-Hermes-3x7B-GGUF/resolve/main/Noro-Hermes-3x7B.Q5_K_M.gguf) | Q5_K_M | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/Noro-Hermes-3x7B-GGUF/resolve/main/Noro-Hermes-3x7B.Q6_K.gguf) | Q6_K | 15.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Noro-Hermes-3x7B-GGUF/resolve/main/Noro-Hermes-3x7B.Q8_0.gguf) | Q8_0 | 19.9 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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 -->
|
nDimensional/Drukhari-SDXL-LoRA-Warhammer-40k
|
nDimensional
| 2024-06-22T14:57:25Z | 360 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"SDXL",
"LoRA",
"en",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-03-29T11:17:26Z |
---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- SDXL
- LoRA
---
<html>
<head>
<link href="https://fonts.googleapis.com/css2?family=Skranji&display=swap" rel="stylesheet">
</head>
<style>
.title-container {
display: flex;
justify-content: center;
align-items: center;
}
.title {
font-size: 2.5em;
text-align: center;
color: #1d1d1d!important;
font-family: 'Skranji';
text-transform: uppercase;
letter-spacing: 0.1em;
padding: 0.5em 0;
background: transparent;
}
.title span {
background: -webkit-linear-gradient(45deg, #fcb900, #ff6900);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
}
.icon {
margin-right: 10px;
}
.icon img {
width: 50px;
height: auto;
padding-bottom: 12px;
}
img {
max-width: 100%;
height: auto;
}
.center {
display: block;
margin-left: auto;
margin-right: auto;
width: 1344;
}
figcaption {
display: block;
font-style: italic;
color: white;
padding: 2px;
text-align: center;
}
</style>
<div class="title-container">
<div class="icon">
<img src="assets/dark-eldar-icon.svg" atl="Drukhari Icon"/>
</div>
<h1 class="title">
<span>Drukhari</span>
</h1>
</div>
<figure>
<img src="assets/sample_image (4).png" alt="sample image" class="center"/>
<figcaption>(illustration), Dark Eldar Archon, helmet, black armor, shadowy ruined landscape, comic book \(style\), cel shading, bold outlines, muted colors, dynamic composition, limited color palette, line art, extremely detailed, warhammer 40k, <lora:Drukhari-000006:1></figcaption>
</figure>
</html>
<!-- START SUPPORT -->
<a href='https://ko-fi.com/M4M3ZBOIT' target='_blank'>
<img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi3.png?v=3' border='0' alt='Buy Me a Coffee at ko-fi.com' />
</a>
<!-- END SUPPORT -->
### Epoch06 recommended!
### Samples images available in [assets dir](https://huggingface.co/nDimensional/Drukhari-SDXL-LoRA-Warhammer-40k/tree/main/assets).
<!-- START Model Info -->
<div class="model-info-container" style='font-family:Monospace'>
<table id="model-info-table">
<tr>
<th></th>
<th></th>
</tr>
<tr>
<td>Base Model</td>
<td>SDXL v1.0</td>
</tr>
<tr>
<td>VAE</td>
<td><a href="https://huggingface.co/madebyollin/sdxl-vae-fp16-fix">SDXL-VAE-FP16-FIX</a></td>
</tr>
<tr>
<td>Type</td>
<td>LoRA</td>
</tr>
</table>
</div>
<!-- END Model Info -->
|
han1997/mamba-2.8b-slimpj-hf
|
han1997
| 2024-06-03T00:48:58Z | 360 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mamba",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-09T20:55:34Z |
---
library_name: transformers
license: apache-2.0
---
The following content is mostly from https://huggingface.co/state-spaces/mamba-2.8b-hf
# Mamba
<!-- Provide a quick summary of what the model is/does. -->
This repository contains the `transfromers` compatible `mamba-2.8b-slimpj`. The checkpoints are untouched, but the full `config.json` and tokenizer are pushed to this repo.
# Usage
You need to install `transformers` from `main` until `transformers=4.39.0` is released.
```bash
pip install git+https://github.com/huggingface/transformers@main
```
We also recommend you to install both `causal_conv_1d` and `mamba-ssm` using:
```bash
pip install causal-conv1d>=1.2.0
pip install mamba-ssm
```
If any of these two is not installed, the "eager" implementation will be used. Otherwise the more optimised `cuda` kernels will be used.
## Generation
You can use the classic `generate` API:
```python
>>> from transformers import MambaConfig, MambaForCausalLM, AutoTokenizer
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("han1997/mamba-2.8b-slimpj-hf")
>>> model = MambaForCausalLM.from_pretrained("han1997/mamba-2.8b-slimpj-hf")
>>> input_ids = tokenizer("Hey how are you doing?", return_tensors="pt")["input_ids"]
>>> out = model.generate(input_ids, max_new_tokens=10)
>>> print(tokenizer.batch_decode(out))
["Hey how are you doing?\n\nI'm doing great.\n\nI"]
```
## PEFT finetuning example
In order to finetune using the `peft` library, we recommend keeping the model in float32!
```python
from datasets import load_dataset
from trl import SFTTrainer
from peft import LoraConfig
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments
tokenizer = AutoTokenizer.from_pretrained("han1997/mamba-2.8b-slimpj-hf")
model = AutoModelForCausalLM.from_pretrained("han1997/mamba-2.8b-slimpj-hf")
dataset = load_dataset("Abirate/english_quotes", split="train")
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=3,
per_device_train_batch_size=4,
logging_dir='./logs',
logging_steps=10,
learning_rate=2e-3
)
lora_config = LoraConfig(
r=8,
target_modules=["x_proj", "embeddings", "in_proj", "out_proj"],
task_type="CAUSAL_LM",
bias="none"
)
trainer = SFTTrainer(
model=model,
tokenizer=tokenizer,
args=training_args,
peft_config=lora_config,
train_dataset=dataset,
dataset_text_field="quote",
)
trainer.train()
```
|
QuantFactory/Nous-Hermes-2-Mistral-7B-DPO-GGUF
|
QuantFactory
| 2024-04-12T05:35:26Z | 360 | 0 | null |
[
"gguf",
"mistral",
"instruct",
"finetune",
"DPO",
"RLHF",
"synthetic data",
"text-generation",
"en",
"base_model:NousResearch/Nous-Hermes-2-Mistral-7B-DPO",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2024-04-11T17:21:11Z |
---
pipeline_tag: text-generation
tags:
- mistral
- instruct
- finetune
- DPO
- RLHF
- synthetic data
base_model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
inference: false
license: apache-2.0
language:
- en
---
# Nous-Hermes-2-Mistral-7B-DPO-GGUF
- Quantized version of [Nous-Hermes-2-Mistral-7B-DPO](https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO)
- Created using llama.cpp
## Available Quants
- IQ3_S
- Q2_K
- Q3_K_L
- Q3_K_M
- Q3_K_S
- Q4_0
- Q4_K_M
- Q4_K_S
- Q5_0
- Q5_K_M
- Q5_K_S
- Q6_K
- Q8_0
|
alokabhishek/Meta-Llama-3-8B-Instruct-bnb-4bit
|
alokabhishek
| 2024-04-26T07:37:53Z | 360 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"4bit",
"bnb",
"bitsandbytes",
"llama-3",
"facebook",
"meta",
"8b",
"quantized",
"conversational",
"arxiv:2305.14314",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2024-04-25T23:28:20Z |
---
library_name: transformers
tags:
- 4bit
- bnb
- bitsandbytes
- llama
- llama-3
- facebook
- meta
- 8b
- quantized
license: other
license_name: llama3
license_link: LICENSE
pipeline_tag: text-generation
---
# Model Card for alokabhishek/Meta-Llama-3-8B-Instruct-bnb-4bit
<!-- Provide a quick summary of what the model is/does. -->
This repo contains 4-bit quantized (using bitsandbytes) model of Meta's Meta-Llama-3-8B-Instruct
## Model Details
- Model creator: [Meta](https://huggingface.co/meta-llama)
- Original model: [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
### About 4 bit quantization using bitsandbytes
QLoRA: Efficient Finetuning of Quantized LLMs: [arXiv - QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/abs/2305.14314)
Hugging Face Blog post on 4-bit quantization using bitsandbytes: [Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA](https://huggingface.co/blog/4bit-transformers-bitsandbytes)
bitsandbytes github repo: [bitsandbytes github repo](https://github.com/TimDettmers/bitsandbytes)
# How to Get Started with the Model
Use the code below to get started with the model.
## How to run from Python code
#### Use a pipeline as a high-level helper
```python
import transformers
import torch
model_id = "alokabhishek/Meta-Llama-3-8B-Instruct-bnb-4bit"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
prompt_instruction = "You are a virtual assistant with advanced expertise in a broad spectrum of topics, equipped to utilize high-level critical thinking, cognitive skills, creativity, and innovation. Your goal is to deliver the most straightforward and accurate answer possible for each question, ensuring high-quality and useful responses for the user. "
user_prompt = "Why is Hulk always angry?"
chat_messages = [
{"role": "system", "content": str(prompt_instruction)},
{"role": "user", "content": str(user_prompt)},
]
prompt = pipeline.tokenizer.apply_chat_template(
chat_messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
output = pipeline(
prompt,
do_sample=True,
max_new_tokens=1024,
temperature=1,
top_k=50,
top_p=1,
num_return_sequences=1,
pad_token_id=text_generation_pipeline.tokenizer.pad_token_id,
eos_token_id=terminators,
)
print(output[0]["generated_text"][len(prompt):])
```
## Meta Llama 3 Original Model Card:
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 2024.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase.
### Use with transformers
You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the `generate()` function. Let's see examples of both.
#### Transformers pipeline
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
#### Transformers AutoModelForCausalLM
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
### Use with `llama3`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct --include "original/*" --local-dir Meta-Llama-3-8B-Instruct
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
|
tanganke/clip-vit-base-patch32_mnist
|
tanganke
| 2024-04-28T11:05:32Z | 360 | 0 |
transformers
|
[
"transformers",
"safetensors",
"clip_vision_model",
"feature-extraction",
"dataset:mnist",
"base_model:openai/clip-vit-base-patch32",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2024-04-28T09:56:50Z |
---
base_model:
- openai/clip-vit-base-patch32
datasets:
- mnist
metrics:
- accuracy
---
# Model Card: tanganke/clip-vit-base-patch32_mnist
## Model Details
- Architecture: ViT-Base with patch size 32
- Training Data: MNIST dataset
## Training Details
Adam Optimizer with a constant learning rate 1e-5 for 4000 steps training (batch_size=32).
Only the vision encoder is fine-tuned.
## Evaluation Results
- pre-trained: 0.4759327471256256
- fine-tuned: 0.9957262277603149
## Usage
load vision model
```python
from transformers import CLIPVisionModel
vision_model = CLIPVisionModel.from_pretrained('tanganke/clip-vit-base-patch32_mnist')
```
substitute the vision encoder of clip
```python
from transformers import CLIPModel
clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
clip_model.vision_model.load_state_dict(vision_model.vision_model.state_dict())
```
|
mradermacher/llama-3-spicy-8B-GGUF
|
mradermacher
| 2024-05-14T15:41:18Z | 360 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:nbeerbower/llama-3-spicy-8B",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null | 2024-05-14T14:39:17Z |
---
base_model: nbeerbower/llama-3-spicy-8B
language:
- en
library_name: transformers
license: llama3
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hfhfix -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/nbeerbower/llama-3-spicy-8B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-spicy-8B-GGUF/resolve/main/llama-3-spicy-8B.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
duyntnet/Antler-7B-evolve-imatrix-GGUF
|
duyntnet
| 2024-05-19T16:05:54Z | 360 | 0 |
transformers
|
[
"transformers",
"gguf",
"imatrix",
"Antler-7B-evolve",
"text-generation",
"en",
"license:other",
"region:us"
] |
text-generation
| 2024-05-19T14:07:03Z |
---
license: other
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- transformers
- gguf
- imatrix
- Antler-7B-evolve
---
Quantizations of https://huggingface.co/Elizezen/Antler-7B-evolve
# From original readme
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit), using **Evolutionary Model Merging**.
Generally better than Antler-7B at writing novels, especially at maintaining context, but can fall short on eroticism compared to the original model. It also tends to generate eos tokens quite early, which I'm currently working on improving.
## Example
### Input
```
その日の昼下がり。私とあやはお揃いの浴衣を身に纏い、冬祭りを楽しむために町へと繰り出していた。むろん、忍者の素性を隠すために皆変装している。
普段着慣れない服装のため少々落ち着かないものの、帯が緩んだり着崩れしないよう意識しながら歩く。するとあやも同じようにぎこちなく歩いているのが分かった。
やがて街並みは活気に満ち始め、道行く人々の賑やかな声が聞こえてくる。
広場に到着すると、そこは大勢の人々でにぎわい、色とりどりの提灯が辺りを照らしていた。様々な出店が並び、大勢の子供達とその親御さんが駆け回っている。
広場の中央付近にある舞台では催し物が開催されており、太鼓の音に合わせて歌っている演者たちがいた。
「わぁ～、きれい！」
目を輝かせて辺りを見回すあや。こうしてみると年相応の子どもに見える。
「こら、勝手に走り回らない」
「えへへ～ごめんなさい」
たしなめられながらも、顔は綻んでいる様子からも分かる通り、彼女も楽しみにしているのは明らかだろう。
あやが楽しそうだと、私も嬉しい。不思議なものだな。今までこんな気持ちになったことはなかった。
思わず私まで笑顔になってしまう。
「このちゃん、このちゃん！見て！このちょうちん可愛い～！」
「おお」
彼女に袖を引かれて連れて行かれたのは、可愛らしい動物の絵柄があしらわれた行燈。どうやら売店が出しているらしい。
屋台では
```
### Output
```
屋台では焼きそばや焼き鳥などの定番と並んで、冬限定の鍋専門店なども出ていた。
「あやはなにか食べたいものあるか？」
「えっとね～、お好み焼きとか……」
「お好み焼き？」
私は初めて聞く料理名に首を傾げる。あやは目をキラキラさせながら、その料理について説明してくれた。
小麦粉の生地にキャベツや豚肉、天かす、そしてお好み焼きソースをかけて焼いた、大阪名物の料理らしい。
「それはまた面白そうなものだな。ではそこの屋台に行ってみよう」
私達は目星をつけた屋台へ向かうことにした。
お好み焼きの屋台は、予想以上の盛況ぶりで行列ができていた。しかし、並ぶこと30分ほどで私たちの番がやってくる。
「おばちゃん、これください」
「あいよ！ちょっと待ってな！」
屋台のおばちゃんは威勢のいい声で返事をすると、手慣れた様子で鉄板の上でお好み焼きを焼き上げる。
「これがお好み焼きだよ」
出来上がったお好み焼きを手にしたあやが、うっとりとした様子でそう言った。
「ほう。見るからに美味しそうだ」
私もその色合いに誘われるようにして、一口頬
```
|
scenario-labs/CyberRealistic_V4.2
|
scenario-labs
| 2024-05-23T14:35:01Z | 360 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-05-23T14:35:01Z |
---
library_name: diffusers
---
Prepared from [CyberRealistic](https://civitai.com/models/15003?modelVersionId=372799)
|
mradermacher/CabraCommand-r-v01-16k-GGUF
|
mradermacher
| 2024-06-01T16:30:41Z | 360 | 0 |
transformers
|
[
"transformers",
"gguf",
"pt",
"base_model:nicolasdec/CabraCommand-r-v01-16k",
"endpoints_compatible",
"region:us"
] | null | 2024-05-29T03:59:44Z |
---
base_model: nicolasdec/CabraCommand-r-v01-16k
language:
- pt
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/nicolasdec/CabraCommand-r-v01-16k
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/CabraCommand-r-v01-16k-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/CabraCommand-r-v01-16k-GGUF/resolve/main/CabraCommand-r-v01-16k.Q2_K.gguf) | Q2_K | 13.9 | |
| [GGUF](https://huggingface.co/mradermacher/CabraCommand-r-v01-16k-GGUF/resolve/main/CabraCommand-r-v01-16k.IQ3_XS.gguf) | IQ3_XS | 15.2 | |
| [GGUF](https://huggingface.co/mradermacher/CabraCommand-r-v01-16k-GGUF/resolve/main/CabraCommand-r-v01-16k.IQ3_S.gguf) | IQ3_S | 16.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/CabraCommand-r-v01-16k-GGUF/resolve/main/CabraCommand-r-v01-16k.Q3_K_S.gguf) | Q3_K_S | 16.0 | |
| [GGUF](https://huggingface.co/mradermacher/CabraCommand-r-v01-16k-GGUF/resolve/main/CabraCommand-r-v01-16k.IQ3_M.gguf) | IQ3_M | 16.8 | |
| [GGUF](https://huggingface.co/mradermacher/CabraCommand-r-v01-16k-GGUF/resolve/main/CabraCommand-r-v01-16k.Q3_K_M.gguf) | Q3_K_M | 17.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/CabraCommand-r-v01-16k-GGUF/resolve/main/CabraCommand-r-v01-16k.Q3_K_L.gguf) | Q3_K_L | 19.2 | |
| [GGUF](https://huggingface.co/mradermacher/CabraCommand-r-v01-16k-GGUF/resolve/main/CabraCommand-r-v01-16k.IQ4_XS.gguf) | IQ4_XS | 19.4 | |
| [GGUF](https://huggingface.co/mradermacher/CabraCommand-r-v01-16k-GGUF/resolve/main/CabraCommand-r-v01-16k.Q4_K_S.gguf) | Q4_K_S | 20.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/CabraCommand-r-v01-16k-GGUF/resolve/main/CabraCommand-r-v01-16k.Q4_K_M.gguf) | Q4_K_M | 21.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/CabraCommand-r-v01-16k-GGUF/resolve/main/CabraCommand-r-v01-16k.Q5_K_S.gguf) | Q5_K_S | 24.4 | |
| [GGUF](https://huggingface.co/mradermacher/CabraCommand-r-v01-16k-GGUF/resolve/main/CabraCommand-r-v01-16k.Q5_K_M.gguf) | Q5_K_M | 25.1 | |
| [GGUF](https://huggingface.co/mradermacher/CabraCommand-r-v01-16k-GGUF/resolve/main/CabraCommand-r-v01-16k.Q6_K.gguf) | Q6_K | 28.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/CabraCommand-r-v01-16k-GGUF/resolve/main/CabraCommand-r-v01-16k.Q8_0.gguf) | Q8_0 | 37.3 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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 -->
|
RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf
|
RichardErkhov
| 2024-05-30T00:03:24Z | 360 | 0 | null |
[
"gguf",
"region:us"
] | null | 2024-05-29T18:11:35Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
ennodata-13b-8bit-raw-15epoch - GGUF
- Model creator: https://huggingface.co/Enno-Ai/
- Original model: https://huggingface.co/Enno-Ai/ennodata-13b-8bit-raw-15epoch/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [ennodata-13b-8bit-raw-15epoch.Q2_K.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.Q2_K.gguf) | Q2_K | 4.52GB |
| [ennodata-13b-8bit-raw-15epoch.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.IQ3_XS.gguf) | IQ3_XS | 4.99GB |
| [ennodata-13b-8bit-raw-15epoch.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.IQ3_S.gguf) | IQ3_S | 5.27GB |
| [ennodata-13b-8bit-raw-15epoch.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.Q3_K_S.gguf) | Q3_K_S | 5.27GB |
| [ennodata-13b-8bit-raw-15epoch.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.IQ3_M.gguf) | IQ3_M | 5.57GB |
| [ennodata-13b-8bit-raw-15epoch.Q3_K.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.Q3_K.gguf) | Q3_K | 5.9GB |
| [ennodata-13b-8bit-raw-15epoch.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.Q3_K_M.gguf) | Q3_K_M | 5.9GB |
| [ennodata-13b-8bit-raw-15epoch.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.Q3_K_L.gguf) | Q3_K_L | 6.45GB |
| [ennodata-13b-8bit-raw-15epoch.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.IQ4_XS.gguf) | IQ4_XS | 6.54GB |
| [ennodata-13b-8bit-raw-15epoch.Q4_0.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.Q4_0.gguf) | Q4_0 | 6.86GB |
| [ennodata-13b-8bit-raw-15epoch.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.IQ4_NL.gguf) | IQ4_NL | 6.9GB |
| [ennodata-13b-8bit-raw-15epoch.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.Q4_K_S.gguf) | Q4_K_S | 6.91GB |
| [ennodata-13b-8bit-raw-15epoch.Q4_K.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.Q4_K.gguf) | Q4_K | 7.33GB |
| [ennodata-13b-8bit-raw-15epoch.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.Q4_K_M.gguf) | Q4_K_M | 7.33GB |
| [ennodata-13b-8bit-raw-15epoch.Q4_1.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.Q4_1.gguf) | Q4_1 | 7.61GB |
| [ennodata-13b-8bit-raw-15epoch.Q5_0.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.Q5_0.gguf) | Q5_0 | 8.36GB |
| [ennodata-13b-8bit-raw-15epoch.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.Q5_K_S.gguf) | Q5_K_S | 8.36GB |
| [ennodata-13b-8bit-raw-15epoch.Q5_K.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.Q5_K.gguf) | Q5_K | 8.6GB |
| [ennodata-13b-8bit-raw-15epoch.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.Q5_K_M.gguf) | Q5_K_M | 8.6GB |
| [ennodata-13b-8bit-raw-15epoch.Q5_1.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.Q5_1.gguf) | Q5_1 | 9.1GB |
| [ennodata-13b-8bit-raw-15epoch.Q6_K.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.Q6_K.gguf) | Q6_K | 9.95GB |
| [ennodata-13b-8bit-raw-15epoch.Q8_0.gguf](https://huggingface.co/RichardErkhov/Enno-Ai_-_ennodata-13b-8bit-raw-15epoch-gguf/blob/main/ennodata-13b-8bit-raw-15epoch.Q8_0.gguf) | Q8_0 | 12.88GB |
Original model description:
Entry not found
|
Felladrin/gguf-Aira-2-124M
|
Felladrin
| 2024-06-07T12:24:48Z | 360 | 0 | null |
[
"gguf",
"base_model:nicholasKluge/Aira-2-124M",
"license:apache-2.0",
"region:us"
] | null | 2024-06-07T10:37:32Z |
---
license: apache-2.0
base_model: nicholasKluge/Aira-2-124M
---
GGUF version of [nicholasKluge/Aira-2-124M](https://huggingface.co/nicholasKluge/Aira-2-124M).
|
Xuhui/ToxDect-roberta-large
|
Xuhui
| 2021-11-07T16:35:03Z | 359 | 4 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"arxiv:2102.00086",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
-
-
thumbnail:
tags:
-
-
-
license:
datasets:
-
-
metrics:
-
-
---
# Toxic language detection
## Model description
A toxic language detection model trained on tweets. The base model is Roberta-large. For more information,
including the **training data**, **limitations and bias**, please refer to the [paper](https://arxiv.org/pdf/2102.00086.pdf) and
Github [repo](https://github.com/XuhuiZhou/Toxic_Debias) for more details.
#### How to use
Note that LABEL_1 means toxic and LABEL_0 means non-toxic in the output.
```python
from transformers import pipeline
classifier = pipeline("text-classification",model='Xuhui/ToxDect-roberta-large', return_all_scores=True)
prediction = classifier("You are f**king stupid!", )
print(prediction)
"""
Output:
[[{'label': 'LABEL_0', 'score': 0.002632011892274022}, {'label': 'LABEL_1', 'score': 0.9973680377006531}]]
"""
```
## Training procedure
The random seed for this model is 22. For other details, please refer to the Github [repo](https://github.com/XuhuiZhou/Toxic_Debias) for more details.
### BibTeX entry and citation info
```bibtex
@inproceedings{zhou-etal-2020-debiasing,
title = {Challenges in Automated Debiasing for Toxic Language Detection},
author = {Zhou, Xuhui and Sap, Maarten and Swayamdipta, Swabha and Choi, Yejin and Smith, Noah A.},
booktitle = {EACL},
abbr = {EACL},
html = {https://www.aclweb.org/anthology/2021.eacl-main.274.pdf},
code = {https://github.com/XuhuiZhou/Toxic_Debias},
year = {2021},
bibtex_show = {true},
selected = {true}
}
```
|
madlag/albert-base-v2-squad
|
madlag
| 2021-05-05T13:54:33Z | 359 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
Albert v2 finetuned on SQuAD v1.
Trained using the [nn_pruning](https://github.com/huggingface/nn_pruning/tree/main/examples/question_answering) script, with pruning disabled.
[Original results](https://github.com/google-research/albert) are F1=90.2, EM=83.2, we improved them to:
```{
"exact_match": 83.74645222327341,
"f1": 90.78776054621733
}```
|
speechbrain/asr-crdnn-rnnlm-librispeech
|
speechbrain
| 2024-02-19T13:10:14Z | 359 | 10 |
speechbrain
|
[
"speechbrain",
"automatic-speech-recognition",
"CTC",
"Attention",
"pytorch",
"en",
"dataset:librispeech",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: "en"
thumbnail:
tags:
- automatic-speech-recognition
- CTC
- Attention
- pytorch
- speechbrain
license: "apache-2.0"
datasets:
- librispeech
metrics:
- wer
- cer
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# CRDNN with CTC/Attention and RNNLM trained on LibriSpeech
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on LibriSpeech (EN) within
SpeechBrain. For a better experience we encourage you to learn more about
[SpeechBrain](https://speechbrain.github.io).
The performance of the model is the following:
| Release | Test WER | GPUs |
|:-------------:|:--------------:| :--------:|
| 20-05-22 | 3.09 | 1xV100 32GB |
## Pipeline description
This ASR system is composed with 3 different but linked blocks:
- Tokenizer (unigram) that transforms words into subword units and trained with
the train transcriptions of LibriSpeech.
- Neural language model (RNNLM) trained on the full 10M words dataset.
- Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of
N blocks of convolutional neural networks with normalisation and pooling on the
frequency domain. Then, a bidirectional LSTM is connected to a final DNN to obtain
the final acoustic representation that is given to the CTC and attention decoders.
The system is trained with recordings sampled at 16kHz (single channel).
The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed.
## Install SpeechBrain
First of all, please install SpeechBrain with the following command:
```
pip install speechbrain
```
Please notice that we encourage you to read our tutorials and learn more about
[SpeechBrain](https://speechbrain.github.io).
### Transcribing your own audio files (in English)
```python
from speechbrain.inference.ASR import EncoderDecoderASR
asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-crdnn-rnnlm-librispeech", savedir="pretrained_models/asr-crdnn-rnnlm-librispeech")
asr_model.transcribe_file('speechbrain/asr-crdnn-rnnlm-librispeech/example.wav')
```
### Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
## Parallel Inference on a Batch
Please, [see this Colab notebook](https://colab.research.google.com/drive/1hX5ZI9S4jHIjahFCZnhwwQmFoGAi3tmu?usp=sharing) to figure out how to transcribe in parallel a batch of input sentences using a pre-trained model.
### Training
The model was trained with SpeechBrain (Commit hash: '2abd9f01').
To train it from scratch follow these steps:
1. Clone SpeechBrain:
```bash
git clone https://github.com/speechbrain/speechbrain/
```
2. Install it:
```bash
cd speechbrain
pip install -r requirements.txt
pip install -e .
```
3. Run Training:
```bash
cd recipes/LibriSpeech/ASR/seq2seq/
python train.py hparams/train_BPE_1000.yaml --data_folder=your_data_folder
```
You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1SAndjcThdkO-YQF8kvwPOXlQ6LMT71vt?usp=sharing).
### Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
# **About SpeechBrain**
- Website: https://speechbrain.github.io/
- Code: https://github.com/speechbrain/speechbrain/
- HuggingFace: https://huggingface.co/speechbrain/
# **Citing SpeechBrain**
Please, cite SpeechBrain if you use it for your research or business.
```bibtex
@misc{speechbrain,
title={{SpeechBrain}: A General-Purpose Speech Toolkit},
author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
year={2021},
eprint={2106.04624},
archivePrefix={arXiv},
primaryClass={eess.AS},
note={arXiv:2106.04624}
}
```
|
circulus/sd-anireal-v2.8
|
circulus
| 2023-03-02T02:18:10Z | 359 | 2 |
diffusers
|
[
"diffusers",
"generative ai",
"stable-diffusion",
"image-to-image",
"realism",
"art",
"text-to-image",
"en",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-01T03:40:00Z |
---
license: gpl-3.0
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- generative ai
- stable-diffusion
- image-to-image
- realism
- art
---
Anireal v2.8
Finetuned Stable Diffusion 1.5 for generating images
You can test this model here >
https://eva.circul.us/index.html
|
timm/ecaresnet50d_pruned.miil_in1k
|
timm
| 2024-02-10T23:38:14Z | 359 | 0 |
timm
|
[
"timm",
"pytorch",
"safetensors",
"image-classification",
"arxiv:1512.03385",
"arxiv:1910.03151",
"arxiv:1812.01187",
"license:apache-2.0",
"region:us"
] |
image-classification
| 2023-04-05T17:57:09Z |
---
license: apache-2.0
library_name: timm
tags:
- image-classification
- timm
---
# Model card for ecaresnet50d_pruned.miil_in1k
A ECA-ResNet-D image classification model with Efficient Channel Attention.
This model features:
* ReLU activations
* 3-layer stem of 3x3 convolutions with pooling
* 2x2 average pool + 1x1 convolution shortcut downsample
* Efficient Channel Attention
Trained on ImageNet-1k by Alibaba MIIL.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 19.9
- GMACs: 2.5
- Activations (M): 6.4
- Image size: train = 224 x 224, test = 288 x 288
- **Papers:**
- Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385
- ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks: https://arxiv.org/abs/1910.03151
- Bag of Tricks for Image Classification with Convolutional Neural Networks: https://arxiv.org/abs/1812.01187
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('ecaresnet50d_pruned.miil_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'ecaresnet50d_pruned.miil_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 64, 112, 112])
# torch.Size([1, 19, 56, 56])
# torch.Size([1, 171, 28, 28])
# torch.Size([1, 818, 14, 14])
# torch.Size([1, 2022, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'ecaresnet50d_pruned.miil_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 2022, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
|model |img_size|top1 |top5 |param_count|gmacs|macts|img/sec|
|------------------------------------------|--------|-----|-----|-----------|-----|-----|-------|
|[seresnextaa101d_32x8d.sw_in12k_ft_in1k_288](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k_288)|320 |86.72|98.17|93.6 |35.2 |69.7 |451 |
|[seresnextaa101d_32x8d.sw_in12k_ft_in1k_288](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k_288)|288 |86.51|98.08|93.6 |28.5 |56.4 |560 |
|[seresnextaa101d_32x8d.sw_in12k_ft_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k)|288 |86.49|98.03|93.6 |28.5 |56.4 |557 |
|[seresnextaa101d_32x8d.sw_in12k_ft_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k)|224 |85.96|97.82|93.6 |17.2 |34.2 |923 |
|[resnext101_32x32d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x32d.fb_wsl_ig1b_ft_in1k)|224 |85.11|97.44|468.5 |87.3 |91.1 |254 |
|[resnetrs420.tf_in1k](https://huggingface.co/timm/resnetrs420.tf_in1k)|416 |85.0 |97.12|191.9 |108.4|213.8|134 |
|[ecaresnet269d.ra2_in1k](https://huggingface.co/timm/ecaresnet269d.ra2_in1k)|352 |84.96|97.22|102.1 |50.2 |101.2|291 |
|[ecaresnet269d.ra2_in1k](https://huggingface.co/timm/ecaresnet269d.ra2_in1k)|320 |84.73|97.18|102.1 |41.5 |83.7 |353 |
|[resnetrs350.tf_in1k](https://huggingface.co/timm/resnetrs350.tf_in1k)|384 |84.71|96.99|164.0 |77.6 |154.7|183 |
|[seresnextaa101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.ah_in1k)|288 |84.57|97.08|93.6 |28.5 |56.4 |557 |
|[resnetrs200.tf_in1k](https://huggingface.co/timm/resnetrs200.tf_in1k)|320 |84.45|97.08|93.2 |31.5 |67.8 |446 |
|[resnetrs270.tf_in1k](https://huggingface.co/timm/resnetrs270.tf_in1k)|352 |84.43|96.97|129.9 |51.1 |105.5|280 |
|[seresnext101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101d_32x8d.ah_in1k)|288 |84.36|96.92|93.6 |27.6 |53.0 |595 |
|[seresnet152d.ra2_in1k](https://huggingface.co/timm/seresnet152d.ra2_in1k)|320 |84.35|97.04|66.8 |24.1 |47.7 |610 |
|[resnetrs350.tf_in1k](https://huggingface.co/timm/resnetrs350.tf_in1k)|288 |84.3 |96.94|164.0 |43.7 |87.1 |333 |
|[resnext101_32x8d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_swsl_ig1b_ft_in1k)|224 |84.28|97.17|88.8 |16.5 |31.2 |1100 |
|[resnetrs420.tf_in1k](https://huggingface.co/timm/resnetrs420.tf_in1k)|320 |84.24|96.86|191.9 |64.2 |126.6|228 |
|[seresnext101_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101_32x8d.ah_in1k)|288 |84.19|96.87|93.6 |27.2 |51.6 |613 |
|[resnext101_32x16d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_wsl_ig1b_ft_in1k)|224 |84.18|97.19|194.0 |36.3 |51.2 |581 |
|[resnetaa101d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa101d.sw_in12k_ft_in1k)|288 |84.11|97.11|44.6 |15.1 |29.0 |1144 |
|[resnet200d.ra2_in1k](https://huggingface.co/timm/resnet200d.ra2_in1k)|320 |83.97|96.82|64.7 |31.2 |67.3 |518 |
|[resnetrs200.tf_in1k](https://huggingface.co/timm/resnetrs200.tf_in1k)|256 |83.87|96.75|93.2 |20.2 |43.4 |692 |
|[seresnextaa101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnextaa101d_32x8d.ah_in1k)|224 |83.86|96.65|93.6 |17.2 |34.2 |923 |
|[resnetrs152.tf_in1k](https://huggingface.co/timm/resnetrs152.tf_in1k)|320 |83.72|96.61|86.6 |24.3 |48.1 |617 |
|[seresnet152d.ra2_in1k](https://huggingface.co/timm/seresnet152d.ra2_in1k)|256 |83.69|96.78|66.8 |15.4 |30.6 |943 |
|[seresnext101d_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101d_32x8d.ah_in1k)|224 |83.68|96.61|93.6 |16.7 |32.0 |986 |
|[resnet152d.ra2_in1k](https://huggingface.co/timm/resnet152d.ra2_in1k)|320 |83.67|96.74|60.2 |24.1 |47.7 |706 |
|[resnetrs270.tf_in1k](https://huggingface.co/timm/resnetrs270.tf_in1k)|256 |83.59|96.61|129.9 |27.1 |55.8 |526 |
|[seresnext101_32x8d.ah_in1k](https://huggingface.co/timm/seresnext101_32x8d.ah_in1k)|224 |83.58|96.4 |93.6 |16.5 |31.2 |1013 |
|[resnetaa101d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa101d.sw_in12k_ft_in1k)|224 |83.54|96.83|44.6 |9.1 |17.6 |1864 |
|[resnet152.a1h_in1k](https://huggingface.co/timm/resnet152.a1h_in1k)|288 |83.46|96.54|60.2 |19.1 |37.3 |904 |
|[resnext101_32x16d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_swsl_ig1b_ft_in1k)|224 |83.35|96.85|194.0 |36.3 |51.2 |582 |
|[resnet200d.ra2_in1k](https://huggingface.co/timm/resnet200d.ra2_in1k)|256 |83.23|96.53|64.7 |20.0 |43.1 |809 |
|[resnext101_32x4d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x4d.fb_swsl_ig1b_ft_in1k)|224 |83.22|96.75|44.2 |8.0 |21.2 |1814 |
|[resnext101_64x4d.c1_in1k](https://huggingface.co/timm/resnext101_64x4d.c1_in1k)|288 |83.16|96.38|83.5 |25.7 |51.6 |590 |
|[resnet152d.ra2_in1k](https://huggingface.co/timm/resnet152d.ra2_in1k)|256 |83.14|96.38|60.2 |15.4 |30.5 |1096 |
|[resnet101d.ra2_in1k](https://huggingface.co/timm/resnet101d.ra2_in1k)|320 |83.02|96.45|44.6 |16.5 |34.8 |992 |
|[ecaresnet101d.miil_in1k](https://huggingface.co/timm/ecaresnet101d.miil_in1k)|288 |82.98|96.54|44.6 |13.4 |28.2 |1077 |
|[resnext101_64x4d.tv_in1k](https://huggingface.co/timm/resnext101_64x4d.tv_in1k)|224 |82.98|96.25|83.5 |15.5 |31.2 |989 |
|[resnetrs152.tf_in1k](https://huggingface.co/timm/resnetrs152.tf_in1k)|256 |82.86|96.28|86.6 |15.6 |30.8 |951 |
|[resnext101_32x8d.tv2_in1k](https://huggingface.co/timm/resnext101_32x8d.tv2_in1k)|224 |82.83|96.22|88.8 |16.5 |31.2 |1099 |
|[resnet152.a1h_in1k](https://huggingface.co/timm/resnet152.a1h_in1k)|224 |82.8 |96.13|60.2 |11.6 |22.6 |1486 |
|[resnet101.a1h_in1k](https://huggingface.co/timm/resnet101.a1h_in1k)|288 |82.8 |96.32|44.6 |13.0 |26.8 |1291 |
|[resnet152.a1_in1k](https://huggingface.co/timm/resnet152.a1_in1k)|288 |82.74|95.71|60.2 |19.1 |37.3 |905 |
|[resnext101_32x8d.fb_wsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_wsl_ig1b_ft_in1k)|224 |82.69|96.63|88.8 |16.5 |31.2 |1100 |
|[resnet152.a2_in1k](https://huggingface.co/timm/resnet152.a2_in1k)|288 |82.62|95.75|60.2 |19.1 |37.3 |904 |
|[resnetaa50d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa50d.sw_in12k_ft_in1k)|288 |82.61|96.49|25.6 |8.9 |20.6 |1729 |
|[resnet61q.ra2_in1k](https://huggingface.co/timm/resnet61q.ra2_in1k)|288 |82.53|96.13|36.8 |9.9 |21.5 |1773 |
|[wide_resnet101_2.tv2_in1k](https://huggingface.co/timm/wide_resnet101_2.tv2_in1k)|224 |82.5 |96.02|126.9 |22.8 |21.2 |1078 |
|[resnext101_64x4d.c1_in1k](https://huggingface.co/timm/resnext101_64x4d.c1_in1k)|224 |82.46|95.92|83.5 |15.5 |31.2 |987 |
|[resnet51q.ra2_in1k](https://huggingface.co/timm/resnet51q.ra2_in1k)|288 |82.36|96.18|35.7 |8.1 |20.9 |1964 |
|[ecaresnet50t.ra2_in1k](https://huggingface.co/timm/ecaresnet50t.ra2_in1k)|320 |82.35|96.14|25.6 |8.8 |24.1 |1386 |
|[resnet101.a1_in1k](https://huggingface.co/timm/resnet101.a1_in1k)|288 |82.31|95.63|44.6 |13.0 |26.8 |1291 |
|[resnetrs101.tf_in1k](https://huggingface.co/timm/resnetrs101.tf_in1k)|288 |82.29|96.01|63.6 |13.6 |28.5 |1078 |
|[resnet152.tv2_in1k](https://huggingface.co/timm/resnet152.tv2_in1k)|224 |82.29|96.0 |60.2 |11.6 |22.6 |1484 |
|[wide_resnet50_2.racm_in1k](https://huggingface.co/timm/wide_resnet50_2.racm_in1k)|288 |82.27|96.06|68.9 |18.9 |23.8 |1176 |
|[resnet101d.ra2_in1k](https://huggingface.co/timm/resnet101d.ra2_in1k)|256 |82.26|96.07|44.6 |10.6 |22.2 |1542 |
|[resnet101.a2_in1k](https://huggingface.co/timm/resnet101.a2_in1k)|288 |82.24|95.73|44.6 |13.0 |26.8 |1290 |
|[seresnext50_32x4d.racm_in1k](https://huggingface.co/timm/seresnext50_32x4d.racm_in1k)|288 |82.2 |96.14|27.6 |7.0 |23.8 |1547 |
|[ecaresnet101d.miil_in1k](https://huggingface.co/timm/ecaresnet101d.miil_in1k)|224 |82.18|96.05|44.6 |8.1 |17.1 |1771 |
|[resnext50_32x4d.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnext50_32x4d.fb_swsl_ig1b_ft_in1k)|224 |82.17|96.22|25.0 |4.3 |14.4 |2943 |
|[ecaresnet50t.a1_in1k](https://huggingface.co/timm/ecaresnet50t.a1_in1k)|288 |82.12|95.65|25.6 |7.1 |19.6 |1704 |
|[resnext50_32x4d.a1h_in1k](https://huggingface.co/timm/resnext50_32x4d.a1h_in1k)|288 |82.03|95.94|25.0 |7.0 |23.8 |1745 |
|[ecaresnet101d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet101d_pruned.miil_in1k)|288 |82.0 |96.15|24.9 |5.8 |12.7 |1787 |
|[resnet61q.ra2_in1k](https://huggingface.co/timm/resnet61q.ra2_in1k)|256 |81.99|95.85|36.8 |7.8 |17.0 |2230 |
|[resnext101_32x8d.tv2_in1k](https://huggingface.co/timm/resnext101_32x8d.tv2_in1k)|176 |81.98|95.72|88.8 |10.3 |19.4 |1768 |
|[resnet152.a1_in1k](https://huggingface.co/timm/resnet152.a1_in1k)|224 |81.97|95.24|60.2 |11.6 |22.6 |1486 |
|[resnet101.a1h_in1k](https://huggingface.co/timm/resnet101.a1h_in1k)|224 |81.93|95.75|44.6 |7.8 |16.2 |2122 |
|[resnet101.tv2_in1k](https://huggingface.co/timm/resnet101.tv2_in1k)|224 |81.9 |95.77|44.6 |7.8 |16.2 |2118 |
|[resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k)|224 |81.84|96.1 |194.0 |36.3 |51.2 |583 |
|[resnet51q.ra2_in1k](https://huggingface.co/timm/resnet51q.ra2_in1k)|256 |81.78|95.94|35.7 |6.4 |16.6 |2471 |
|[resnet152.a2_in1k](https://huggingface.co/timm/resnet152.a2_in1k)|224 |81.77|95.22|60.2 |11.6 |22.6 |1485 |
|[resnetaa50d.sw_in12k_ft_in1k](https://huggingface.co/timm/resnetaa50d.sw_in12k_ft_in1k)|224 |81.74|96.06|25.6 |5.4 |12.4 |2813 |
|[ecaresnet50t.a2_in1k](https://huggingface.co/timm/ecaresnet50t.a2_in1k)|288 |81.65|95.54|25.6 |7.1 |19.6 |1703 |
|[ecaresnet50d.miil_in1k](https://huggingface.co/timm/ecaresnet50d.miil_in1k)|288 |81.64|95.88|25.6 |7.2 |19.7 |1694 |
|[resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k)|224 |81.62|96.04|88.8 |16.5 |31.2 |1101 |
|[wide_resnet50_2.tv2_in1k](https://huggingface.co/timm/wide_resnet50_2.tv2_in1k)|224 |81.61|95.76|68.9 |11.4 |14.4 |1930 |
|[resnetaa50.a1h_in1k](https://huggingface.co/timm/resnetaa50.a1h_in1k)|288 |81.61|95.83|25.6 |8.5 |19.2 |1868 |
|[resnet101.a1_in1k](https://huggingface.co/timm/resnet101.a1_in1k)|224 |81.5 |95.16|44.6 |7.8 |16.2 |2125 |
|[resnext50_32x4d.a1_in1k](https://huggingface.co/timm/resnext50_32x4d.a1_in1k)|288 |81.48|95.16|25.0 |7.0 |23.8 |1745 |
|[gcresnet50t.ra2_in1k](https://huggingface.co/timm/gcresnet50t.ra2_in1k)|288 |81.47|95.71|25.9 |6.9 |18.6 |2071 |
|[wide_resnet50_2.racm_in1k](https://huggingface.co/timm/wide_resnet50_2.racm_in1k)|224 |81.45|95.53|68.9 |11.4 |14.4 |1929 |
|[resnet50d.a1_in1k](https://huggingface.co/timm/resnet50d.a1_in1k)|288 |81.44|95.22|25.6 |7.2 |19.7 |1908 |
|[ecaresnet50t.ra2_in1k](https://huggingface.co/timm/ecaresnet50t.ra2_in1k)|256 |81.44|95.67|25.6 |5.6 |15.4 |2168 |
|[ecaresnetlight.miil_in1k](https://huggingface.co/timm/ecaresnetlight.miil_in1k)|288 |81.4 |95.82|30.2 |6.8 |13.9 |2132 |
|[resnet50d.ra2_in1k](https://huggingface.co/timm/resnet50d.ra2_in1k)|288 |81.37|95.74|25.6 |7.2 |19.7 |1910 |
|[resnet101.a2_in1k](https://huggingface.co/timm/resnet101.a2_in1k)|224 |81.32|95.19|44.6 |7.8 |16.2 |2125 |
|[seresnet50.ra2_in1k](https://huggingface.co/timm/seresnet50.ra2_in1k)|288 |81.3 |95.65|28.1 |6.8 |18.4 |1803 |
|[resnext50_32x4d.a2_in1k](https://huggingface.co/timm/resnext50_32x4d.a2_in1k)|288 |81.3 |95.11|25.0 |7.0 |23.8 |1746 |
|[seresnext50_32x4d.racm_in1k](https://huggingface.co/timm/seresnext50_32x4d.racm_in1k)|224 |81.27|95.62|27.6 |4.3 |14.4 |2591 |
|[ecaresnet50t.a1_in1k](https://huggingface.co/timm/ecaresnet50t.a1_in1k)|224 |81.26|95.16|25.6 |4.3 |11.8 |2823 |
|[gcresnext50ts.ch_in1k](https://huggingface.co/timm/gcresnext50ts.ch_in1k)|288 |81.23|95.54|15.7 |4.8 |19.6 |2117 |
|[senet154.gluon_in1k](https://huggingface.co/timm/senet154.gluon_in1k)|224 |81.23|95.35|115.1 |20.8 |38.7 |545 |
|[resnet50.a1_in1k](https://huggingface.co/timm/resnet50.a1_in1k)|288 |81.22|95.11|25.6 |6.8 |18.4 |2089 |
|[resnet50_gn.a1h_in1k](https://huggingface.co/timm/resnet50_gn.a1h_in1k)|288 |81.22|95.63|25.6 |6.8 |18.4 |676 |
|[resnet50d.a2_in1k](https://huggingface.co/timm/resnet50d.a2_in1k)|288 |81.18|95.09|25.6 |7.2 |19.7 |1908 |
|[resnet50.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnet50.fb_swsl_ig1b_ft_in1k)|224 |81.18|95.98|25.6 |4.1 |11.1 |3455 |
|[resnext50_32x4d.tv2_in1k](https://huggingface.co/timm/resnext50_32x4d.tv2_in1k)|224 |81.17|95.34|25.0 |4.3 |14.4 |2933 |
|[resnext50_32x4d.a1h_in1k](https://huggingface.co/timm/resnext50_32x4d.a1h_in1k)|224 |81.1 |95.33|25.0 |4.3 |14.4 |2934 |
|[seresnet50.a2_in1k](https://huggingface.co/timm/seresnet50.a2_in1k)|288 |81.1 |95.23|28.1 |6.8 |18.4 |1801 |
|[seresnet50.a1_in1k](https://huggingface.co/timm/seresnet50.a1_in1k)|288 |81.1 |95.12|28.1 |6.8 |18.4 |1799 |
|[resnet152s.gluon_in1k](https://huggingface.co/timm/resnet152s.gluon_in1k)|224 |81.02|95.41|60.3 |12.9 |25.0 |1347 |
|[resnet50.d_in1k](https://huggingface.co/timm/resnet50.d_in1k)|288 |80.97|95.44|25.6 |6.8 |18.4 |2085 |
|[gcresnet50t.ra2_in1k](https://huggingface.co/timm/gcresnet50t.ra2_in1k)|256 |80.94|95.45|25.9 |5.4 |14.7 |2571 |
|[resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k)|224 |80.93|95.73|44.2 |8.0 |21.2 |1814 |
|[resnet50.c1_in1k](https://huggingface.co/timm/resnet50.c1_in1k)|288 |80.91|95.55|25.6 |6.8 |18.4 |2084 |
|[seresnext101_32x4d.gluon_in1k](https://huggingface.co/timm/seresnext101_32x4d.gluon_in1k)|224 |80.9 |95.31|49.0 |8.0 |21.3 |1585 |
|[seresnext101_64x4d.gluon_in1k](https://huggingface.co/timm/seresnext101_64x4d.gluon_in1k)|224 |80.9 |95.3 |88.2 |15.5 |31.2 |918 |
|[resnet50.c2_in1k](https://huggingface.co/timm/resnet50.c2_in1k)|288 |80.86|95.52|25.6 |6.8 |18.4 |2085 |
|[resnet50.tv2_in1k](https://huggingface.co/timm/resnet50.tv2_in1k)|224 |80.85|95.43|25.6 |4.1 |11.1 |3450 |
|[ecaresnet50t.a2_in1k](https://huggingface.co/timm/ecaresnet50t.a2_in1k)|224 |80.84|95.02|25.6 |4.3 |11.8 |2821 |
|[ecaresnet101d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet101d_pruned.miil_in1k)|224 |80.79|95.62|24.9 |3.5 |7.7 |2961 |
|[seresnet33ts.ra2_in1k](https://huggingface.co/timm/seresnet33ts.ra2_in1k)|288 |80.79|95.36|19.8 |6.0 |14.8 |2506 |
|[ecaresnet50d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet50d_pruned.miil_in1k)|288 |80.79|95.58|19.9 |4.2 |10.6 |2349 |
|[resnet50.a2_in1k](https://huggingface.co/timm/resnet50.a2_in1k)|288 |80.78|94.99|25.6 |6.8 |18.4 |2088 |
|[resnet50.b1k_in1k](https://huggingface.co/timm/resnet50.b1k_in1k)|288 |80.71|95.43|25.6 |6.8 |18.4 |2087 |
|[resnext50_32x4d.ra_in1k](https://huggingface.co/timm/resnext50_32x4d.ra_in1k)|288 |80.7 |95.39|25.0 |7.0 |23.8 |1749 |
|[resnetrs101.tf_in1k](https://huggingface.co/timm/resnetrs101.tf_in1k)|192 |80.69|95.24|63.6 |6.0 |12.7 |2270 |
|[resnet50d.a1_in1k](https://huggingface.co/timm/resnet50d.a1_in1k)|224 |80.68|94.71|25.6 |4.4 |11.9 |3162 |
|[eca_resnet33ts.ra2_in1k](https://huggingface.co/timm/eca_resnet33ts.ra2_in1k)|288 |80.68|95.36|19.7 |6.0 |14.8 |2637 |
|[resnet50.a1h_in1k](https://huggingface.co/timm/resnet50.a1h_in1k)|224 |80.67|95.3 |25.6 |4.1 |11.1 |3452 |
|[resnext50d_32x4d.bt_in1k](https://huggingface.co/timm/resnext50d_32x4d.bt_in1k)|288 |80.67|95.42|25.0 |7.4 |25.1 |1626 |
|[resnetaa50.a1h_in1k](https://huggingface.co/timm/resnetaa50.a1h_in1k)|224 |80.63|95.21|25.6 |5.2 |11.6 |3034 |
|[ecaresnet50d.miil_in1k](https://huggingface.co/timm/ecaresnet50d.miil_in1k)|224 |80.61|95.32|25.6 |4.4 |11.9 |2813 |
|[resnext101_64x4d.gluon_in1k](https://huggingface.co/timm/resnext101_64x4d.gluon_in1k)|224 |80.61|94.99|83.5 |15.5 |31.2 |989 |
|[gcresnet33ts.ra2_in1k](https://huggingface.co/timm/gcresnet33ts.ra2_in1k)|288 |80.6 |95.31|19.9 |6.0 |14.8 |2578 |
|[gcresnext50ts.ch_in1k](https://huggingface.co/timm/gcresnext50ts.ch_in1k)|256 |80.57|95.17|15.7 |3.8 |15.5 |2710 |
|[resnet152.a3_in1k](https://huggingface.co/timm/resnet152.a3_in1k)|224 |80.56|95.0 |60.2 |11.6 |22.6 |1483 |
|[resnet50d.ra2_in1k](https://huggingface.co/timm/resnet50d.ra2_in1k)|224 |80.53|95.16|25.6 |4.4 |11.9 |3164 |
|[resnext50_32x4d.a1_in1k](https://huggingface.co/timm/resnext50_32x4d.a1_in1k)|224 |80.53|94.46|25.0 |4.3 |14.4 |2930 |
|[wide_resnet101_2.tv2_in1k](https://huggingface.co/timm/wide_resnet101_2.tv2_in1k)|176 |80.48|94.98|126.9 |14.3 |13.2 |1719 |
|[resnet152d.gluon_in1k](https://huggingface.co/timm/resnet152d.gluon_in1k)|224 |80.47|95.2 |60.2 |11.8 |23.4 |1428 |
|[resnet50.b2k_in1k](https://huggingface.co/timm/resnet50.b2k_in1k)|288 |80.45|95.32|25.6 |6.8 |18.4 |2086 |
|[ecaresnetlight.miil_in1k](https://huggingface.co/timm/ecaresnetlight.miil_in1k)|224 |80.45|95.24|30.2 |4.1 |8.4 |3530 |
|[resnext50_32x4d.a2_in1k](https://huggingface.co/timm/resnext50_32x4d.a2_in1k)|224 |80.45|94.63|25.0 |4.3 |14.4 |2936 |
|[wide_resnet50_2.tv2_in1k](https://huggingface.co/timm/wide_resnet50_2.tv2_in1k)|176 |80.43|95.09|68.9 |7.3 |9.0 |3015 |
|[resnet101d.gluon_in1k](https://huggingface.co/timm/resnet101d.gluon_in1k)|224 |80.42|95.01|44.6 |8.1 |17.0 |2007 |
|[resnet50.a1_in1k](https://huggingface.co/timm/resnet50.a1_in1k)|224 |80.38|94.6 |25.6 |4.1 |11.1 |3461 |
|[seresnet33ts.ra2_in1k](https://huggingface.co/timm/seresnet33ts.ra2_in1k)|256 |80.36|95.1 |19.8 |4.8 |11.7 |3267 |
|[resnext101_32x4d.gluon_in1k](https://huggingface.co/timm/resnext101_32x4d.gluon_in1k)|224 |80.34|94.93|44.2 |8.0 |21.2 |1814 |
|[resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k)|224 |80.32|95.4 |25.0 |4.3 |14.4 |2941 |
|[resnet101s.gluon_in1k](https://huggingface.co/timm/resnet101s.gluon_in1k)|224 |80.28|95.16|44.7 |9.2 |18.6 |1851 |
|[seresnet50.ra2_in1k](https://huggingface.co/timm/seresnet50.ra2_in1k)|224 |80.26|95.08|28.1 |4.1 |11.1 |2972 |
|[resnetblur50.bt_in1k](https://huggingface.co/timm/resnetblur50.bt_in1k)|288 |80.24|95.24|25.6 |8.5 |19.9 |1523 |
|[resnet50d.a2_in1k](https://huggingface.co/timm/resnet50d.a2_in1k)|224 |80.22|94.63|25.6 |4.4 |11.9 |3162 |
|[resnet152.tv2_in1k](https://huggingface.co/timm/resnet152.tv2_in1k)|176 |80.2 |94.64|60.2 |7.2 |14.0 |2346 |
|[seresnet50.a2_in1k](https://huggingface.co/timm/seresnet50.a2_in1k)|224 |80.08|94.74|28.1 |4.1 |11.1 |2969 |
|[eca_resnet33ts.ra2_in1k](https://huggingface.co/timm/eca_resnet33ts.ra2_in1k)|256 |80.08|94.97|19.7 |4.8 |11.7 |3284 |
|[gcresnet33ts.ra2_in1k](https://huggingface.co/timm/gcresnet33ts.ra2_in1k)|256 |80.06|94.99|19.9 |4.8 |11.7 |3216 |
|[resnet50_gn.a1h_in1k](https://huggingface.co/timm/resnet50_gn.a1h_in1k)|224 |80.06|94.95|25.6 |4.1 |11.1 |1109 |
|[seresnet50.a1_in1k](https://huggingface.co/timm/seresnet50.a1_in1k)|224 |80.02|94.71|28.1 |4.1 |11.1 |2962 |
|[resnet50.ram_in1k](https://huggingface.co/timm/resnet50.ram_in1k)|288 |79.97|95.05|25.6 |6.8 |18.4 |2086 |
|[resnet152c.gluon_in1k](https://huggingface.co/timm/resnet152c.gluon_in1k)|224 |79.92|94.84|60.2 |11.8 |23.4 |1455 |
|[seresnext50_32x4d.gluon_in1k](https://huggingface.co/timm/seresnext50_32x4d.gluon_in1k)|224 |79.91|94.82|27.6 |4.3 |14.4 |2591 |
|[resnet50.d_in1k](https://huggingface.co/timm/resnet50.d_in1k)|224 |79.91|94.67|25.6 |4.1 |11.1 |3456 |
|[resnet101.tv2_in1k](https://huggingface.co/timm/resnet101.tv2_in1k)|176 |79.9 |94.6 |44.6 |4.9 |10.1 |3341 |
|[resnetrs50.tf_in1k](https://huggingface.co/timm/resnetrs50.tf_in1k)|224 |79.89|94.97|35.7 |4.5 |12.1 |2774 |
|[resnet50.c2_in1k](https://huggingface.co/timm/resnet50.c2_in1k)|224 |79.88|94.87|25.6 |4.1 |11.1 |3455 |
|[ecaresnet26t.ra2_in1k](https://huggingface.co/timm/ecaresnet26t.ra2_in1k)|320 |79.86|95.07|16.0 |5.2 |16.4 |2168 |
|[resnet50.a2_in1k](https://huggingface.co/timm/resnet50.a2_in1k)|224 |79.85|94.56|25.6 |4.1 |11.1 |3460 |
|[resnet50.ra_in1k](https://huggingface.co/timm/resnet50.ra_in1k)|288 |79.83|94.97|25.6 |6.8 |18.4 |2087 |
|[resnet101.a3_in1k](https://huggingface.co/timm/resnet101.a3_in1k)|224 |79.82|94.62|44.6 |7.8 |16.2 |2114 |
|[resnext50_32x4d.ra_in1k](https://huggingface.co/timm/resnext50_32x4d.ra_in1k)|224 |79.76|94.6 |25.0 |4.3 |14.4 |2943 |
|[resnet50.c1_in1k](https://huggingface.co/timm/resnet50.c1_in1k)|224 |79.74|94.95|25.6 |4.1 |11.1 |3455 |
|[ecaresnet50d_pruned.miil_in1k](https://huggingface.co/timm/ecaresnet50d_pruned.miil_in1k)|224 |79.74|94.87|19.9 |2.5 |6.4 |3929 |
|[resnet33ts.ra2_in1k](https://huggingface.co/timm/resnet33ts.ra2_in1k)|288 |79.71|94.83|19.7 |6.0 |14.8 |2710 |
|[resnet152.gluon_in1k](https://huggingface.co/timm/resnet152.gluon_in1k)|224 |79.68|94.74|60.2 |11.6 |22.6 |1486 |
|[resnext50d_32x4d.bt_in1k](https://huggingface.co/timm/resnext50d_32x4d.bt_in1k)|224 |79.67|94.87|25.0 |4.5 |15.2 |2729 |
|[resnet50.bt_in1k](https://huggingface.co/timm/resnet50.bt_in1k)|288 |79.63|94.91|25.6 |6.8 |18.4 |2086 |
|[ecaresnet50t.a3_in1k](https://huggingface.co/timm/ecaresnet50t.a3_in1k)|224 |79.56|94.72|25.6 |4.3 |11.8 |2805 |
|[resnet101c.gluon_in1k](https://huggingface.co/timm/resnet101c.gluon_in1k)|224 |79.53|94.58|44.6 |8.1 |17.0 |2062 |
|[resnet50.b1k_in1k](https://huggingface.co/timm/resnet50.b1k_in1k)|224 |79.52|94.61|25.6 |4.1 |11.1 |3459 |
|[resnet50.tv2_in1k](https://huggingface.co/timm/resnet50.tv2_in1k)|176 |79.42|94.64|25.6 |2.6 |6.9 |5397 |
|[resnet32ts.ra2_in1k](https://huggingface.co/timm/resnet32ts.ra2_in1k)|288 |79.4 |94.66|18.0 |5.9 |14.6 |2752 |
|[resnet50.b2k_in1k](https://huggingface.co/timm/resnet50.b2k_in1k)|224 |79.38|94.57|25.6 |4.1 |11.1 |3459 |
|[resnext50_32x4d.tv2_in1k](https://huggingface.co/timm/resnext50_32x4d.tv2_in1k)|176 |79.37|94.3 |25.0 |2.7 |9.0 |4577 |
|[resnext50_32x4d.gluon_in1k](https://huggingface.co/timm/resnext50_32x4d.gluon_in1k)|224 |79.36|94.43|25.0 |4.3 |14.4 |2942 |
|[resnext101_32x8d.tv_in1k](https://huggingface.co/timm/resnext101_32x8d.tv_in1k)|224 |79.31|94.52|88.8 |16.5 |31.2 |1100 |
|[resnet101.gluon_in1k](https://huggingface.co/timm/resnet101.gluon_in1k)|224 |79.31|94.53|44.6 |7.8 |16.2 |2125 |
|[resnetblur50.bt_in1k](https://huggingface.co/timm/resnetblur50.bt_in1k)|224 |79.31|94.63|25.6 |5.2 |12.0 |2524 |
|[resnet50.a1h_in1k](https://huggingface.co/timm/resnet50.a1h_in1k)|176 |79.27|94.49|25.6 |2.6 |6.9 |5404 |
|[resnext50_32x4d.a3_in1k](https://huggingface.co/timm/resnext50_32x4d.a3_in1k)|224 |79.25|94.31|25.0 |4.3 |14.4 |2931 |
|[resnet50.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnet50.fb_ssl_yfcc100m_ft_in1k)|224 |79.22|94.84|25.6 |4.1 |11.1 |3451 |
|[resnet33ts.ra2_in1k](https://huggingface.co/timm/resnet33ts.ra2_in1k)|256 |79.21|94.56|19.7 |4.8 |11.7 |3392 |
|[resnet50d.gluon_in1k](https://huggingface.co/timm/resnet50d.gluon_in1k)|224 |79.07|94.48|25.6 |4.4 |11.9 |3162 |
|[resnet50.ram_in1k](https://huggingface.co/timm/resnet50.ram_in1k)|224 |79.03|94.38|25.6 |4.1 |11.1 |3453 |
|[resnet50.am_in1k](https://huggingface.co/timm/resnet50.am_in1k)|224 |79.01|94.39|25.6 |4.1 |11.1 |3461 |
|[resnet32ts.ra2_in1k](https://huggingface.co/timm/resnet32ts.ra2_in1k)|256 |79.01|94.37|18.0 |4.6 |11.6 |3440 |
|[ecaresnet26t.ra2_in1k](https://huggingface.co/timm/ecaresnet26t.ra2_in1k)|256 |78.9 |94.54|16.0 |3.4 |10.5 |3421 |
|[resnet152.a3_in1k](https://huggingface.co/timm/resnet152.a3_in1k)|160 |78.89|94.11|60.2 |5.9 |11.5 |2745 |
|[wide_resnet101_2.tv_in1k](https://huggingface.co/timm/wide_resnet101_2.tv_in1k)|224 |78.84|94.28|126.9 |22.8 |21.2 |1079 |
|[seresnext26d_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26d_32x4d.bt_in1k)|288 |78.83|94.24|16.8 |4.5 |16.8 |2251 |
|[resnet50.ra_in1k](https://huggingface.co/timm/resnet50.ra_in1k)|224 |78.81|94.32|25.6 |4.1 |11.1 |3454 |
|[seresnext26t_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26t_32x4d.bt_in1k)|288 |78.74|94.33|16.8 |4.5 |16.7 |2264 |
|[resnet50s.gluon_in1k](https://huggingface.co/timm/resnet50s.gluon_in1k)|224 |78.72|94.23|25.7 |5.5 |13.5 |2796 |
|[resnet50d.a3_in1k](https://huggingface.co/timm/resnet50d.a3_in1k)|224 |78.71|94.24|25.6 |4.4 |11.9 |3154 |
|[wide_resnet50_2.tv_in1k](https://huggingface.co/timm/wide_resnet50_2.tv_in1k)|224 |78.47|94.09|68.9 |11.4 |14.4 |1934 |
|[resnet50.bt_in1k](https://huggingface.co/timm/resnet50.bt_in1k)|224 |78.46|94.27|25.6 |4.1 |11.1 |3454 |
|[resnet34d.ra2_in1k](https://huggingface.co/timm/resnet34d.ra2_in1k)|288 |78.43|94.35|21.8 |6.5 |7.5 |3291 |
|[gcresnext26ts.ch_in1k](https://huggingface.co/timm/gcresnext26ts.ch_in1k)|288 |78.42|94.04|10.5 |3.1 |13.3 |3226 |
|[resnet26t.ra2_in1k](https://huggingface.co/timm/resnet26t.ra2_in1k)|320 |78.33|94.13|16.0 |5.2 |16.4 |2391 |
|[resnet152.tv_in1k](https://huggingface.co/timm/resnet152.tv_in1k)|224 |78.32|94.04|60.2 |11.6 |22.6 |1487 |
|[seresnext26ts.ch_in1k](https://huggingface.co/timm/seresnext26ts.ch_in1k)|288 |78.28|94.1 |10.4 |3.1 |13.3 |3062 |
|[bat_resnext26ts.ch_in1k](https://huggingface.co/timm/bat_resnext26ts.ch_in1k)|256 |78.25|94.1 |10.7 |2.5 |12.5 |3393 |
|[resnet50.a3_in1k](https://huggingface.co/timm/resnet50.a3_in1k)|224 |78.06|93.78|25.6 |4.1 |11.1 |3450 |
|[resnet50c.gluon_in1k](https://huggingface.co/timm/resnet50c.gluon_in1k)|224 |78.0 |93.99|25.6 |4.4 |11.9 |3286 |
|[eca_resnext26ts.ch_in1k](https://huggingface.co/timm/eca_resnext26ts.ch_in1k)|288 |78.0 |93.91|10.3 |3.1 |13.3 |3297 |
|[seresnext26t_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26t_32x4d.bt_in1k)|224 |77.98|93.75|16.8 |2.7 |10.1 |3841 |
|[resnet34.a1_in1k](https://huggingface.co/timm/resnet34.a1_in1k)|288 |77.92|93.77|21.8 |6.1 |6.2 |3609 |
|[resnet101.a3_in1k](https://huggingface.co/timm/resnet101.a3_in1k)|160 |77.88|93.71|44.6 |4.0 |8.3 |3926 |
|[resnet26t.ra2_in1k](https://huggingface.co/timm/resnet26t.ra2_in1k)|256 |77.87|93.84|16.0 |3.4 |10.5 |3772 |
|[seresnext26ts.ch_in1k](https://huggingface.co/timm/seresnext26ts.ch_in1k)|256 |77.86|93.79|10.4 |2.4 |10.5 |4263 |
|[resnetrs50.tf_in1k](https://huggingface.co/timm/resnetrs50.tf_in1k)|160 |77.82|93.81|35.7 |2.3 |6.2 |5238 |
|[gcresnext26ts.ch_in1k](https://huggingface.co/timm/gcresnext26ts.ch_in1k)|256 |77.81|93.82|10.5 |2.4 |10.5 |4183 |
|[ecaresnet50t.a3_in1k](https://huggingface.co/timm/ecaresnet50t.a3_in1k)|160 |77.79|93.6 |25.6 |2.2 |6.0 |5329 |
|[resnext50_32x4d.a3_in1k](https://huggingface.co/timm/resnext50_32x4d.a3_in1k)|160 |77.73|93.32|25.0 |2.2 |7.4 |5576 |
|[resnext50_32x4d.tv_in1k](https://huggingface.co/timm/resnext50_32x4d.tv_in1k)|224 |77.61|93.7 |25.0 |4.3 |14.4 |2944 |
|[seresnext26d_32x4d.bt_in1k](https://huggingface.co/timm/seresnext26d_32x4d.bt_in1k)|224 |77.59|93.61|16.8 |2.7 |10.2 |3807 |
|[resnet50.gluon_in1k](https://huggingface.co/timm/resnet50.gluon_in1k)|224 |77.58|93.72|25.6 |4.1 |11.1 |3455 |
|[eca_resnext26ts.ch_in1k](https://huggingface.co/timm/eca_resnext26ts.ch_in1k)|256 |77.44|93.56|10.3 |2.4 |10.5 |4284 |
|[resnet26d.bt_in1k](https://huggingface.co/timm/resnet26d.bt_in1k)|288 |77.41|93.63|16.0 |4.3 |13.5 |2907 |
|[resnet101.tv_in1k](https://huggingface.co/timm/resnet101.tv_in1k)|224 |77.38|93.54|44.6 |7.8 |16.2 |2125 |
|[resnet50d.a3_in1k](https://huggingface.co/timm/resnet50d.a3_in1k)|160 |77.22|93.27|25.6 |2.2 |6.1 |5982 |
|[resnext26ts.ra2_in1k](https://huggingface.co/timm/resnext26ts.ra2_in1k)|288 |77.17|93.47|10.3 |3.1 |13.3 |3392 |
|[resnet34.a2_in1k](https://huggingface.co/timm/resnet34.a2_in1k)|288 |77.15|93.27|21.8 |6.1 |6.2 |3615 |
|[resnet34d.ra2_in1k](https://huggingface.co/timm/resnet34d.ra2_in1k)|224 |77.1 |93.37|21.8 |3.9 |4.5 |5436 |
|[seresnet50.a3_in1k](https://huggingface.co/timm/seresnet50.a3_in1k)|224 |77.02|93.07|28.1 |4.1 |11.1 |2952 |
|[resnext26ts.ra2_in1k](https://huggingface.co/timm/resnext26ts.ra2_in1k)|256 |76.78|93.13|10.3 |2.4 |10.5 |4410 |
|[resnet26d.bt_in1k](https://huggingface.co/timm/resnet26d.bt_in1k)|224 |76.7 |93.17|16.0 |2.6 |8.2 |4859 |
|[resnet34.bt_in1k](https://huggingface.co/timm/resnet34.bt_in1k)|288 |76.5 |93.35|21.8 |6.1 |6.2 |3617 |
|[resnet34.a1_in1k](https://huggingface.co/timm/resnet34.a1_in1k)|224 |76.42|92.87|21.8 |3.7 |3.7 |5984 |
|[resnet26.bt_in1k](https://huggingface.co/timm/resnet26.bt_in1k)|288 |76.35|93.18|16.0 |3.9 |12.2 |3331 |
|[resnet50.tv_in1k](https://huggingface.co/timm/resnet50.tv_in1k)|224 |76.13|92.86|25.6 |4.1 |11.1 |3457 |
|[resnet50.a3_in1k](https://huggingface.co/timm/resnet50.a3_in1k)|160 |75.96|92.5 |25.6 |2.1 |5.7 |6490 |
|[resnet34.a2_in1k](https://huggingface.co/timm/resnet34.a2_in1k)|224 |75.52|92.44|21.8 |3.7 |3.7 |5991 |
|[resnet26.bt_in1k](https://huggingface.co/timm/resnet26.bt_in1k)|224 |75.3 |92.58|16.0 |2.4 |7.4 |5583 |
|[resnet34.bt_in1k](https://huggingface.co/timm/resnet34.bt_in1k)|224 |75.16|92.18|21.8 |3.7 |3.7 |5994 |
|[seresnet50.a3_in1k](https://huggingface.co/timm/seresnet50.a3_in1k)|160 |75.1 |92.08|28.1 |2.1 |5.7 |5513 |
|[resnet34.gluon_in1k](https://huggingface.co/timm/resnet34.gluon_in1k)|224 |74.57|91.98|21.8 |3.7 |3.7 |5984 |
|[resnet18d.ra2_in1k](https://huggingface.co/timm/resnet18d.ra2_in1k)|288 |73.81|91.83|11.7 |3.4 |5.4 |5196 |
|[resnet34.tv_in1k](https://huggingface.co/timm/resnet34.tv_in1k)|224 |73.32|91.42|21.8 |3.7 |3.7 |5979 |
|[resnet18.fb_swsl_ig1b_ft_in1k](https://huggingface.co/timm/resnet18.fb_swsl_ig1b_ft_in1k)|224 |73.28|91.73|11.7 |1.8 |2.5 |10213 |
|[resnet18.a1_in1k](https://huggingface.co/timm/resnet18.a1_in1k)|288 |73.16|91.03|11.7 |3.0 |4.1 |6050 |
|[resnet34.a3_in1k](https://huggingface.co/timm/resnet34.a3_in1k)|224 |72.98|91.11|21.8 |3.7 |3.7 |5967 |
|[resnet18.fb_ssl_yfcc100m_ft_in1k](https://huggingface.co/timm/resnet18.fb_ssl_yfcc100m_ft_in1k)|224 |72.6 |91.42|11.7 |1.8 |2.5 |10213 |
|[resnet18.a2_in1k](https://huggingface.co/timm/resnet18.a2_in1k)|288 |72.37|90.59|11.7 |3.0 |4.1 |6051 |
|[resnet14t.c3_in1k](https://huggingface.co/timm/resnet14t.c3_in1k)|224 |72.26|90.31|10.1 |1.7 |5.8 |7026 |
|[resnet18d.ra2_in1k](https://huggingface.co/timm/resnet18d.ra2_in1k)|224 |72.26|90.68|11.7 |2.1 |3.3 |8707 |
|[resnet18.a1_in1k](https://huggingface.co/timm/resnet18.a1_in1k)|224 |71.49|90.07|11.7 |1.8 |2.5 |10187 |
|[resnet14t.c3_in1k](https://huggingface.co/timm/resnet14t.c3_in1k)|176 |71.31|89.69|10.1 |1.1 |3.6 |10970 |
|[resnet18.gluon_in1k](https://huggingface.co/timm/resnet18.gluon_in1k)|224 |70.84|89.76|11.7 |1.8 |2.5 |10210 |
|[resnet18.a2_in1k](https://huggingface.co/timm/resnet18.a2_in1k)|224 |70.64|89.47|11.7 |1.8 |2.5 |10194 |
|[resnet34.a3_in1k](https://huggingface.co/timm/resnet34.a3_in1k)|160 |70.56|89.52|21.8 |1.9 |1.9 |10737 |
|[resnet18.tv_in1k](https://huggingface.co/timm/resnet18.tv_in1k)|224 |69.76|89.07|11.7 |1.8 |2.5 |10205 |
|[resnet10t.c3_in1k](https://huggingface.co/timm/resnet10t.c3_in1k)|224 |68.34|88.03|5.4 |1.1 |2.4 |13079 |
|[resnet18.a3_in1k](https://huggingface.co/timm/resnet18.a3_in1k)|224 |68.25|88.17|11.7 |1.8 |2.5 |10167 |
|[resnet10t.c3_in1k](https://huggingface.co/timm/resnet10t.c3_in1k)|176 |66.71|86.96|5.4 |0.7 |1.5 |20327 |
|[resnet18.a3_in1k](https://huggingface.co/timm/resnet18.a3_in1k)|160 |65.66|86.26|11.7 |0.9 |1.3 |18229 |
## Citation
```bibtex
@article{He2015,
author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
title = {Deep Residual Learning for Image Recognition},
journal = {arXiv preprint arXiv:1512.03385},
year = {2015}
}
```
```bibtex
@InProceedings{wang2020eca,
title={ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks},
author={Qilong Wang, Banggu Wu, Pengfei Zhu, Peihua Li, Wangmeng Zuo and Qinghua Hu},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2020}
}
```
```bibtex
@article{He2018BagOT,
title={Bag of Tricks for Image Classification with Convolutional Neural Networks},
author={Tong He and Zhi Zhang and Hang Zhang and Zhongyue Zhang and Junyuan Xie and Mu Li},
journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2018},
pages={558-567}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
microsoft/focalnet-tiny
|
microsoft
| 2023-05-03T16:16:36Z | 359 | 1 |
transformers
|
[
"transformers",
"pytorch",
"focalnet",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2203.11926",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-04-17T14:53:08Z |
---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# FocalNet (tiny-sized model)
FocalNet model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Focal Modulation Networks
](https://arxiv.org/abs/2203.11926) by Yang et al. and first released in [this repository](https://github.com/microsoft/FocalNet).
Disclaimer: The team releasing FocalNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
Focul Modulation Networks are an alternative to Vision Transformers, where self-attention (SA) is completely replaced by a focal modulation mechanism for modeling token interactions in vision.
Focal modulation comprises three components: (i) hierarchical contextualization, implemented using a stack of depth-wise convolutional layers, to encode visual contexts from short to long ranges, (ii) gated aggregation to selectively gather contexts for each query token based on its
content, and (iii) element-wise modulation or affine transformation to inject the aggregated context into the query. Extensive experiments show FocalNets outperform the state-of-the-art SA counterparts (e.g., Vision Transformers, Swin and Focal Transformers) with similar computational costs on the tasks of image classification, object detection, and segmentation.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=focalnet) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import FocalNetImageProcessor, FocalNetForImageClassification
import torch
from datasets import load_dataset
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
preprocessor = FocalNetImageProcessor.from_pretrained("microsoft/focalnet-tiny")
model = FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny")
inputs = preprocessor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/focalnet).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2203-11926,
author = {Jianwei Yang and
Chunyuan Li and
Jianfeng Gao},
title = {Focal Modulation Networks},
journal = {CoRR},
volume = {abs/2203.11926},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2203.11926},
doi = {10.48550/arXiv.2203.11926},
eprinttype = {arXiv},
eprint = {2203.11926},
timestamp = {Tue, 29 Mar 2022 18:07:24 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2203-11926.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
GT4SD/multitask-text-and-chemistry-t5-small-augm
|
GT4SD
| 2023-05-15T17:48:11Z | 359 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"arxiv:2301.12586",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2023-05-12T21:52:31Z |
---
license: mit
language:
- en
---
# Multitask Text and Chemistry T5
Multitask Text and Chemistry T5 : a multi-domain, multi-task language model to solve a wide range of tasks in both the chemical and natural language domains. Published by [Christofidellis et al.](https://arxiv.org/pdf/2301.12586.pdf)
**Model Details**: The Multitask Text and Chemistry T5 variant trained using <em>t5-small</em> as its pretrained based and the <em>augmented dataset</em>.
**Developers**: Dimitrios Christofidellis*, Giorgio Giannone*, Jannis Born, Teodoro Laino and Matteo Manica from IBM Research and Ole Winther from Technical University of Denmark.
**Distributors**: Model natively integrated into GT4SD.
**Model date**: 2023.
**Model type**: A Transformer-based language model that is trained on a multi-domain and a multi-task dataset by aggregating available datasets
for the tasks of Forward reaction prediction, Retrosynthesis, Molecular captioning, Text-conditional de novo generation and Paragraph to actions.
**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**:
N.A.
**Paper or other resource for more information**:
The Multitask Text and Chemistry T5 [Christofidellis et al.](https://arxiv.org/pdf/2301.12586.pdf)
**License**: MIT
**Where to send questions or comments about the model**: Open an issue on [GT4SD repository](https://github.com/GT4SD/gt4sd-core).
## Citation
```bib
@article{christofidellis2023unifying,
title={Unifying Molecular and Textual Representations via Multi-task Language Modelling},
author={Christofidellis, Dimitrios and Giannone, Giorgio and Born, Jannis and Winther, Ole and Laino, Teodoro and Manica, Matteo},
journal={arXiv preprint arXiv:2301.12586},
year={2023}
}
```
*equal contribution
|
llm-book/bert-base-japanese-v3-jsts
|
llm-book
| 2023-07-29T11:27:18Z | 359 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"ja",
"dataset:llm-book/JGLUE",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-11T15:27:32Z |
---
language:
- ja
license: apache-2.0
library_name: transformers
datasets:
- llm-book/JGLUE
---
# bert-base-japanese-v3-jsts
「[大規模言語モデル入門](https://www.amazon.co.jp/dp/4297136333)」の第5章で紹介している(意味類似度計算)のモデルです。
[cl-tohoku/bert-base-japanese-v3](https://huggingface.co/cl-tohoku/bert-base-japanese-v3)を[JGLUE](https://huggingface.co/datasets/llm-book/JGLUE)のJSTSデータセットでファインチューニングして構築されています。
## 関連リンク
* [GitHubリポジトリ](https://github.com/ghmagazine/llm-book)
* [Colabノートブック（訓練）](https://colab.research.google.com/github/ghmagazine/llm-book/blob/main/chapter5/5-4-sts-finetuning.ipynb)
* [Colabノートブック（推論）](https://colab.research.google.com/github/ghmagazine/llm-book/blob/main/chapter5/5-4-sts-analysis.ipynb)
* [データセット](https://huggingface.co/datasets/llm-book/JGLUE)
* [大規模言語モデル入門（Amazon.co.jp）](https://www.amazon.co.jp/dp/4297136333/)
* [大規模言語モデル入門（gihyo.jp）](https://gihyo.jp/book/2023/978-4-297-13633-8)
## 使い方
```python
from transformers import pipeline
text_sim_pipeline = pipeline(
model="llm-book/bert-base-japanese-v3-jsts",
function_to_apply="none",
)
text = "川べりでサーフボードを持った人たちがいます"
sim_text = "サーファーたちが川べりに立っています"
# textとsim_textの類似度を計算
result = text_sim_pipeline({"text": text, "text_pair": sim_text})
print(result["score"])
# 3.5703558921813965
```
## ライセンス
[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0)
|
lorahub/flan_t5_large-wmt16_translate_ro-en
|
lorahub
| 2023-07-24T09:51:08Z | 359 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-24T09:50:59Z |
---
library_name: peft
---
|
lorahub/flan_t5_large-anli_r1
|
lorahub
| 2023-07-24T09:55:18Z | 359 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-24T09:55:08Z |
---
library_name: peft
---
|
ifaouibadi/Llama-2-7b-Verse-Chat
|
ifaouibadi
| 2023-10-09T11:32:41Z | 359 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"facebook",
"meta",
"pytorch",
"llama-2",
"text-generation",
"en",
"arxiv:2307.09288",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"license:llama2",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-10-09T11:32:41Z |
---
language:
- en
license: llama2
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
model_name: Llama 2 7B Chat
arxiv: 2307.09288
base_model: meta-llama/Llama-2-7b-chat-hf
inference: false
model_creator: Meta Llama 2
model_type: llama
pipeline_tag: text-generation
prompt_template: '[INST] <<SYS>>
You are a helpful, respectful and honest assistant. Always answer as helpfully as
possible, while being safe. Your answers should not include any harmful, unethical,
racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses
are socially unbiased and positive in nature. If a question does not make any sense,
or is not factually coherent, explain why instead of answering something not correct.
If you don''t know the answer to a question, please don''t share false information.
<</SYS>>
{prompt}[/INST]
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
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<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
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</div>
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</div>
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Llama 2 7B Chat - GGUF
- Model creator: [Meta Llama 2](https://huggingface.co/meta-llama)
- Original model: [Llama 2 7B Chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Meta Llama 2's Llama 2 7B Chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Llama-2-7b-Chat-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF)
* [Meta Llama 2's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Llama-2-Chat
```
[INST] <<SYS>>
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
<</SYS>>
{prompt}[/INST]
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [llama-2-7b-chat.Q2_K.gguf](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/blob/main/llama-2-7b-chat.Q2_K.gguf) | Q2_K | 2 | 2.83 GB| 5.33 GB | smallest, significant quality loss - not recommended for most purposes |
| [llama-2-7b-chat.Q3_K_S.gguf](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/blob/main/llama-2-7b-chat.Q3_K_S.gguf) | Q3_K_S | 3 | 2.95 GB| 5.45 GB | very small, high quality loss |
| [llama-2-7b-chat.Q3_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/blob/main/llama-2-7b-chat.Q3_K_M.gguf) | Q3_K_M | 3 | 3.30 GB| 5.80 GB | very small, high quality loss |
| [llama-2-7b-chat.Q3_K_L.gguf](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/blob/main/llama-2-7b-chat.Q3_K_L.gguf) | Q3_K_L | 3 | 3.60 GB| 6.10 GB | small, substantial quality loss |
| [llama-2-7b-chat.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/blob/main/llama-2-7b-chat.Q4_0.gguf) | Q4_0 | 4 | 3.83 GB| 6.33 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [llama-2-7b-chat.Q4_K_S.gguf](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/blob/main/llama-2-7b-chat.Q4_K_S.gguf) | Q4_K_S | 4 | 3.86 GB| 6.36 GB | small, greater quality loss |
| [llama-2-7b-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/blob/main/llama-2-7b-chat.Q4_K_M.gguf) | Q4_K_M | 4 | 4.08 GB| 6.58 GB | medium, balanced quality - recommended |
| [llama-2-7b-chat.Q5_0.gguf](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/blob/main/llama-2-7b-chat.Q5_0.gguf) | Q5_0 | 5 | 4.65 GB| 7.15 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [llama-2-7b-chat.Q5_K_S.gguf](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/blob/main/llama-2-7b-chat.Q5_K_S.gguf) | Q5_K_S | 5 | 4.65 GB| 7.15 GB | large, low quality loss - recommended |
| [llama-2-7b-chat.Q5_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/blob/main/llama-2-7b-chat.Q5_K_M.gguf) | Q5_K_M | 5 | 4.78 GB| 7.28 GB | large, very low quality loss - recommended |
| [llama-2-7b-chat.Q6_K.gguf](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/blob/main/llama-2-7b-chat.Q6_K.gguf) | Q6_K | 6 | 5.53 GB| 8.03 GB | very large, extremely low quality loss |
| [llama-2-7b-chat.Q8_0.gguf](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/blob/main/llama-2-7b-chat.Q8_0.gguf) | Q8_0 | 8 | 7.16 GB| 9.66 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Llama-2-7b-Chat-GGUF and below it, a specific filename to download, such as: llama-2-7b-chat.q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub>=0.17.1
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Llama-2-7b-Chat-GGUF llama-2-7b-chat.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Llama-2-7b-Chat-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Llama-2-7b-Chat-GGUF llama-2-7b-chat.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m llama-2-7b-chat.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n{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 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model from Python using ctransformers
#### First install the package
```bash
# Base ctransformers with no GPU acceleration
pip install ctransformers>=0.2.24
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]>=0.2.24
# Or with ROCm GPU acceleration
CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems
CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
```
#### Simple example code to load one of these GGUF models
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-7b-Chat-GGUF", model_file="llama-2-7b-chat.q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here's guides on using llama-cpp-python or ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Meta Llama 2's Llama 2 7B Chat
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software “bug,” or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
<!-- original-model-card end -->
|
TheBloke/PiVoT-0.1-Starling-LM-RP-GGUF
|
TheBloke
| 2023-11-28T19:17:50Z | 359 | 13 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"text-generation",
"en",
"base_model:maywell/PiVoT-0.1-Starling-LM-RP",
"license:cc-by-nc-4.0",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-11-28T15:25:05Z |
---
base_model: maywell/PiVoT-0.1-Starling-LM-RP
inference: false
language:
- en
license: cc-by-nc-4.0
model_creator: Jeonghwan Park
model_name: Pivot 0.1 Starling LM RP
model_type: mistral
pipeline_tag: text-generation
prompt_template: 'GPT4 User: {prompt}<|end_of_turn|>GPT4 Assistant:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
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<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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</div>
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Pivot 0.1 Starling LM RP - GGUF
- Model creator: [Jeonghwan Park](https://huggingface.co/maywell)
- Original model: [Pivot 0.1 Starling LM RP](https://huggingface.co/maywell/PiVoT-0.1-Starling-LM-RP)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Jeonghwan Park's Pivot 0.1 Starling LM RP](https://huggingface.co/maywell/PiVoT-0.1-Starling-LM-RP).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/PiVoT-0.1-Starling-LM-RP-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/PiVoT-0.1-Starling-LM-RP-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/PiVoT-0.1-Starling-LM-RP-GGUF)
* [Jeonghwan Park's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/maywell/PiVoT-0.1-Starling-LM-RP)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: OpenChat
```
GPT4 User: {prompt}<|end_of_turn|>GPT4 Assistant:
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [pivot-0.1-starling-lm-rp.Q2_K.gguf](https://huggingface.co/TheBloke/PiVoT-0.1-Starling-LM-RP-GGUF/blob/main/pivot-0.1-starling-lm-rp.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [pivot-0.1-starling-lm-rp.Q3_K_S.gguf](https://huggingface.co/TheBloke/PiVoT-0.1-Starling-LM-RP-GGUF/blob/main/pivot-0.1-starling-lm-rp.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [pivot-0.1-starling-lm-rp.Q3_K_M.gguf](https://huggingface.co/TheBloke/PiVoT-0.1-Starling-LM-RP-GGUF/blob/main/pivot-0.1-starling-lm-rp.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [pivot-0.1-starling-lm-rp.Q3_K_L.gguf](https://huggingface.co/TheBloke/PiVoT-0.1-Starling-LM-RP-GGUF/blob/main/pivot-0.1-starling-lm-rp.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [pivot-0.1-starling-lm-rp.Q4_0.gguf](https://huggingface.co/TheBloke/PiVoT-0.1-Starling-LM-RP-GGUF/blob/main/pivot-0.1-starling-lm-rp.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [pivot-0.1-starling-lm-rp.Q4_K_S.gguf](https://huggingface.co/TheBloke/PiVoT-0.1-Starling-LM-RP-GGUF/blob/main/pivot-0.1-starling-lm-rp.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [pivot-0.1-starling-lm-rp.Q4_K_M.gguf](https://huggingface.co/TheBloke/PiVoT-0.1-Starling-LM-RP-GGUF/blob/main/pivot-0.1-starling-lm-rp.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [pivot-0.1-starling-lm-rp.Q5_0.gguf](https://huggingface.co/TheBloke/PiVoT-0.1-Starling-LM-RP-GGUF/blob/main/pivot-0.1-starling-lm-rp.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [pivot-0.1-starling-lm-rp.Q5_K_S.gguf](https://huggingface.co/TheBloke/PiVoT-0.1-Starling-LM-RP-GGUF/blob/main/pivot-0.1-starling-lm-rp.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [pivot-0.1-starling-lm-rp.Q5_K_M.gguf](https://huggingface.co/TheBloke/PiVoT-0.1-Starling-LM-RP-GGUF/blob/main/pivot-0.1-starling-lm-rp.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [pivot-0.1-starling-lm-rp.Q6_K.gguf](https://huggingface.co/TheBloke/PiVoT-0.1-Starling-LM-RP-GGUF/blob/main/pivot-0.1-starling-lm-rp.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [pivot-0.1-starling-lm-rp.Q8_0.gguf](https://huggingface.co/TheBloke/PiVoT-0.1-Starling-LM-RP-GGUF/blob/main/pivot-0.1-starling-lm-rp.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/PiVoT-0.1-Starling-LM-RP-GGUF and below it, a specific filename to download, such as: pivot-0.1-starling-lm-rp.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/PiVoT-0.1-Starling-LM-RP-GGUF pivot-0.1-starling-lm-rp.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/PiVoT-0.1-Starling-LM-RP-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/PiVoT-0.1-Starling-LM-RP-GGUF pivot-0.1-starling-lm-rp.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m pivot-0.1-starling-lm-rp.Q4_K_M.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "GPT4 User: {prompt}<|end_of_turn|>GPT4 Assistant:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 8192` 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)
## How to run 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%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. 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="./pivot-0.1-starling-lm-rp.Q4_K_M.gguf", # Download the model file first
n_ctx=8192, # 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(
"GPT4 User: {prompt}<|end_of_turn|>GPT4 Assistant:", # 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="./pivot-0.1-starling-lm-rp.Q4_K_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."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
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# Original model card: Jeonghwan Park's Pivot 0.1 Starling LM RP
# PiVoT-0.1-Starling-LM-RP
# **Model Details**
### Description
PiVoT-0.1-Starling-LM-RP is RP finetuned model based on Starling-LM-alpha. Using Synatra-RP dataset.
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## Prompt template: OpenChat
```
GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:
```
Follow me on twitter: https://twitter.com/stablefluffy
Consider Support me making these model alone: https://www.buymeacoffee.com/mwell or with Runpod Credit Gift 💕
Contact me on Telegram: https://t.me/AlzarTakkarsen
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