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| title: README | |
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| # Visual Informatics Group @ University of Texas at Austin ([VITA-Group](https://vita-group.github.io/)) | |
| At VITA group, we have unusually broad, and forever-evolving research interests spanning from the theory to the | |
| application aspects of machine learning (ML). Our current "research keywords" include, but are not limited to: | |
| sparsity (from classical optimization to modern neural networks); efficient training, inference or transfer | |
| (especially, of large foundation models); robustness and trustworthiness; learning to optimize (L2O); | |
| generative AI; graph learning, and more. | |
| ## Compressed LLM Model Zone | |
| **NOTE: All compressed LLMs are moved to a new repo at [compressed-llm](https://huggingface.co/compressed-llm).** | |
| The models are prepared by [Visual Informatics Group @ University of Texas at Austin (VITA-group)](https://vita-group.github.io/). Credits to Ajay Jaiswal, Zhenyu Zhang, Zhangheng Li, Lu Yin, Shiwei Liu and Junyuan Hong. | |
| License: [MIT License](https://opensource.org/license/mit/) | |
| Setup environment | |
| ```shell | |
| pip install torch==2.0.0+cu117 torchvision==0.15.1+cu117 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu117 | |
| pip install transformers==4.31.0 | |
| pip install accelerate | |
| pip install auto-gptq # for gptq | |
| ``` | |
| How to use pruned models | |
| ```python | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| base_model = 'llama-2-7b' | |
| comp_method = 'magnitude_unstructured' | |
| comp_degree = 0.2 | |
| model_path = f'vita-group/{base_model}_{comp_method}' | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_path, | |
| revision=f's{comp_degree}', | |
| torch_dtype=torch.float16, | |
| low_cpu_mem_usage=True, | |
| device_map="auto" | |
| ) | |
| tokenizer = AutoTokenizer.from_pretrained('meta-llama/Llama-2-7b-hf') | |
| input_ids = tokenizer('Hello! I am a VITA-compressed-LLM chatbot!', return_tensors='pt').input_ids.cuda() | |
| outputs = model.generate(input_ids, max_new_tokens=128) | |
| print(tokenizer.decode(outputs[0])) | |
| ``` | |
| How to use wanda+gptq models | |
| ```python | |
| from transformers import AutoTokenizer | |
| from auto_gptq import AutoGPTQForCausalLM | |
| model_path = 'vita-group/llama-2-7b_wanda_2_4_gptq_4bit_128g' | |
| tokenizer_path = 'meta-llama/Llama-2-7b-hf' | |
| model = AutoGPTQForCausalLM.from_quantized( | |
| model_path, | |
| # inject_fused_attention=False, # or | |
| disable_exllama=True, | |
| device_map='auto', | |
| ) | |
| tokenizer = AutoTokenizer.from_pretrained(tokenizer_path, trust_remote_code=True) | |
| input_ids = tokenizer('Hello! I am a VITA-compressed-LLM chatbot!', return_tensors='pt').input_ids.to('cuda') | |
| outputs = model.generate(input_ids=input_ids, max_length=128) | |
| tokenizer.decode(outputs[0]) | |
| ``` | |
| How to use gptq models | |
| ```python | |
| from transformers import AutoTokenizer | |
| from auto_gptq import AutoGPTQForCausalLM | |
| # model_path = 'vita-group/llama-2-7b_wanda_2_4_gptq_4bit_128g' | |
| # tokenizer_path = 'meta-llama/Llama-2-7b-hf' | |
| model_path = 'vita-group/vicuna-7b-v1.3_gptq' | |
| tokenizer_path = 'lmsys/vicuna-7b-v1.3' | |
| model = AutoGPTQForCausalLM.from_quantized( | |
| model_path, | |
| # inject_fused_attention=False, # or | |
| disable_exllama=True, | |
| device_map='auto', | |
| revision='2bit_128g', | |
| ) | |
| from transformers import AutoTokenizer | |
| tokenizer = AutoTokenizer.from_pretrained(tokenizer_path, trust_remote_code=True) | |
| input_ids = tokenizer('Hello! I am a VITA-compressed-LLM chatbot!', return_tensors='pt').input_ids.to('cuda') | |
| outputs = model.generate(input_ids=input_ids, max_length=128) | |
| tokenizer.decode(outputs[0]) | |
| ``` | |
| | | Base Model | Model Size | Compression Method | Compression Degree | | |
| |---:|:-------------|:-------------|:------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | |
| | 0 | Llama-2 | 13b | [magnitude_semistruct](https://huggingface.co/vita-group/llama-2-13b_magnitude_semistruct) | [0.5_2to4](https://huggingface.co/vita-group/llama-2-13b_magnitude_semistruct/tree/0.5_2to4) | | |
| | 1 | Llama-2 | 13b | [sparsegpt_semistruct](https://huggingface.co/vita-group/llama-2-13b_sparsegpt_semistruct) | [0.5_2to4](https://huggingface.co/vita-group/llama-2-13b_sparsegpt_semistruct/tree/0.5_2to4) | | |
| | 2 | Llama-2 | 7b | [magnitude_unstructured](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured) | [s0.1](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured/tree/s0.1) | | |
| | 3 | Llama-2 | 7b | [magnitude_unstructured](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured) | [s0.2](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured/tree/s0.2) | | |
| | 4 | Llama-2 | 7b | [magnitude_unstructured](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured) | [s0.3](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured/tree/s0.3) | | |
| | 5 | Llama-2 | 7b | [magnitude_unstructured](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured) | [s0.5](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured/tree/s0.5) | | |
| | 6 | Llama-2 | 7b | [magnitude_unstructured](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured) | [s0.6](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured/tree/s0.6) | | |
| | 7 | Llama-2 | 7b | [sparsegpt_unstructured](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured) | [s0.1](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured/tree/s0.1) | | |
| | 8 | Llama-2 | 7b | [sparsegpt_unstructured](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured) | [s0.2](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured/tree/s0.2) | | |
| | 9 | Llama-2 | 7b | [sparsegpt_unstructured](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured) | [s0.3](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured/tree/s0.3) | | |
| | 10 | Llama-2 | 7b | [sparsegpt_unstructured](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured) | [s0.5](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured/tree/s0.5) | | |
| | 11 | Llama-2 | 7b | [sparsegpt_unstructured](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured) | [s0.6](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured/tree/s0.6) | | |
| | 12 | Llama-2 | 7b | [wanda_gptq](https://huggingface.co/vita-group/llama-2-7b_wanda_2_4_gptq_4bit_128g) | 4bit_128g | | |
| | 13 | Llama-2 | 7b | [wanda_unstructured](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured) | [s0.1](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured/tree/s0.1) | | |
| | 14 | Llama-2 | 7b | [wanda_unstructured](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured) | [s0.2](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured/tree/s0.2) | | |
| | 15 | Llama-2 | 7b | [wanda_unstructured](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured) | [s0.3](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured/tree/s0.3) | | |
| | 16 | Llama-2 | 7b | [wanda_unstructured](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured) | [s0.5](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured/tree/s0.5) | | |
| | 17 | Llama-2 | 7b | [wanda_unstructured](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured) | [s0.6](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured/tree/s0.6) | | |
| | 18 | Llama-2-chat | 13b | [magnitude_semistruct](https://huggingface.co/vita-group/llama-2-13b-chat_magnitude_semistruct) | [0.5_2to4](https://huggingface.co/vita-group/llama-2-13b-chat_magnitude_semistruct/tree/0.5_2to4) | | |
| | 19 | Llama-2-chat | 13b | [sparsegpt_semistruct](https://huggingface.co/vita-group/llama-2-13b-chat_sparsegpt_semistruct) | [0.5_2to4](https://huggingface.co/vita-group/llama-2-13b-chat_sparsegpt_semistruct/tree/0.5_2to4) | | |
| | 20 | vicuna | 13b | [magnitude_semistruct](https://huggingface.co/vita-group/vicuna-13b-v1.3_magnitude_semistruct) | [0.5_2to4](https://huggingface.co/vita-group/vicuna-13b-v1.3_magnitude_semistruct/tree/0.5_2to4) | | |
| | 21 | vicuna | 13b | [sparsegpt_semistruct](https://huggingface.co/vita-group/vicuna-13b-v1.3_sparsegpt_semistruct) | [0.5_2to4](https://huggingface.co/vita-group/vicuna-13b-v1.3_sparsegpt_semistruct/tree/0.5_2to4) | | |
| | 22 | vicuna-v1.3 | 13b | [gptq](https://huggingface.co/vita-group/vicuna-13b-v1.3_gptq) | [10bit_128g](https://huggingface.co/vita-group/vicuna-13b-v1.3_gptq/tree/10bit_128g) | | |
| | 23 | vicuna-v1.3 | 13b | [gptq](https://huggingface.co/vita-group/vicuna-13b-v1.3_gptq) | [12bit_128g](https://huggingface.co/vita-group/vicuna-13b-v1.3_gptq/tree/12bit_128g) | | |
| | 24 | vicuna-v1.3 | 13b | [gptq](https://huggingface.co/vita-group/vicuna-13b-v1.3_gptq) | [14bit_128g](https://huggingface.co/vita-group/vicuna-13b-v1.3_gptq/tree/14bit_128g) | | |
| | 25 | vicuna-v1.3 | 13b | [gptq](https://huggingface.co/vita-group/vicuna-13b-v1.3_gptq) | [2bit_128g](https://huggingface.co/vita-group/vicuna-13b-v1.3_gptq/tree/2bit_128g) | | |
| | 26 | vicuna-v1.3 | 13b | [gptq](https://huggingface.co/vita-group/vicuna-13b-v1.3_gptq) | [3bit_128g](https://huggingface.co/vita-group/vicuna-13b-v1.3_gptq/tree/3bit_128g) | | |
| | 27 | vicuna-v1.3 | 13b | [gptq](https://huggingface.co/vita-group/vicuna-13b-v1.3_gptq) | [4bit_128g](https://huggingface.co/vita-group/vicuna-13b-v1.3_gptq/tree/4bit_128g) | | |
| | 29 | vicuna-v1.3 | 13b | [gptq](https://huggingface.co/vita-group/vicuna-13b-v1.3_gptq) | [8bit_128g](https://huggingface.co/vita-group/vicuna-13b-v1.3_gptq/tree/8bit_128g) | | |
| | 30 | vicuna-v1.3 | 7b | [gptq](https://huggingface.co/vita-group/vicuna-7b-v1.3_gptq) | [10bit_128g](https://huggingface.co/vita-group/vicuna-7b-v1.3_gptq/tree/10bit_128g) | | |
| | 31 | vicuna-v1.3 | 7b | [gptq](https://huggingface.co/vita-group/vicuna-7b-v1.3_gptq) | [12bit_128g](https://huggingface.co/vita-group/vicuna-7b-v1.3_gptq/tree/12bit_128g) | | |
| | 32 | vicuna-v1.3 | 7b | [gptq](https://huggingface.co/vita-group/vicuna-7b-v1.3_gptq) | [14bit_128g](https://huggingface.co/vita-group/vicuna-7b-v1.3_gptq/tree/14bit_128g) | | |
| | 33 | vicuna-v1.3 | 7b | [gptq](https://huggingface.co/vita-group/vicuna-7b-v1.3_gptq) | [2bit_128g](https://huggingface.co/vita-group/vicuna-7b-v1.3_gptq/tree/2bit_128g) | | |
| | 34 | vicuna-v1.3 | 7b | [gptq](https://huggingface.co/vita-group/vicuna-7b-v1.3_gptq) | [3bit_128g](https://huggingface.co/vita-group/vicuna-7b-v1.3_gptq/tree/3bit_128g) | | |
| | 35 | vicuna-v1.3 | 7b | [gptq](https://huggingface.co/vita-group/vicuna-7b-v1.3_gptq) | [4bit_128g](https://huggingface.co/vita-group/vicuna-7b-v1.3_gptq/tree/4bit_128g) | | |
| | 37 | vicuna-v1.3 | 7b | [gptq](https://huggingface.co/vita-group/vicuna-7b-v1.3_gptq) | [8bit_128g](https://huggingface.co/vita-group/vicuna-7b-v1.3_gptq/tree/8bit_128g) | | |
| ## Citations | |
| If you are using models in this hub, please consider citing our papers. | |
| ```bibtex | |
| @article{jaiswal2023emergence, | |
| title={The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter}, | |
| author={Jaiswal, Ajay and Liu, Shiwei and Chen, Tianlong and Wang, Zhangyang}, | |
| journal={arXiv}, | |
| year={2023} | |
| } | |
| @article{jaiswal2023compressing, | |
| title={Compressing LLMs: The Truth is Rarely Pure and Never Simple}, | |
| author={Ajay Jaiswal and Zhe Gan and Xianzhi Du and Bowen Zhang and Zhangyang Wang and Yinfei Yang}, | |
| year={2023}, | |
| journal={arXiv}, | |
| } | |
| ``` | |
| For any question, please contact [Junyuan Hong](mailto:[email protected]). | |