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---
title: README
emoji: 🐇
colorFrom: pink
colorTo: indigo
sdk: static
pinned: false
---
# Compressed LLM Model Zone
The models are prepared by [Visual Informatics Group @ University of Texas at Austin (VITA-group)](https://vita-group.github.io/) and
[Center for Applied Scientific Computing](https://computing.llnl.gov/casc) at [LLNL](https://www.llnl.gov/).
Credits to Ajay Jaiswal, Jinhao Duan, 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'compressed-llm/{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 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 = 'compressed-llm/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 = 'compressed-llm/llama-2-7b_wanda_2_4_gptq_4bit_128g'
# tokenizer_path = 'meta-llama/Llama-2-7b-hf'
model_path = 'compressed-llm/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])
```
## 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]).
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