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Compressed Large Language Models

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Compressed LLM Model Zone

The models are prepared by Visual Informatics Group @ University of Texas at Austin (VITA-group) and Center for Applied Scientific Computing at LLNL. Credits to Ajay Jaiswal, Jinhao Duan, Zhenyu Zhang, Zhangheng Li, Lu Yin, Shiwei Liu and Junyuan Hong.

License: MIT License

Simplified lists:

  • Models: Llama-2 13b, Llama-2 chat 13b, Vicuna 13b v1.3
  • Compression methods:
    • Pruning: Magnitude-based, Wanda, SparseGPT (2:4 semi-structured)
    • Quantization: AWQ, GPTQ (3,4,8 bits)

Setup environment

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 models

How to use pruned models

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

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

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.

@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.

datasets

None public yet