metadata

license: cc-by-sa-3.0
tags:
  - MosaicML
  - AWQ
inference: false

MPT-7B-Chat (4-bit 128g AWQ Quantized)

MPT-7B-Chat is a chatbot-like model for dialogue generation.

This model is a 4-bit 128 group size AWQ quantized model. For more information about AWQ quantization, please click here.

Model Date

July 5, 2023

Model License

Please refer to original MPT model license (link).

Please refer to the AWQ quantization license (link).

CUDA Version

This model was successfully tested on CUDA driver v12.1 and toolkit v11.7 with Python v3.10.11.

How to Use

git clone https://github.com/mit-han-lab/llm-awq \
&& cd llm-awq \
&& git checkout 71d8e68df78de6c0c817b029a568c064bf22132d \
&& pip install -e . \
&& cd awq/kernels \
&& python setup.py install

import torch
from awq.quantize.quantizer import real_quantize_model_weight
from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer
from accelerate import init_empty_weights, load_checkpoint_and_dispatch
from huggingface_hub import hf_hub_download

model_name = "mosaicml/mpt-7b-chat"

# Config
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)

# Tokenizer
tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name)

# Model
w_bit = 4
q_config = {
    "zero_point": True,
    "q_group_size": 128,
}

load_quant = hf_hub_download('abhinavkulkarni/mpt-7b-chat-w4-g128-awq', 'pytorch_model.bin')

with init_empty_weights():
    model = AutoModelForCausalLM.from_pretrained(model_name, config=config, 
                                                 torch_dtype=torch.float16, trust_remote_code=True)

real_quantize_model_weight(model, w_bit=w_bit, q_config=q_config, init_only=True)

model = load_checkpoint_and_dispatch(model, load_quant, device_map="balanced")

# Inference
prompt = f'''What is the difference between nuclear fusion and fission?
###Response:'''

input_ids = tokenizer(prompt, return_tensors='pt').input_ids.cuda()
output = model.generate(
    inputs=input_ids, 
    temperature=0.7,
    max_new_tokens=512,
    top_p=0.15,
    top_k=0,
    repetition_penalty=1.1,
    eos_token_id=tokenizer.eos_token_id
)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Evaluation

This evaluation was done using LM-Eval.

MPT-7B-Chat

Task	Version	Metric	Value
wikitext	1	word_perplexity	13.5936
		byte_perplexity	1.6291
		bits_per_byte	0.7040

MPT-7B-Chat (4-bit 128-group AWQ)

Task	Version	Metric	Value
wikitext	1	word_perplexity	14.0922
		byte_perplexity	1.6401
		bits_per_byte	0.7138

Acknowledgements

The MPT model was originally finetuned by Sam Havens and the MosaicML NLP team. Please cite this model using the following format:

@online{MosaicML2023Introducing,
    author    = {MosaicML NLP Team},
    title     = {Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs},
    year      = {2023},
    url       = {www.mosaicml.com/blog/mpt-7b},
    note      = {Accessed: 2023-03-28}, % change this date
    urldate   = {2023-03-28} % change this date
}

The model was quantized with AWQ technique. If you find AWQ useful or relevant to your research, please kindly cite the paper:

@article{lin2023awq,
  title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
  author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
  journal={arXiv},
  year={2023}
}