inference: false
Vicuña 33b v1.3 (4-bit 128g AWQ Quantized)
Vicuna is a chat assistant trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT.
- Developed by: LMSYS
- Model type: An auto-regressive language model based on the transformer architecture.
- License: Non-commercial license
- Finetuned from model: LLaMA.
This model is a 4-bit 128 group size AWQ quantized model. For more information about AWQ quantization, please click here.
Model Date
July 14, 2023
Model License
Please refer to original Vicuna model license (link).
Please refer to the AWQ quantization license (link).
CUDA Version
This model was successfully tested on CUDA driver v530.30.02 and runtime v11.7 with Python v3.10.11. Please note that AWQ requires NVIDIA GPUs with compute capability of 8.0 or higher.
For Docker users, the nvcr.io/nvidia/pytorch:23.06-py3 image is runtime v12.1 but otherwise the same as the configuration above and has also been verified to work.
How to Use
git clone https://github.com/abhinavkulkarni/llm-awq \
&& cd llm-awq \
&& git checkout e977c5a570c5048b67a45b1eb823b81de02d0d60 \
&& 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 snapshot_download
model_name = "abhinavkulkarni/lmsys-vicuna-33b-v1.3-w4-g128-awq"
# 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 = snapshot_download(model_name)
with init_empty_weights():
model = AutoModelForCausalLM.from_config(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
)
Evaluation
This evaluation was done using LM-Eval.
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| wikitext | 1 | word_perplexity | 9.8210 | ||
| byte_perplexity | 1.5330 | ||||
| bits_per_byte | 0.6163 |
vicuna-33b-v1.3 (4-bit 128-group AWQ)
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| wikitext | 1 | word_perplexity | 9.9924 | ||
| byte_perplexity | 1.5380 | ||||
| bits_per_byte | 0.6210 |
Acknowledgements
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}
}