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---
license: cc-by-sa-3.0
tags:
- MosaicML
- AWQ
inference: false
---
# MPT-30B-Instruct (4-bit 128g AWQ Quantized)
[MPT-30B-Instruct](https://huggingface.co/mosaicml/mpt-30b-chat) is a model for short-form instruction following.
This model is a 4-bit 128 group size AWQ quantized model. For more information about AWQ quantization, please click [here](https://github.com/mit-han-lab/llm-awq).
## Model Date
July 5, 2023
## Model License
Please refer to original MPT model license ([link](https://huggingface.co/mosaicml/mpt-30b-chat)).
Please refer to the AWQ quantization license ([link](https://github.com/llm-awq/blob/main/LICENSE)).
## 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
```bash
git clone https://github.com/mit-han-lab/llm-awq \
&& cd llm-awq \
&& git checkout f084f40bd996f3cf3a0633c1ad7d9d476c318aaa \
&& pip install -e . \
&& cd awq/kernels \
&& python setup.py install
```
```python
import time
import torch
from awq.quantize.quantizer import real_quantize_model_weight
from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer, TextStreamer
from accelerate import init_empty_weights, load_checkpoint_and_dispatch
from huggingface_hub import snapshot_download
model_name = "abhinavkulkarni/mosaicml-mpt-30b-chat-w4-g128-awq"
# Config
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
# Tokenizer
try:
tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name, trust_remote_code=True)
except:
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_special_tokens=True)
# 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.tie_weights()
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,
streamer=streamer)
```
## Evaluation
This evaluation was done using [LM-Eval](https://github.com/EleutherAI/lm-evaluation-harness).
[MPT-30B-Instruct](https://huggingface.co/mosaicml/mpt-30b-chat)
| Task |Version| Metric | Value | |Stderr|
|--------|------:|---------------|------:|---|------|
|wikitext| 1|word_perplexity|11.5609| | |
| | |byte_perplexity| 1.5805| | |
| | |bits_per_byte | 0.6603| | |
[MPT-30B-Instruct (4-bit 128-group AWQ)](https://huggingface.co/abhinavkulkarni/mosaicml-mpt-30b-chat-w4-g128-awq)
| Task |Version| Metric | Value | |Stderr|
|--------|------:|---------------|------:|---|------|
|wikitext| 1|word_perplexity|11.6649| | |
| | |byte_perplexity| 1.5831| | |
| | |bits_per_byte | 0.6628| | |
## 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-30B: A New Standard for Open-Source, Commercially Usable LLMs},
year = {2023},
url = {www.mosaicml.com/blog/mpt-30b},
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}
}
```
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