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---
license: cc-by-sa-3.0
language:
- en
tags:
- AWQ
inference: false
---
# VMware/open-llama-7B-v2-open-instruct (4-bit 128g AWQ Quantized)
Instruction-tuned version of the fully trained Open LLama 7B v2 model. The model is open for <b>COMMERCIAL USE</b>. <br>
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 12, 2023
## Model License
Please refer to original OpenLLaMa model license ([link](https://huggingface.co/VMware/open-llama-7b-v2-open-instruct)).
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 ce4a6bb1c238c014a06672cb74f6865573494d66 \
&& pip install -e . \
&& cd awq/kernels \
&& python setup.py install
```
```python
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/VMware-open-llama-7b-v2-open-instruct"
# 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).
[Open-LLaMA-7B-v2-Instruct](https://huggingface.co/VMware/open-llama-7b-v2-open-instruct)
| Task |Version| Metric | Value | |Stderr|
|--------|------:|---------------|------:|---|------|
|wikitext| 1|word_perplexity|16.6822| | |
| | |byte_perplexity| 1.6927| | |
| | |bits_per_byte | 0.7593| | |
[Open-LLaMA-7B-v2-Instruct (4-bit 128-group AWQ)](https://huggingface.co/abhinavkulkarni/VMware-open-llama-7b-v2-open-instruct-w4-g128-awq)
| Task |Version| Metric | Value | |Stderr|
|--------|------:|---------------|------:|---|------|
|wikitext| 1|word_perplexity|17.1546| | |
| | |byte_perplexity| 1.7015| | |
| | |bits_per_byte | 0.7668| | |
## Acknowledgements
If you found OpenLLaMA useful in your research or applications, please cite using the following BibTeX:
```
@software{openlm2023openllama,
author = {Geng, Xinyang and Liu, Hao},
title = {OpenLLaMA: An Open Reproduction of LLaMA},
month = May,
year = 2023,
url = {https://github.com/openlm-research/open_llama}
}
```
```
@software{together2023redpajama,
author = {Together Computer},
title = {RedPajama-Data: An Open Source Recipe to Reproduce LLaMA training dataset},
month = April,
year = 2023,
url = {https://github.com/togethercomputer/RedPajama-Data}
}
```
```
@article{touvron2023llama,
title={Llama: Open and efficient foundation language models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and others},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
```
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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