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README.md
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---
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license: cc
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language:
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- en
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tags:
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- AWQ
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inference: false
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---
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# VMware/open-llama-7B-open-instruct (4-bit 128g AWQ Quantized)
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[Instruction-tuned version](https://huggingface.co/VMware/open-llama-7b-open-instruct) of the fully trained [Open LLama 7B](https://huggingface.co/openlm-research/open_llama_7b) model.
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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).
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## Model Date
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July 5, 2023
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## Model License
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Please refer to original MPT model license ([link](https://huggingface.co/VMware/open-llama-7b-open-instruct)).
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Please refer to the AWQ quantization license ([link](https://github.com/llm-awq/blob/main/LICENSE)).
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## CUDA Version
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This model was successfully tested on CUDA driver v12.1 and toolkit v11.7 with Python v3.10.11.
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## How to Use
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```bash
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git clone https://github.com/mit-han-lab/llm-awq \
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&& cd llm-awq \
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&& git checkout 71d8e68df78de6c0c817b029a568c064bf22132d \
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&& pip install -e .
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```
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```python
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import torch
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from awq.quantize.quantizer import real_quantize_model_weight
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from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer
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from accelerate import init_empty_weights, load_checkpoint_and_dispatch
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from huggingface_hub import hf_hub_download
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model_name = "VMware/open-llama-7b-open-instruct"
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# Config
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config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
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# Tokenizer
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tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name)
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# Model
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w_bit = 4
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q_config = {
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"zero_point": True,
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"q_group_size": 128,
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}
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load_quant = hf_hub_download('abhinavkulkarni/open-llama-7b-open-instruct-w4-g128-awq', 'pytorch_model.bin')
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with init_empty_weights():
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model = AutoModelForCausalLM.from_pretrained(model_name, config=config,
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torch_dtype=torch.float16, trust_remote_code=True)
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real_quantize_model_weight(model, w_bit=w_bit, q_config=q_config, init_only=True)
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model = load_checkpoint_and_dispatch(model, load_quant, device_map="balanced")
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# Inference
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prompt = f'''What is the difference between nuclear fusion and fission?
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###Response:'''
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input_ids = tokenizer(prompt, return_tensors='pt').input_ids.cuda()
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output = model.generate(
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inputs=input_ids,
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temperature=0.7,
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max_new_tokens=512,
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top_p=0.15,
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top_k=0,
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repetition_penalty=1.1,
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eos_token_id=tokenizer.eos_token_id
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)
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print(tokenizer.decode(output[0]))
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```
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## Evaluation
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This evaluation was done using [LM-Eval](https://github.com/EleutherAI/lm-evaluation-harness).
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[Open-LLaMA-7B-Instruct](https://huggingface.co/VMware/open-llama-7b-open-instruct)
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| Task |Version| Metric | Value | |Stderr|
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|--------|------:|---------------|------:|---|------|
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|wikitext| 1|word_perplexity|11.7531| | |
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| | |byte_perplexity| 1.5853| | |
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| | |bits_per_byte | 0.6648| | |
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[Open-LLaMA-7B-Instruct (4-bit 128-group AWQ)](https://huggingface.co/abhinavkulkarni/open-llama-7b-open-instruct-w4-g128-awq)
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| Task |Version| Metric | Value | |Stderr|
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|--------|------:|---------------|------:|---|------|
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|wikitext| 1|word_perplexity|12.1840| | |
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| | |byte_perplexity| 1.5961| | |
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| | |bits_per_byte | 0.6745| | |
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## Acknowledgements
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If you found OpenLLaMA useful in your research or applications, please cite using the following BibTeX:
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```
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@software{openlm2023openllama,
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author = {Geng, Xinyang and Liu, Hao},
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title = {OpenLLaMA: An Open Reproduction of LLaMA},
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month = May,
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year = 2023,
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url = {https://github.com/openlm-research/open_llama}
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}
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```
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```
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@software{together2023redpajama,
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author = {Together Computer},
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title = {RedPajama-Data: An Open Source Recipe to Reproduce LLaMA training dataset},
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month = April,
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year = 2023,
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url = {https://github.com/togethercomputer/RedPajama-Data}
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}
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```
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```
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@article{touvron2023llama,
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title={Llama: Open and efficient foundation language models},
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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},
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journal={arXiv preprint arXiv:2302.13971},
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year={2023}
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}
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```
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The model was quantized with AWQ technique. If you find AWQ useful or relevant to your research, please kindly cite the paper:
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```
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@article{lin2023awq,
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title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
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author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
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journal={arXiv},
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year={2023}
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}
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```
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