Create README.md

README.md

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+---
+language:
+  - en
+pipeline_tag: text-generation
+inference: false
+tags:
+  - facebook
+  - meta
+  - pytorch
+  - llama
+  - llama-2
+---
+# **Llama 2** (4-bit 128g AWQ Quantized)
+Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format.
+
+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 19, 2023
+
+## Model License
+
+Please refer to the original LLaMA 2 model license ([link](https://huggingface.co/meta-llama/Llama-2-7b-hf)).
+
+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/abhinavkulkarni/llm-awq \
+&& cd llm-awq \
+&& git checkout e977c5a570c5048b67a45b1eb823b81de02d0d60 \
+&& 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
+from accelerate import init_empty_weights, load_checkpoint_and_dispatch
+from huggingface_hub import snapshot_download
+
+model_name = "abhinavkulkarni/meta-llama-Llama-2-7b-chat-hf-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](https://github.com/EleutherAI/lm-evaluation-harness).
+
+[Llama-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
+
+|  Task  |Version|    Metric     | Value |   |Stderr|
+|--------|------:|---------------|------:|---|------|
+|wikitext|      1|word_perplexity|12.1967|   |      |
+|        |       |byte_perplexity| 1.5964|   |      |
+|        |       |bits_per_byte  | 0.6748|   |      |
+
+[Llama-2-7b-chat (4-bit 128-group AWQ)](https://huggingface.co/abhinavkulkarni/meta-llama-Llama-2-7b-chat-hf-w4-g128-awq)
+
+|  Task  |Version|    Metric     | Value |   |Stderr|
+|--------|------:|---------------|------:|---|------|
+|wikitext|      1|word_perplexity|12.5962|   |      |
+|        |       |byte_perplexity| 1.6060|   |      |
+|        |       |bits_per_byte  | 0.6835|   |      |
+
+## 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}
+}
+```