---
library_name: transformers
tags:
- 4bit
- AWQ
- AutoAWQ
- llama
- llama-2
- facebook
- meta
- 7b
- quantized
license: llama2
pipeline_tag: text-generation
---

# Model Card for alokabhishek/Llama-2-7b-chat-hf-4bit-AWQ

<!-- Provide a quick summary of what the model is/does. -->

This repo contains 4-bit quantized (using AutoAWQ) model of Meta's meta-llama/Llama-2-7b-chat-hf

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration is developed by MIT-HAN-Lab


## Model Details

- Model creator: [Meta](https://huggingface.co/meta-llama)
- Original model: [Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)


### About 4 bit quantization using AutoAWQ

- AutoAWQ github repo: [AutoAWQ github repo](https://github.com/casper-hansen/AutoAWQ/tree/main)
- MIT-han-lab llm-aws github repo:  [MIT-han-lab llm-aws github repo](https://github.com/mit-han-lab/llm-awq/tree/main)

@inproceedings{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 Chen, Wei-Ming and Wang, Wei-Chen and Xiao, Guangxuan and Dang, Xingyu and Gan, Chuang and Han, Song},
  booktitle={MLSys},
  year={2024}
}


# How to Get Started with the Model

Use the code below to get started with the model.

## How to run from Python code

#### First install the package
```shell
!pip install autoawq
!pip install accelerate
```

#### Import 

```python
import torch
import os
from torch import bfloat16
from huggingface_hub import login, HfApi, create_repo
from transformers import AutoTokenizer, pipeline
from awq import AutoAWQForCausalLM
```

#### Use a pipeline as a high-level helper

```python
# define the model ID
model_id_llama = "alokabhishek/Llama-2-7b-chat-hf-4bit-AWQ"

# Load model
tokenizer_llama = AutoTokenizer.from_pretrained(model_id_llama, use_fast=True)
model_llama = AutoAWQForCausalLM.from_quantized(model_id_llama, fuse_layer=True, trust_remote_code = False, safetensors = True)

# Set up the prompt and prompt template. Change instruction as per requirements.
prompt_llama = "Tell me a funny joke about Large Language Models meeting a Blackhole in an intergalactic Bar."
fromatted_prompt = f'''[INST] <<SYS>> You are a helpful, and fun loving assistant. Always answer as jestfully as possible. <</SYS>> {prompt_llama} [/INST] '''

tokens = tokenizer_llama(fromatted_prompt, return_tensors="pt").input_ids.cuda()

# Generate output, adjust parameters as per requirements
generation_output = model_llama.generate(tokens, do_sample=True, temperature=1.7, top_p=0.95, top_k=40, max_new_tokens=512)

# Print the output
print(tokenizer_llama.decode(generation_output[0], skip_special_tokens=True))


```


## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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## Model Card Authors [optional]

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## Model Card Contact

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