# 4-bit Quantized Llama 3 Model

## Description
This repository hosts the 4-bit quantized version of the Llama 3 model. Optimized for reduced memory usage and faster inference, this model is suitable for deployment in environments where computational resources are limited.

## Model Details
- **Model Type**: Transformer-based language model.
- **Quantization**: 4-bit precision.
- **Advantages**:
  - **Memory Efficiency**: Reduces memory usage significantly, allowing deployment on devices with limited RAM.
  - **Inference Speed**: Accelerates inference times, depending on the hardware's ability to process low-bit computations.

## How to Use
To utilize this model efficiently, follow the steps below:

### Loading the Quantized Model
Load the model with specific parameters to ensure it utilizes 4-bit precision:
```python
from transformers import AutoModelForCausalLM

model_4bit = AutoModelForCausalLM.from_pretrained("SweatyCrayfish/llama-3-8b-quantized", device_map="auto", load_in_4bit=True)
```
## Adjusting Precision of Components
Adjust the precision of other components, which are by default converted to torch.float16:
```python
import torch
from transformers import AutoModelForCausalLM

model_4bit = AutoModelForCausalLM.from_pretrained("SweatyCrayfish/llama-3-8b-quantized", load_in_4bit=True, torch_dtype=torch.float32)
print(model_4bit.model.decoder.layers[-1].final_layer_norm.weight.dtype)
```
## Citation
Original repository and citations:
@article{llama3modelcard,
  title={Llama 3 Model Card},
  author={AI@Meta},
  year={2024},
  url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}