README.md

# TinyLlama Inference

This code demonstrates how to load and run inference using the `huzaifa1117/tinyllama_AWQ_4bit` model with quantization for efficient computation on CUDA devices.

## Installation

To begin, ensure you have the necessary libraries installed:

```bash
pip install torch transformers peft awq
```

## Usage

### Model Loading and Inference

```python
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    HqqConfig
)
from peft import PeftModel
import torch
from awq import AutoAWQForCausalLM

# Use CUDA if available
device = torch.device("cuda")

# Model ID and quantization configuration
model_id = "huzaifa1117/tinyllama_AWQ_4bit"
quant_config = HqqConfig(nbits=1, group_size=64, quant_zero=False, quant_scale=False, axis=1)

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Load the model with quantization on CUDA
model = AutoAWQForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, use_cache=False, device_map='cuda')

# Move the model to the CUDA device
model.to(device)

# Tokenize input and run inference
input_text = "Your input text here"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)
output = model.generate(input_ids, max_length=50)

# Decode and print the output
output_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(output_text)
```

### Notes

- This setup is designed for efficient computation using quantization, reducing model size and computation cost.
- Ensure that you have a CUDA-capable GPU for running this code efficiently.

## Quantization

The model uses `HqqConfig` to apply 1-bit quantization for all linear layers, ensuring high performance on resource-constrained hardware:

```python
quant_config = HqqConfig(nbits=1, group_size=64, quant_zero=False, quant_scale=False, axis=1)
```

## License

This project is licensed under the terms of the MIT license.