README.md

---
base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

# Uploaded  model: Llama 3.1 8B Finetuned

- **Developed by:** kparkhade
- **License:** apache-2.0
- **Base model :** unsloth/Meta-Llama-3.1-8B-bnb-4bit

## Overview
This fine-tuned Llama 3.1 8B model was optimized for efficient text generation tasks. 
By leveraging advanced optimization techniques from [Unsloth](https://github.com/unslothai/unsloth) and [Hugging Face's](https://huggingface.co/docs/trl/) TRL library, 
training was completed 2x faster than conventional methods.

### Key Features
- **Speed Optimized:** Training was accelerated with the Unsloth framework, significantly reducing resource consumption.
- **Model Compatibility:** Compatible with Hugging Face's ecosystem for seamless integration.
- **Quantization:** Built on a 4-bit quantized base model for efficient deployment and inference.

## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "kparkhade/Llama-3.1-8B"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Your input prompt here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Applications
This model can be used for:
- Creative writing (e.g., story or poetry generation)
- Generating conversational responses
- Assisting with coding-related queries

## Acknowledgements
Special thanks to the [Unsloth](https://github.com/unslothai/unsloth) team for providing tools that make model fine-tuning faster and more efficient.