---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- multilingual
- instruction-tuning
license: apache-2.0
language:
- en
- kn
datasets:
- charanhu/kannada-instruct-dataset-390k
library_name: transformers
---

# Uploaded Model: devshaheen/llama-3.2-3b-Instruct-finetune

## Overview

- **Developed by:** devshaheen
- **License:** Apache-2.0
- **Finetuned from model:** `unsloth/llama-3.2-3b-instruct-bnb-4bit`
- **Languages Supported:** 
  - **English** (`en`) for general-purpose text generation and instruction-following tasks.
  - **Kannada** (`kn`) with a focus on localized and culturally aware text generation.
- **Dataset Used:** [charanhu/kannada-instruct-dataset-390k](https://huggingface.co/datasets/charanhu/kannada-instruct-dataset-390k)

This model is a fine-tuned version of LLaMA, optimized for multilingual instruction-following tasks with a specific emphasis on English and Kannada. It utilizes 4-bit quantization for efficient deployment in low-resource environments without compromising performance.

---

## Features

### 1. **Instruction Tuning**
The model is trained to follow a wide range of instructions and generate contextually relevant responses. It excels in both creative and factual text generation tasks.

### 2. **Multilingual Support**
The model is capable of generating text in Kannada and English, making it suitable for users requiring bilingual capabilities.

### 3. **Optimized Training**
Training was accelerated using [Unsloth](https://github.com/unslothai/unsloth), achieving **2x faster training** compared to conventional methods. This was complemented by HuggingFace's TRL (Transformers Reinforcement Learning) library to ensure high performance.

### 4. **Efficiency through Quantization**
Built on the `bnb-4bit` quantized model, it is designed for optimal performance in environments with limited computational resources while maintaining precision and depth in output.

---

## Usage Scenarios

### General Use
- Text completion and creative writing.
- Generating instructions or following queries in English and Kannada.

### Specialized Applications
- Localized AI systems in Kannada for chatbots, educational tools, and more.
- Research and development on multilingual instruction-tuned models.

---

## Performance and Metrics

### Evaluation Dataset:
The model was fine-tuned on [charanhu/kannada-instruct-dataset-390k](https://huggingface.co/datasets/charanhu/kannada-instruct-dataset-390k), a comprehensive dataset designed for Kannada instruction tuning. 

### Training Parameters:
- **Base Model:** LLaMA 3.2-3B-Instruct
- **Optimizer:** AdamW
- **Quantization:** 4-bit (bnb-4bit)
- **Framework:** HuggingFace Transformers + Unsloth

---

## Example Usage

### Python Code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_name = "devshaheen/llama-3.2-3b-Instruct-finetune"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
input_text = "How does climate change affect the monsoon in Karnataka?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))