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

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, 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, 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))