updated readme

README.md

@@ -6,17 +6,87 @@ tags:
 - unsloth
 - llama
 - trl
+- multilingual
+- instruction-tuning
 license: apache-2.0
 language:
 - en
+- kn
+datasets:
+- charanhu/kannada-instruct-dataset-390k
+library_name: transformers
 ---
 
-# Uploaded  model
+# 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
+- **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
 
-This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+# 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)
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+# 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))