MODEL_CARD.md

# mm-llm-coder-lite-v1 Model Card

<p align="center">
  <img src="https://img.shields.io/badge/Myanmar-LLM-blue?style=for-the-badge&logo=huggingface" alt="Myanmar LLM">
  <img src="https://img.shields.io/badge/License-MIT-green?style=for-the-badge" alt="License">
  <img src="https://img.shields.io/badge/Model-phi--2-orange?style=for-the-badge" alt="Base Model">
</p>

## 📌 Overview

**mm-llm-coder-lite-v1** is a Lite version of the Myanmar Large Language Model, specifically optimized for **efficiency** in Myanmar (Burmese) programming tasks. This model is designed for developers in Myanmar who need a lightweight, fast model for code generation and conversational AI.

### Key Design Goals

- 🚀 **Efficient**: Optimized for low-resource environments
- 💻 **Code-focused**: Specialized in programming tasks
- 🌍 **Myanmar-first**: Built for Myanmar developers

## 📊 Model Specifications

| Specification | Value |
|--------------|-------|
| **Parameters** | ~2.7B (base), ~2.6M (trainable with LoRA) |
| **Base Model** | microsoft/phi-2 |
| **Fine-tuning Method** | LoRA (Low-Rank Adaptation) |
| **Training Data Type** | Myanmar code + conversation dataset |
| **LoRA Rank (r)** | 16 |
| **LoRA Alpha** | 32 |
| **Max Length** | 512 tokens |
| **Training Epochs** | 3 |
| **Learning Rate** | 2e-4 |

## 🚀 Quick Start

### Installation

```bash
pip install torch transformers peft accelerate
```

### Basic Usage (Python)

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model
model_name = "amkyawdev/mm-llm-coder-lite-v1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Set pad token
tokenizer.pad_token = tokenizer.eos_token
```

### Generate Response

```python
# Create prompt in Myanmar format
prompt = """System: သင်သည် မြန်မာစာကျွမ်းကျင်သော AI အကူအညီပေးသူဖြစ်သည်။

User: Python နဲ့ Fibonacci function ရေးပေးပါ။

Assistant:"""

# Generate
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
inputs = {k: v.to(model.device) for k, v in inputs.items()}

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.7,
    top_p=0.95,
    do_sample=True
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

### Using Gradio Space

```python
# Visit: https://huggingface.co/spaces/amkyawdev/mm-llm-coder-lite-v1
# Or use via API
from gradio_client import Client

client = Client("amkyawdev/mm-llm-coder-lite-v1")
result = client.predict(
    "Python နဲ့ list sort လုပ်နည်း",  # user message
    fn_index=0
)
print(result)
```

## 📝 Sample Prompts (Myanmar)

### Example 1: Code Generation

```
User: Python နဲ့ Fibonacci function ရေးပေးပါ။
Assistant: def fibonacci(n):
    if n <= 1:
        return n
    else:
        return fibonacci(n-1) + fibonacci(n-2)
```

### Example 2: Translation

```
User: Hello ပါတ်မှားပါ။
Assistant: မင်္ဂလာပါ။ သင့်အား ကူညီပါသည်။
```

### Example 3: Data Cleaning

```
User: မြန်မာစာသားအမှားမှားပြင်ပါ။
Assistant: import re

def clean_myanmar_text(text):
    # Remove extra spaces
    text = re.sub(r'\s+', ' ', text)
    # ... (more cleaning logic)
    return text
```

## ⚠️ Limitations (Lite Version)

This is a **Lite** version with intentional trade-offs:

### Performance Limitations

| Limitation | Description |
|-----------|------------|
| **Smaller Context** | Max 512 tokens (vs 2048+ in full version) |
| **Limited Knowledge** | Trained on ~20K samples |
| **Code Complexity** | Best for simple to intermediate tasks |
| **Language Coverage** | Primarily Myanmar, limited English |

### Expected Behavior

1. **Fast Inference**: optimized for speed over quality
2. **Simple Tasks**: Good for basic code generation
3. **Complex Tasks**: May struggle with advanced algorithms
4. **Long Conversations**: Context may degrade after ~3-4 turns

### Recommendations for Developers

- Use for: Simple scripts, code translation, learning
- Avoid: Production-grade complex systems, long context tasks
- Fine-tune: For your specific use case if needed

## 📁 Training Data

- **Dataset**: [amkyawdev/myanmar-llm-data](https://huggingface.co/datasets/amkyawdev/myanmar-llm-data)
- **Training Samples**: ~20,327
- **Test Samples**: ~17,155
- **Categories**: Code (90%), Translation, General, Greetings

## 🏷️ Tags

`myanmar` `burmese` `llm` `code-generation` `fine-tuned` `lora` `phi-2` `transformers`

## 📜 License

MIT License - See [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgments

- Microsoft for phi-2 base model
- Hugging Face community
- Myanmar developers

---

<p align="center">
  🇲🇲 Made for Myanmar Developers
</p>