WYNN747/burmese-coder-4b-mlx

Burmese Coder 4B - MLX (4-bit)

Burmese Coder 4B is an instruction-tuned model based on Gemma 3 4B, specifically fine-tuned to assist with programming tasks in the Burmese language. This repository contains the model in the native MLX format, optimized for Apple Silicon (M1/M2/M3/M4).

🚀 Quick Start: LM Studio (Recommended)

This model is fully compatible with LM Studio for a seamless, local experience on macOS.

📦 Setup Instructions

1. Download the model:

   • In LM Studio, go to the Search tab.
   • Enter WYNN747/burmese-coder-4b-mlx and click Download.
   • Alternatively, you can use the Local Folder import feature for this specific directory.

2. Configure the Inference Engine:

   • Ensure you are using the LM Studio MLX backend (found in Settings > Engines & Frameworks).
   • If you see a "Missing Library" error, click the Fix button in the MLX settings.

Link : https://lmstudio.ai/download

📊 Model Performance

• Quantization: 4-bit affline (quantized via mlx-lm)
• VRAM Usage: ~2.3 GB
• Inference Speed: ~60 tokens/sec (M-series Pro/Max chips)
• Primary Focus: Python, Burmese language instruction-following, and natural language explanations of code.

🐍 Python Usage (MLX)

For programmatic access, install mlx-lm:

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("WYNN747/burmese-coder-4b-mlx")

messages = [
    {"role": "user", "content": "Python မှာ list တစ်ခုကို ဘယ်လို sort လုပ်ရမလဲ?"}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

response = generate(model, tokenizer, prompt=prompt, verbose=True)
print(response)

📝 Training Details

• Base Model: unsloth/gemma-3-4b-it
• Adapter: WYNN747/burmese-coder-4b (LoRA merged)
• Dataset: Custom Burmese MBPP and HumanEval-translated datasets.
• Conversion: Merged and quantized using the MLX framework for maximum performance on macOS.

📜 License

This model is released under the Apache 2.0 license.