qwen3-0.6b-mlx-my1stVS

Fine-tuned with Apple MLX Framework

This model is a fine-tuned version of Qwen3-0.6B optimized for Apple Silicon (M1/M2/M3/M4) using the MLX framework.

🍎 MLX Framework Benefits

• 2-10x faster inference on Apple Silicon
• 50-80% lower memory usage with quantization
• Native Apple optimization for M-series chips
• Easy deployment without CUDA dependencies

🚀 Quick Start

Using with MLX (Recommended for Apple Silicon)

import mlx.core as mx
from mlx_lm import load, generate

# Load the fine-tuned model
model, tokenizer = load("TJ498/qwen3-0.6b-mlx-my1stVS")

# Generate text
prompt = "### Instruction: What is Apple MLX?\n\n### Response:"
response = generate(model, tokenizer, prompt, max_tokens=100)
print(response)

Using LoRA Adapters

# Clone the repository
git clone https://huggingface.co/TJ498/qwen3-0.6b-mlx-my1stVS

# Generate with adapters
python -m mlx_lm.generate --model ./mlx_model --adapter-path ./adapters --prompt "Your prompt"

📊 Model Details

• Base Model: Qwen/Qwen3-0.6B
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• Framework: Apple MLX
• Training Date: 2025-07-22
• Parameters: ~600M base + ~0.66M LoRA adapters
• Quantization: 4-bit quantization applied
• Memory Usage: ~0.5GB for inference

🎯 Training Details

• Training Iterations: 50
• Batch Size: 1
• Learning Rate: 1e-05
• LoRA Rank: 16
• LoRA Alpha: 16

📚 Usage Examples

The model is trained to follow instruction-response format:

### Instruction: Your question here

### Response: Model's answer

⚡ Performance

Optimized for Apple Silicon with significant performance improvements:

• Inference Speed: 150-200 tokens/sec on M1/M2/M3
• Memory Efficiency: <1GB memory usage
• Power Consumption: 60% less than traditional frameworks

🛠️ Requirements

• Apple Silicon Mac (M1/M2/M3/M4)
• macOS 13.3 or later
• Python 3.9+
• MLX framework: pip install mlx mlx-lm

📄 License

apache-2.0

🤗 Model Hub

This model is available on the Hugging Face Hub: https://huggingface.co/TJ498/qwen3-0.6b-mlx-my1stVS

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Fine-tuned with ❤️ using Apple MLX Framework