This repository contains a 3-bit quantized MLX version of the Qwen/Qwen3.8-27B multimodal (vision-language) model, optimized for Apple Silicon via MLX and mlx-vlm.

📊 Overview & Model Summary

Attribute	Details
Base Model	Qwen/Qwen3.8-27B
Architecture	Vision-Language Dense Model (27 Billion Parameters)
Quantization	3-bit uniform quantization (`--q-bits 3`)
Format	MLX Native (`.safetensors`)
Estimated VRAM	~11–12 GB Unified Memory (vs. ~54 GB in FP16 / ~15 GB in 4-bit)
Primary Target	Apple Silicon Macs (M1/M2/M3/M4 with 16GB+ Unified Memory)

💡 Why 3-Bit?

Uniform 2-bit quantization often degrades weights to the point of producing corrupt output ("garbage/word salad").

3-bit quantization provides an optimal middle ground for memory-constrained systems:

Significantly higher coherence & reasoning capabilities compared to 2-bit.
Lower memory footprint (~11–12 GB VRAM) compared to standard 4-bit (~15 GB VRAM), fitting comfortably on 16GB RAM Macs with system overhead to spare.

🚀 Quickstart & Usage Guide

1. Prerequisites & Installation

Ensure you have Python 3.10+ and the required MLX multimodal packages installed:

pip install -U mlx-vlm mlx

2. Run Generation via CLI

To test the model with a text prompt directly from your terminal:

bash

python3 -m mlx_vlm.generate \
  --model <YOUR_HF_USERNAME>/Qwen3.8-27B-3bit-mlx \
  --prompt "Hello! Write a clean Python script to reverse a string."

For multimodal/vision inputs:

bash

python3 -m mlx_vlm.generate \
  --model <YOUR_HF_USERNAME>/Qwen3.8-27B-3bit-mlx \
  --image https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg \
  --prompt "Describe what you see in this image."

💻 Integration with VS Code & Local Clients

Serve this model locally using an OpenAI-compatible HTTP endpoint to connect with extensions like Continue.dev, Roo Code, or Cline.

Step 1: Start the Server

bash

python3 -m mlx_vlm.server \
  --model <YOUR_HF_USERNAME>/Qwen3.8-27B-3bit-mlx \
  --port 8000

Step 2: Configure Continue.dev (`config.json`)

Add the following provider entry to your ~/.continue/config.json:

json

{
  "models": [
    {
      "title": "Qwen 3.8 27B (3-bit MLX)",
      "provider": "openai",
      "model": "default",
      "apiBase": "http://127.0.0.1:8000/v1",
      "apiKey": "sk-local"
    }
  ]
}

⚡ Precision & Performance Comparison

Precision / Quant	Disk Size	Min VRAM	Coherence / Output Quality
FP16 (Base)	~54 GB	~60 GB	Baseline (100%)
4-Bit MLX	~15 GB	~16 GB	High (~95% quality retention)
3-Bit MLX (This Repo)	~11 GB	~12 GB	Moderate-High (Usable logic, fits 16GB Macs)
2-Bit MLX	~7.5 GB	~8 GB	Low (Prone to output artifacts / garbage)

🛠️ Reproduction / How This Model Was Created

Converted using mlx_vlm.convert on Apple Silicon:

bash

# Convert base model to 3-bit MLX format
python3 -m mlx_vlm.convert \
  --hf-path Qwen/Qwen3.8-27B \
  -q \
  --q-bits 3 \
  --mlx-path ~/models/Qwen3.8-27B-3bit-mlx

# Push to Hugging Face Hub
hf repo create Qwen3.8-27B-3bit-mlx --type model
hf upload <YOUR_HF_USERNAME>/Qwen3.8-27B-3bit-mlx ~/models/Qwen3.8-27B-3bit-mlx .

📜 License & Acknowledgments

Base Model License: Apache 2.0 (inherited from Qwen family)
Framework: Built with Apple's MLX framework.

mickyba/Qwen3.8-27B-3bit-mlx

Qwen 3.8 27B — 3-Bit MLX Model

📊 Overview & Model Summary

💡 Why 3-Bit?

🚀 Quickstart & Usage Guide

1. Prerequisites & Installation

2. Run Generation via CLI

💻 Integration with VS Code & Local Clients

Step 1: Start the Server

Step 2: Configure Continue.dev (config.json)

⚡ Precision & Performance Comparison

🛠️ Reproduction / How This Model Was Created

📜 License & Acknowledgments

mickyba/
Qwen3.8-27B-3bit-mlx

Step 2: Configure Continue.dev (`config.json`)