Instructions to use johninthepool/Qwen3.8-27B-VL-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use johninthepool/Qwen3.8-27B-VL-mlx-4bit with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("johninthepool/Qwen3.8-27B-VL-mlx-4bit") config = load_config("johninthepool/Qwen3.8-27B-VL-mlx-4bit") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use johninthepool/Qwen3.8-27B-VL-mlx-4bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "johninthepool/Qwen3.8-27B-VL-mlx-4bit"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "johninthepool/Qwen3.8-27B-VL-mlx-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use johninthepool/Qwen3.8-27B-VL-mlx-4bit with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "johninthepool/Qwen3.8-27B-VL-mlx-4bit"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default johninthepool/Qwen3.8-27B-VL-mlx-4bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use johninthepool/Qwen3.8-27B-VL-mlx-4bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "johninthepool/Qwen3.8-27B-VL-mlx-4bit"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "johninthepool/Qwen3.8-27B-VL-mlx-4bit" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Qwen3.8-27B-VL-mlx-4bit
A 4-bit MLX build of Qwen/Qwen3.8-27B with the vision tower preserved, converted with mlx-vlm for Apple Silicon.
Vision vs. speculative decode — this is the vision-capable variant
Qwen3.8-27B ships with a vision encoder. The MTPLX text-only builds in this account are converted with mtplx forge, which preserves the model's multi-token-prediction (MTP) head for native speculative decoding — but drops the vision tower, since mtplx/mlx-lm's conversion path is text-only.
This repo is the inverse tradeoff: converted with mlx-vlm instead, which keeps the vision tower intact so the model can actually process images and video, but does not carry mtplx's MTP contract — no speculative decoding here.
| Variant | Vision | MTP / spec-decode |
|---|---|---|
| MTPLX-4bit | No | Yes |
| MTPLX-8bit | No | Yes |
| MTPLX-bf16 | No | Yes |
| VL-mlx-4bit (this repo) | Yes | No |
Quantization
| Parameter | Value |
|---|---|
| Precision | 4-bit (4.695 effective bits/weight) |
| Group size | 64 |
| Source | Qwen/Qwen3.8-27B (bf16 native) |
| Toolchain | mlx-vlm (not mtplx) |
Requirements
- Apple Silicon Mac (M-series)
- mlx-vlm:
pip install -U mlx-vlm
Usage
python -m mlx_vlm generate --model johninthepool/Qwen3.8-27B-VL-mlx-4bit \
--image path/to/image.jpg --prompt "Describe this image."
Provenance
Converted directly from Qwen/Qwen3.8-27B with no fine-tuning or distillation — a direct 4-bit affine quantization of the release weights, vision tower included.
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4-bit
Model tree for johninthepool/Qwen3.8-27B-VL-mlx-4bit
Base model
Qwen/Qwen3.8-27B