Instructions to use fcmeyer/Qwen3.8-27B-MLX-bf16-mtp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use fcmeyer/Qwen3.8-27B-MLX-bf16-mtp 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("fcmeyer/Qwen3.8-27B-MLX-bf16-mtp") config = load_config("fcmeyer/Qwen3.8-27B-MLX-bf16-mtp") # 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 fcmeyer/Qwen3.8-27B-MLX-bf16-mtp with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "fcmeyer/Qwen3.8-27B-MLX-bf16-mtp"
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": "fcmeyer/Qwen3.8-27B-MLX-bf16-mtp" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use fcmeyer/Qwen3.8-27B-MLX-bf16-mtp with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "fcmeyer/Qwen3.8-27B-MLX-bf16-mtp"
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 "fcmeyer/Qwen3.8-27B-MLX-bf16-mtp" \ --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"
- Hermes Agent
How to use fcmeyer/Qwen3.8-27B-MLX-bf16-mtp 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 "fcmeyer/Qwen3.8-27B-MLX-bf16-mtp"
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 fcmeyer/Qwen3.8-27B-MLX-bf16-mtp
Run Hermes
hermes
- Atomic Chat
Qwen3.8-27B-MLX-bf16-mtp
Full-precision bf16 MLX conversion of Qwen/Qwen3.8-27B with the native MTP (multi-token prediction) head preserved, built for the oMLX inference engine on Apple Silicon.
- Complete VLM: vision tower included (image + video understanding).
- MTP-preserved: all 15
mtp.*tensors kept (mtp_num_hidden_layers: 1), enabling Lightning-MTP speculative decoding in oMLX (mtp_enabled: true). - bf16 everywhere (~52 GB weights): no quantization; the Gated DeltaNet recurrent state self-promotes to fp32 internally at runtime, so bf16 storage is lossless in practice.
- Norm convention: weights are in MLX's shifted (+1) RMSNorm convention, including all MTP-head norms — safe for oMLX's per-key convention detection (no double shift).
Conversion details
- Converter:
mlx_vlm.convert(mlx-vlm 0.6.3, mlx 0.32.0) run with oMLX 0.5.7'smlx_vlm_mtppatches applied (apply_mlx_vlm_mtp_runtime_patch()+apply_mlx_vlm_mtp_patch()before load), which is what preserves the MTP head — stockmlx_vlm.convertstripsmtp.*. dtype="bfloat16",quantize=False.- Source revision:
1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0.
Measured on an M5 Max (128 GB), oMLX 0.5.7
| Config | Decode speed | Notes |
|---|---|---|
| MTP off | ~8.9 tok/s | plain bf16 decode |
| MTP on (depth 3) | ~20.3 tok/s (2.3×) | 71–89% draft acceptance, 2.5–3.2 tok/backbone-cycle |
31K-token prompt prefills in ~40 s (GDN chunked Metal kernel path); needle retrieval and image grounding verified.
Usage with oMLX
Place under ~/.omlx/models/<org>/Qwen3.8-27B-MLX-bf16-mtp (or download via the oMLX admin dashboard), then enable MTP for the model:
"Qwen3.8-27B-MLX-bf16-mtp": { "mtp_enabled": true, "max_context_window": 262144 }
oMLX routes it to the VLM engine automatically and selects the Lightning MTP speculative backend. Sampling defaults from generation_config.json: temperature 1.0, top_p 0.95, top_k 20.
If you want a standalone drafter for oMLX's external vlm_mtp_enabled path instead, you can extract one from this checkpoint's source with python -m mlx_vlm.speculative.drafters.qwen3_5_mtp.split (emits model_type: qwen3_5_mtp, block_size: 3, ~830 MB). The in-checkpoint native path above is faster and is the recommended default.
The model also works with stock mlx-vlm ≥ 0.6.3 as a plain bf16 VLM (the mtp.* tensors are ignored by loaders that don't implement MTP).
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Base model
Qwen/Qwen3.8-27B