Instructions to use zesming/Qwen3.8-27B-oQ4e-mtp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use zesming/Qwen3.8-27B-oQ4e-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("zesming/Qwen3.8-27B-oQ4e-mtp") config = load_config("zesming/Qwen3.8-27B-oQ4e-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 zesming/Qwen3.8-27B-oQ4e-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 "zesming/Qwen3.8-27B-oQ4e-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": "zesming/Qwen3.8-27B-oQ4e-mtp" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use zesming/Qwen3.8-27B-oQ4e-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 "zesming/Qwen3.8-27B-oQ4e-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 zesming/Qwen3.8-27B-oQ4e-mtp
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use zesming/Qwen3.8-27B-oQ4e-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 "zesming/Qwen3.8-27B-oQ4e-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 "zesming/Qwen3.8-27B-oQ4e-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"
Qwen3.8-27B-oQ4e-mtp
This is an MLX oQ4e quantization of Qwen/Qwen3.8-27B, produced with oMLX 0.5.7.
Quantization
- Quantizer: oMLX oQ4e
- Base precision target: 4-bit affine, group size 64
- Effective mixed-precision budget reported by oMLX: approximately 4.9 bpw
- Calibration: oQe imatrix-weighted affine quantization
- Non-quantized tensor dtype: bfloat16
- Native MTP weights and configuration retained
- Vision tower retained; this is not a text-only conversion
- Output: four safetensors shards, approximately 15.83 GB
oQ4e uses oMLX layer-sensitivity planning together with activation-importance calibration. Architecture protection rules leave the vision encoder unquantized and protect output-critical tensors.
Multimodal configuration note
The official base checkpoint includes both preprocessor_config.json and video_preprocessor_config.json. oMLX 0.5.7 copied the image preprocessor configuration during quantization but did not copy the separate video preprocessor sidecar. The original video_preprocessor_config.json from Qwen/Qwen3.8-27B was therefore added unchanged to this repository so that the official image/video processor metadata is preserved.
No vision weights were reconstructed or borrowed from another checkpoint.
Validation
The converted checkpoint was validated locally with oMLX 0.5.7:
- Loaded successfully as a VLM.
- Native Lightning MTP was selected and activated at runtime.
- Text generation completed successfully with MTP draft acceptance recorded by oMLX.
- An OpenAI-compatible multimodal API request correctly identified the main subject of a test image.
- The checkpoint index contains the retained language_model.mtp.* tensors and the full vision_tower.* tensor set.
Suggested oMLX settings
For thinking mode, the Qwen model card recommends:
- temperature=1.0
- top_p=0.95
- top_k=20
- min_p=0.0
- presence_penalty=0.0
- repetition_penalty=1.0
This conversion supports enable_thinking, preserve_thinking, and reasoning_effort (xhigh, medium, or low). Native Lightning MTP can be enabled in the oMLX model settings.
License and attribution
The base model is licensed under Apache 2.0. See the included LICENSE file and the official Qwen3.8-27B model card for details.
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Qwen/Qwen3.8-27B