Instructions to use spacecomx/Qwen3.8-27B-oQ6e-fp16-mtp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use spacecomx/Qwen3.8-27B-oQ6e-fp16-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("spacecomx/Qwen3.8-27B-oQ6e-fp16-mtp") config = load_config("spacecomx/Qwen3.8-27B-oQ6e-fp16-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 spacecomx/Qwen3.8-27B-oQ6e-fp16-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 "spacecomx/Qwen3.8-27B-oQ6e-fp16-mtp"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "spacecomx/Qwen3.8-27B-oQ6e-fp16-mtp" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use spacecomx/Qwen3.8-27B-oQ6e-fp16-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 "spacecomx/Qwen3.8-27B-oQ6e-fp16-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 spacecomx/Qwen3.8-27B-oQ6e-fp16-mtp
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use spacecomx/Qwen3.8-27B-oQ6e-fp16-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 "spacecomx/Qwen3.8-27B-oQ6e-fp16-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 "spacecomx/Qwen3.8-27B-oQ6e-fp16-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-oQ6e-fp16-mtp
An oQ mixed-precision MLX quantization of Qwen/Qwen3.8-27B, packaged for Apple Silicon.
Quantization
- Base quantization: 6-bit affine, group size 64
- Selective higher precision: the token embeddings, LM head, and selected linear-attention / MLP projections remain 8-bit; see
config.jsonfor the complete per-module configuration - Format: MLX safetensors
- MTP: retained (
mtp_num_hidden_layers: 1); speculative decoding requires runtime support
Chat template
chat_template.jinja uses the Froggeric Qwen Fixed Chat Templates template, version qwen3.8-froggeric-v22.2.
This is an intentional behavioral change from the upstream Qwen template. It adds compatibility handling for system/developer messages, thinking-history fields, tool-call serialization, and tool responses. It does not alter the model weights or improve the underlying model capability. The template is intended for compatible Jinja-capable runtimes such as oMLX/MLX, llama.cpp, LM Studio, and vLLM. Review the upstream template project for its supported options and runtime-specific setup.
Usage
Use a current MLX-compatible runtime that supports MLX safetensors, the quantization metadata in config.json, and the included chat_template.jinja.
oMLX discovers models from subdirectories of its configured model directory:
omlx serve --model-dir /path/to/models
The server exposes an OpenAI-compatible API on port 8000 by default:
curl http://127.0.0.1:8000/v1/chat/completions \
-H 'Content-Type: application/json' \
-d '{
"model": "Qwen3.8-27B-oQ6e-fp16-mtp",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}'
For Qwen reasoning mode, use temperature: 0.6, top_p: 0.95, top_k: 20, and min_p: 0. For non-thinking mode, use temperature: 0.7, top_p: 0.8, top_k: 20, and min_p: 0.
Avoid greedy decoding while thinking is enabled; Qwen documents that it can cause repetitive output. The checkpoint retains its multi-token-prediction head, but speculative decoding requires runtime support.
The included chat_template.jinja is the Froggeric fixed template. Use a runtime that honors external Jinja chat templates and per-module quantization metadata. Compatibility outside oMLX, including mlx-lm and mlx-vlm, depends on the installed runtime version and has not been validated for this release.
License and attribution
Apache 2.0, inherited from Qwen/Qwen3.8-27B. A copy of
the license is included as LICENSE. Quantization does not change the licensing terms — all
credit for the underlying model belongs to the Qwen team. This repository distributes a quantized derivative. The included
chat template is derived from the Apache-2.0-licensed Froggeric Qwen Fixed Chat Templates.
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