Instructions to use trigger2k25/Qwen3.6-35B-A3B-heretic-BF16-mtp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use trigger2k25/Qwen3.6-35B-A3B-heretic-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("trigger2k25/Qwen3.6-35B-A3B-heretic-BF16-mtp") config = load_config("trigger2k25/Qwen3.6-35B-A3B-heretic-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 trigger2k25/Qwen3.6-35B-A3B-heretic-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 "trigger2k25/Qwen3.6-35B-A3B-heretic-BF16-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": "trigger2k25/Qwen3.6-35B-A3B-heretic-BF16-mtp" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use trigger2k25/Qwen3.6-35B-A3B-heretic-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 "trigger2k25/Qwen3.6-35B-A3B-heretic-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 trigger2k25/Qwen3.6-35B-A3B-heretic-BF16-mtp
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use trigger2k25/Qwen3.6-35B-A3B-heretic-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 "trigger2k25/Qwen3.6-35B-A3B-heretic-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 "trigger2k25/Qwen3.6-35B-A3B-heretic-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"
Qwen3.6-35B-A3B Heretic — MLX BF16, Vision + Native MTP
Full multimodal MLX-BF16 conversion of
llmfan46/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved,
pinned to revision 9599ac17d26daf33daf0fdd8f6c897ff4c6dc89a.
This build preserves all three required components:
- the 40-layer Qwen3.6-35B-A3B MoE language backbone;
- the complete 333-tensor vision tower and image/video processor metadata;
- the embedded one-layer Native-MTP head (no external draft model).
The source has 1,045 tensors: 333 vision tensors, 19 Native-MTP tensors, and 693 remaining language tensors. The MLX conversion has 1,086 tensors because the 40 backbone MoE gate/up tensors and the one MTP MoE gate/up tensor are split into MLX runtime projections. All saved tensors remain BF16; no quantization is applied.
oMLX compatibility
Verified against oMLX commit 4cb5516d3de3184209cdfaa53369c8c33b6a91ba
and its pinned mlx-vlm commit
78b96eb5462141447b9a6b4943ef553891da56dd. The verification requires:
- strict oMLX VLM loading;
- a bound
language_model.mtphead andmtp_forwardruntime method; - actual image preprocessing and vision-embedding generation;
- a short end-to-end image-conditioned generation.
Copy or clone the repository into the oMLX model directory, select it as a Vision-Language Model, and enable Native MTP in the model settings. Do not select an external draft model for Native MTP.
BF16 is not eligible for oMLX Qwen ANE Prompt Processing, which currently accepts affine 4/5/6/8-bit projections with group size 64 or 128. This build therefore uses the GPU for BF16 prompt processing.
Reproducibility
The conversion project records its source pins and includes a static verifier
plus a runtime smoke test. The generated conversion-manifest.json records the
exact source revision and tensor counts.
Safety and licensing
This is an uncensored/heretic derivative. Outputs may be inaccurate, offensive, unsafe, or unlawful for a particular use. Apply independent safety controls and human review. Licensing and use restrictions of the upstream checkpoint and all of its base models continue to apply; consult the linked source repository before redistribution or deployment.
- Downloads last month
- 334
Quantized
Model tree for trigger2k25/Qwen3.6-35B-A3B-heretic-BF16-mtp
Base model
Qwen/Qwen3.6-35B-A3B