Instructions to use trustos/Qwen3.6-35B-A3B-MTPLX-Vision-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use trustos/Qwen3.6-35B-A3B-MTPLX-Vision-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("trustos/Qwen3.6-35B-A3B-MTPLX-Vision-MTP") config = load_config("trustos/Qwen3.6-35B-A3B-MTPLX-Vision-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 trustos/Qwen3.6-35B-A3B-MTPLX-Vision-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 "trustos/Qwen3.6-35B-A3B-MTPLX-Vision-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": "trustos/Qwen3.6-35B-A3B-MTPLX-Vision-MTP" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use trustos/Qwen3.6-35B-A3B-MTPLX-Vision-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 "trustos/Qwen3.6-35B-A3B-MTPLX-Vision-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 "trustos/Qwen3.6-35B-A3B-MTPLX-Vision-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 trustos/Qwen3.6-35B-A3B-MTPLX-Vision-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 "trustos/Qwen3.6-35B-A3B-MTPLX-Vision-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 trustos/Qwen3.6-35B-A3B-MTPLX-Vision-MTP
Run Hermes
hermes
Qwen3.6-35B-A3B · MTPLX · Vision + MTP (unified coder + VLM)
One local model that both codes at Qwen3.6-35B level and sees images, for Apple Silicon via MTPLX native-MTP serving.
What this is
Qwen/Qwen3.6-35B-A3B ships multimodal (Qwen3_5MoeForConditionalGeneration, 333 model.visual.*
vision-tower tensors) — but common MLX/MTPLX text conversions strip the vision tower, leaving a
text-only build with dangling image_token_ids. This repo restores it:
- Body: MTPLX 8-bit forge of Qwen3.6-35B-A3B (affine,
group_size=64) — an MoE tuned for fast agentic coding on Apple Silicon. - MTP sidecar (
mtp.safetensors): native multi-token-prediction draft head → MTPLX speculative decoding (~100 tok/s decode ladder on an M-series Max). - Vision tower (
vision_tower.safetensors, 333 tensors, bf16 unquantized): extracted from the official checkpoint's ownmodel.visual.*weights (shards 1–2), renamed to thevision_tower.*prefix. Because the tower comes from the same checkpoint as the body, vision-language alignment is preserved by construction — no fine-tuning, no cross-model transplant. - Official multimodal
chat_template.jinjaincluded.
Measured (M5 Max 128 GB, MTPLX 2.0.1)
| Test | Result |
|---|---|
| Coding: 429-line real-module repair (gated agentic loop) | converged 1 turn / 83.6 s — parity with the text-only build (80 s) |
| Coding: small py/ts bug fixtures | 1 turn, 2.6 s / 2.7 s |
OpenAI-style tools calls (4 shapes incl. no-tool-needed) |
4/4, identical to the text-only reference |
| Vision: synthetic shape/color/text reads | 2/2 exact (incl. text color) |
| MTP speculative decode warm-up ladder | ~97–103 tok/s |
Caveats: single-trial agentic benchmarks on one machine; vision checks are synthetic smoke tests, not a VLM benchmark suite.
Run it (MTPLX)
mtplx quickstart --model <this-repo-dir> --port 8092 \
--chat-template-path <this-repo-dir>/chat_template.jinja
# OpenAI-compatible: POST /v1/chat/completions with text and/or image_url content parts
Reasoning ON by default; serve with --reasoning off for terse agentic use.
Provenance & license
Derived solely from Qwen/Qwen3.6-35B-A3B
(Apache-2.0). Quantization + MTP packaging via MTPLX forge; vision tower restored verbatim (bf16) from
the same checkpoint. No third-party weights. Apache-2.0, same as the base.
- Downloads last month
- 271
8-bit
Model tree for trustos/Qwen3.6-35B-A3B-MTPLX-Vision-MTP
Base model
Qwen/Qwen3.6-35B-A3B