Image-Text-to-Text
MLX
Safetensors
qwen3_5
apple-silicon
speculative-decoding
qwen
qwen3
qwen3-next
mtp
mtplx
local-ai
vision-language
multimodal
conversational
Instructions to use Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision 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("Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision") config = load_config("Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision") # 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 Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision"
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": "Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision 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 "Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision"
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 Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision"
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 "Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision" \ --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"
| { | |
| "arch_id": "qwen3-next-mtp", | |
| "artifact_role": "maximum-speed-flat4-candidate", | |
| "base_trunk": "mlx-community/Qwen3.6-27B-4bit", | |
| "exactness_baseline": { | |
| "attention_impl": "mlx_vector_paged", | |
| "context": 64, | |
| "gate": "phase0h-paged-verifier-exactness", | |
| "max_abs_diff": 0.0, | |
| "mode": "decode-from-stock-prefix", | |
| "sample_agreement": 1.0, | |
| "status": "passed", | |
| "topk_overlap_ratio": 1.0, | |
| "total_variation": 0.0, | |
| "verify_tokens": 4 | |
| }, | |
| "generic_vlm_loader_compatible": true, | |
| "mtp_depth_max": 3, | |
| "mtp_file": "mtp/weights.safetensors", | |
| "mtp_sidecar": "native MTP sidecar", | |
| "mtp_sidecar_file": "mtp/weights.safetensors", | |
| "mtplx_version": "0.1.0-preview", | |
| "recommended_draft_lm_head": { | |
| "bits": 3, | |
| "group_size": 64, | |
| "mode": "affine" | |
| }, | |
| "recommended_draft_sampler": { | |
| "temperature": 0.7, | |
| "top_k": 20, | |
| "top_p": 0.95 | |
| }, | |
| "recommended_profile": "sustained", | |
| "sampler": { | |
| "temperature": 0.6, | |
| "top_k": 20, | |
| "top_p": 0.95 | |
| }, | |
| "speed_evidence": { | |
| "acceptance_by_depth": [ | |
| 0.9, | |
| 0.7593, | |
| 0.6476 | |
| ], | |
| "depth": 3, | |
| "verdict": "mtp_depth_wins", | |
| "profile": "sustained", | |
| "forge_verify_rows": [ | |
| { | |
| "acceptance_by_position": [], | |
| "depth": 0, | |
| "finish_reasons": { | |
| "stop": 1 | |
| }, | |
| "hit_token_budget": false, | |
| "hit_token_budget_count": 0, | |
| "multiplier_vs_ar": 1.0, | |
| "quality_passed": true, | |
| "tok_s": 13.66, | |
| "tok_s_decode": 14.49, | |
| "tok_s_e2e": 13.66, | |
| "verify_time_s": null | |
| }, | |
| { | |
| "acceptance_by_position": [ | |
| 0.9233 | |
| ], | |
| "depth": 1, | |
| "finish_reasons": { | |
| "stop": 1 | |
| }, | |
| "hit_token_budget": false, | |
| "hit_token_budget_count": 0, | |
| "multiplier_vs_ar": 1.4381, | |
| "quality_passed": true, | |
| "tok_s": 19.65, | |
| "tok_s_decode": 20.9, | |
| "tok_s_e2e": 19.65, | |
| "verify_time_s": null | |
| }, | |
| { | |
| "acceptance_by_position": [ | |
| 0.9496, | |
| 0.7526 | |
| ], | |
| "depth": 2, | |
| "finish_reasons": { | |
| "stop": 1 | |
| }, | |
| "hit_token_budget": false, | |
| "hit_token_budget_count": 0, | |
| "multiplier_vs_ar": 1.4594, | |
| "quality_passed": true, | |
| "tok_s": 19.94, | |
| "tok_s_decode": 20.79, | |
| "tok_s_e2e": 19.94, | |
| "verify_time_s": null | |
| }, | |
| { | |
| "acceptance_by_position": [ | |
| 0.9, | |
| 0.7593, | |
| 0.6476 | |
| ], | |
| "depth": 3, | |
| "finish_reasons": { | |
| "stop": 1 | |
| }, | |
| "hit_token_budget": false, | |
| "hit_token_budget_count": 0, | |
| "multiplier_vs_ar": 1.3208, | |
| "quality_passed": true, | |
| "tok_s": 18.05, | |
| "tok_s_decode": 19.71, | |
| "tok_s_e2e": 18.05, | |
| "verify_time_s": null | |
| } | |
| ], | |
| "greedy_diagnostic": { | |
| "tok_s": 13.66 | |
| }, | |
| "tok_s": [ | |
| 18.05 | |
| ], | |
| "draft_sampler": { | |
| "temperature": 0.7, | |
| "top_k": 20, | |
| "top_p": 0.95 | |
| }, | |
| "enable_thinking": false, | |
| "mtp_history_policy": "committed", | |
| "measurement_notes": "Re-measured 2026-07-18 on Apple M5 Pro 64GB, MTPLX 1.0.4, mtp_history_policy=committed (CLI default), draft_temperature=0.7 (model recommended_draft_sampler), enable_thinking=false, warm (4 warmup prompts), 8 measured prompts from calibration_coding suite, max_tokens=192, median tok/s. tok_s_e2e = generated_tokens/total_elapsed (wall-clock, includes prefill). tok_s_decode = decode-only. multiplier_vs_ar uses e2e. The prior baked numbers (acceptance 1.0/0.98/0.94, tok/s 63) were on Apple M5 Max 128GB and are not reproducible on M5 Pro 64GB due to lower memory bandwidth; preserved below as historical_m5max. Acceptance is stable across runs (greedy draft gives 0.87/0.74/0.64, recommended 0.7 draft gives 0.90/0.76/0.65 -- within noise).", | |
| "historical_m5max": { | |
| "acceptance_by_depth": [ | |
| 1.0, | |
| 0.9795918367346939, | |
| 0.9387755102040817 | |
| ], | |
| "accepted_by_depth": [ | |
| 49, | |
| 48, | |
| 46 | |
| ], | |
| "correction_tokens": 3, | |
| "depth": 3, | |
| "draft_lm_head": "3-bit affine group64 draft-only head", | |
| "draft_sampler": { | |
| "temperature": 0.7, | |
| "top_k": 20, | |
| "top_p": 0.95 | |
| }, | |
| "drafted_by_depth": [ | |
| 49, | |
| 49, | |
| 49 | |
| ], | |
| "enable_thinking": false, | |
| "greedy_diagnostic": { | |
| "accepted_by_depth": [ | |
| 49, | |
| 49, | |
| 43 | |
| ], | |
| "correction_tokens": 0, | |
| "draft_sampler": { | |
| "temperature": 0.0, | |
| "top_k": 0, | |
| "top_p": 1.0 | |
| }, | |
| "drafted_by_depth": [ | |
| 51, | |
| 51, | |
| 51 | |
| ], | |
| "target_sampler": { | |
| "temperature": 0.0, | |
| "top_k": 0, | |
| "top_p": 1.0 | |
| }, | |
| "tok_s": 60.108346853627076, | |
| "verify_calls": 51 | |
| }, | |
| "older_3bit_tok_s": [ | |
| 60.03822277200243, | |
| 60.06063543439426 | |
| ], | |
| "previous_best_tok_s": [ | |
| 61.52744855840259, | |
| 60.89966247416089 | |
| ], | |
| "profile": "sustained", | |
| "timestamp": "2026-05-03T23:07:00+0100", | |
| "tok_s": [ | |
| 63.05651084048216, | |
| 62.885564723941826 | |
| ], | |
| "verify_calls": 49 | |
| } | |
| }, | |
| "verified_on": { | |
| "hardware": "Apple M5 Pro", | |
| "machine_arch": "arm64", | |
| "macos": "26.5.2", | |
| "model": "Qwen3.6-27B-MTPLX-Optimized-Speed-Vision", | |
| "timestamp": "2026-07-18T00:00:00+00:00" | |
| } | |
| } | |