Text Generation
MLX
Safetensors
English
qwen3_5_moe
mtplx
mtp
multi-token-prediction
speculative-decoding
qwen35moe
Mixture of Experts
4-bit precision
conversational
Instructions to use wang-yang/Ornith-1.0-35B-MTPLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use wang-yang/Ornith-1.0-35B-MTPLX with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("wang-yang/Ornith-1.0-35B-MTPLX") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use wang-yang/Ornith-1.0-35B-MTPLX with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "wang-yang/Ornith-1.0-35B-MTPLX"
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": "wang-yang/Ornith-1.0-35B-MTPLX" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use wang-yang/Ornith-1.0-35B-MTPLX 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 "wang-yang/Ornith-1.0-35B-MTPLX"
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 wang-yang/Ornith-1.0-35B-MTPLX
Run Hermes
hermes
- OpenClaw new
How to use wang-yang/Ornith-1.0-35B-MTPLX with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "wang-yang/Ornith-1.0-35B-MTPLX"
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 "wang-yang/Ornith-1.0-35B-MTPLX" \ --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"
- MLX LM
How to use wang-yang/Ornith-1.0-35B-MTPLX with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "wang-yang/Ornith-1.0-35B-MTPLX"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "wang-yang/Ornith-1.0-35B-MTPLX" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "wang-yang/Ornith-1.0-35B-MTPLX", "messages": [ {"role": "user", "content": "Hello"} ] }'
| { | |
| "arch_id": "qwen3-next-mtp", | |
| "artifact_role": "forge-local", | |
| "base_trunk": "ornith-mtp4bit-src", | |
| "exactness_baseline": {}, | |
| "forge_provenance": { | |
| "forge_inputs": { | |
| "mtp_source_path": "ornith-mtp4bit-src", | |
| "trunk_path": "Ornith-1.0-35B-MTP-grafted-mtp4bit" | |
| }, | |
| "forge_recipe": { | |
| "body_bits": 4, | |
| "body_group_size": 64, | |
| "body_mode": "affine", | |
| "mtp_policy": "requantize", | |
| "mtp_quant_bits": 4, | |
| "mtp_quant_group_size": 64, | |
| "mtp_quant_mode": "affine" | |
| }, | |
| "forged_at": "2026-06-27T08:34:45+09:00", | |
| "forged_locally": true, | |
| "mtp_contract": { | |
| "base_hidden_variant": "post_norm", | |
| "concat_order": "embedding_hidden", | |
| "hidden_variant": "post_norm", | |
| "mtp_position_mode": "local", | |
| "mtp_quant_bits": 4, | |
| "mtp_quant_group_size": 64, | |
| "mtp_quant_mode": "affine" | |
| }, | |
| "mtplx_version": "1.0.4", | |
| "published_to_hf": null, | |
| "source_format": "mlx_affine_with_mtp", | |
| "source_repo": "ornith-mtp4bit-src", | |
| "source_sha": null | |
| }, | |
| "mtp_contract": { | |
| "base_hidden_variant": "post_norm", | |
| "concat_order": "embedding_hidden", | |
| "hidden_variant": "post_norm", | |
| "mtp_position_mode": "local", | |
| "mtp_quant_bits": 4, | |
| "mtp_quant_group_size": 64, | |
| "mtp_quant_mode": "affine" | |
| }, | |
| "mtp_depth_max": 3, | |
| "mtp_sidecar": "bf16-qwen-moe-experts", | |
| "mtplx_version": "1.0.4", | |
| "recommended_profile": "sustained", | |
| "sampler": { | |
| "temperature": 0.6, | |
| "top_k": 20, | |
| "top_p": 0.95 | |
| }, | |
| "speed_evidence": { | |
| "acceptance_by_depth": [ | |
| 0.9075144508670521, | |
| 0.7803468208092486, | |
| 0.6416184971098265 | |
| ], | |
| "acceptance_collapsed": [], | |
| "depth": 3, | |
| "failure_reasons": [], | |
| "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": 76.40498246720135, | |
| "verify_time_s": 6.917488250008319 | |
| }, | |
| { | |
| "acceptance_by_position": [ | |
| 0.8956521739130435 | |
| ], | |
| "depth": 1, | |
| "finish_reasons": { | |
| "stop": 1 | |
| }, | |
| "hit_token_budget": false, | |
| "hit_token_budget_count": 0, | |
| "multiplier_vs_ar": 1.3583988701800576, | |
| "quality_passed": true, | |
| "tok_s": 103.78844185957342, | |
| "verify_time_s": 5.282904820574913 | |
| }, | |
| { | |
| "acceptance_by_position": [ | |
| 0.9263157894736842, | |
| 0.7859649122807018 | |
| ], | |
| "depth": 2, | |
| "finish_reasons": { | |
| "stop": 1 | |
| }, | |
| "hit_token_budget": false, | |
| "hit_token_budget_count": 0, | |
| "multiplier_vs_ar": 1.5097969979060482, | |
| "quality_passed": true, | |
| "tok_s": 115.35601315404485, | |
| "verify_time_s": 5.197808049095329 | |
| }, | |
| { | |
| "acceptance_by_position": [ | |
| 0.9075144508670521, | |
| 0.7803468208092486, | |
| 0.6416184971098265 | |
| ], | |
| "depth": 3, | |
| "finish_reasons": { | |
| "stop": 1 | |
| }, | |
| "hit_token_budget": false, | |
| "hit_token_budget_count": 0, | |
| "multiplier_vs_ar": 1.5156776981197617, | |
| "quality_passed": true, | |
| "tok_s": 115.8053279507685, | |
| "verify_time_s": 3.6170050567016006 | |
| } | |
| ], | |
| "greedy_diagnostic": { | |
| "tok_s": 76.40498246720135 | |
| }, | |
| "quality_rejected": [], | |
| "tok_s": [ | |
| 115.8053279507685 | |
| ], | |
| "verdict": "mtp_depth_wins" | |
| }, | |
| "verified_on": { | |
| "hardware": "macOS-15.7.3-arm64-arm-64bit", | |
| "machine_arch": "arm64", | |
| "macos": "15.7.3", | |
| "model": "Ornith-1.0-35B-MTP-grafted-mtp4bit", | |
| "timestamp": "2026-06-27T08:34:45+09:00" | |
| } | |
| } |