Text Generation
MLX
Safetensors
hy_v3
mixture-of-experts
apple-silicon
conversational
4-bit precision
Instructions to use avlp12/Hy3-Alis-MLX-Dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use avlp12/Hy3-Alis-MLX-Dynamic 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("avlp12/Hy3-Alis-MLX-Dynamic") 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 avlp12/Hy3-Alis-MLX-Dynamic with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "avlp12/Hy3-Alis-MLX-Dynamic"
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": "avlp12/Hy3-Alis-MLX-Dynamic" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use avlp12/Hy3-Alis-MLX-Dynamic with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "avlp12/Hy3-Alis-MLX-Dynamic"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "avlp12/Hy3-Alis-MLX-Dynamic" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "avlp12/Hy3-Alis-MLX-Dynamic", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use avlp12/Hy3-Alis-MLX-Dynamic 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 "avlp12/Hy3-Alis-MLX-Dynamic"
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 avlp12/Hy3-Alis-MLX-Dynamic
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use avlp12/Hy3-Alis-MLX-Dynamic with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "avlp12/Hy3-Alis-MLX-Dynamic"
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 "avlp12/Hy3-Alis-MLX-Dynamic" \ --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"
Commit History
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docs: link parent collection
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card: add 'Final state, all tiers' summary
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card: add tier-by-tier tuning rationale (why only T128 got sensitivity-placement + ZH-DWQ)
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T128 v2: sensitivity-ranked down3 + ZH-mixed DWQ (KL 0.802->0.550, ZH 1.43->0.83), tag T128-v1
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fig3: T128 v2 numbers
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MTP notes: corrected 4-bit draft analysis (measured draft latency + per-prompt acceptance)
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MTP section: measured self-spec results (k=1 1.13x lossless) + harness usage
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T128 branch now DWQ-distilled: KL 0.942->0.802, all tables/figures updated
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fig3: T128 DWQ-distilled values
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model card: head-to-head comparison vs 4 other Hy3 MLX ports (same harness)
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model card: add figures (tier fit, recipe, KL-by-language)
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card figures
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card figures
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card figures
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model card: 4-tier table, measured quality/needle/memory, usage, T128 experimental warning
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Add files using upload-large-folder tool
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initial commit
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