Instructions to use phaseshift-studio/mtron-qwen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use phaseshift-studio/mtron-qwen with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf phaseshift-studio/mtron-qwen:Q4_K_M # Run inference directly in the terminal: llama cli -hf phaseshift-studio/mtron-qwen:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf phaseshift-studio/mtron-qwen:Q4_K_M # Run inference directly in the terminal: llama cli -hf phaseshift-studio/mtron-qwen:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf phaseshift-studio/mtron-qwen:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf phaseshift-studio/mtron-qwen:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf phaseshift-studio/mtron-qwen:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf phaseshift-studio/mtron-qwen:Q4_K_M
Use Docker
docker model run hf.co/phaseshift-studio/mtron-qwen:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use phaseshift-studio/mtron-qwen with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "phaseshift-studio/mtron-qwen" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "phaseshift-studio/mtron-qwen", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/phaseshift-studio/mtron-qwen:Q4_K_M
- Ollama
How to use phaseshift-studio/mtron-qwen with Ollama:
ollama run hf.co/phaseshift-studio/mtron-qwen:Q4_K_M
- Unsloth Studio
How to use phaseshift-studio/mtron-qwen with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for phaseshift-studio/mtron-qwen to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for phaseshift-studio/mtron-qwen to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for phaseshift-studio/mtron-qwen to start chatting
- Pi
How to use phaseshift-studio/mtron-qwen with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf phaseshift-studio/mtron-qwen:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "phaseshift-studio/mtron-qwen:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use phaseshift-studio/mtron-qwen with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf phaseshift-studio/mtron-qwen:Q4_K_M
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 phaseshift-studio/mtron-qwen:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use phaseshift-studio/mtron-qwen with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf phaseshift-studio/mtron-qwen:Q4_K_M
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 "phaseshift-studio/mtron-qwen:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use phaseshift-studio/mtron-qwen with Docker Model Runner:
docker model run hf.co/phaseshift-studio/mtron-qwen:Q4_K_M
- Lemonade
How to use phaseshift-studio/mtron-qwen with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull phaseshift-studio/mtron-qwen:Q4_K_M
Run and chat with the model
lemonade run user.mtron-qwen-Q4_K_M
List all available models
lemonade list
Qwen models fine-tuned on the mtron programming language
http://metatron.phaseshift.studio
Overview
mtron-qwen is a family of Qwen models fine-tuned with QLoRA on the mtron functional programming language of the metatron vm. Each variant can evaluate mtron expressions, explain language concepts, and translate between mtron sugar operators and their desugared instruction forms.
Variants
| Variant | Base Model | Params | Size | Files |
|---|---|---|---|---|
| mtron-qwen-4b | Qwen3-4B | 4B | 2.5 GB | mtron-qwen-4b.Q4_K_M.gguf |
| mtron-qwen-8b | Qwen3-8B | 8B | 5.0 GB | mtron-qwen-8b.Q4_K_M.gguf |
| mtron-qwen-14b | Qwen3-14B | 14B | 8.4 GB | mtron-qwen-14b.Q4_K_M.gguf |
Training
All variants share the same training methodology:
| Parameter | Value |
|---|---|
| Method | QLoRA (bitsandbytes 4-bit NF4) |
| LoRA rank (r) | 32 |
| LoRA alpha | 8 |
| Optimizer | AdamW 8-bit |
| Scheduler | Cosine with warmup |
| Dataset | mtron expression evaluation pairs with operator documentation (2,660 entries) |
| Hardware | 2ร NVIDIA RTX 3090 (48 GB) |
Per-Variant Training Details
| Metric | mtron-qwen-4b | mtron-qwen-8b | mtron-qwen-14b |
|---|---|---|---|
| Base model | Qwen3-4B | Qwen3-8B | Qwen3-14B |
| Training steps | 600 | 600 | 600 |
| Batch size (effective) | 8 | 8 | 8 |
| Initial loss | 3.81 | 3.15 | 3.24 |
| Best loss | 0.27 | 0.24 | 0.23 |
| Final loss | 0.81 | 0.76 | 0.69 |
| Training time | ~15 min | ~20 min | 31 min |
Training Plots
Qwen3-4B
Qwen3-8B
Qwen3-14B
Usage
Ollama
Create a Modelfile (example for 14B variant):
FROM ./mtron-qwen-14b.Q4_K_M.gguf
TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
{{ .Response }}<|im_end|>"""
PARAMETER temperature 0.7
PARAMETER stop "<|im_end|>"
Then register and run:
ollama create mtron-qwen-14b -f Modelfile
ollama run mtron-qwen-14b
Prompt Format (ChatML)
<|im_start|>system
You are an expert in the mtron functional programming language.
Evaluate the given mtron expression and return the result.<|im_end|>
<|im_start|>user
/m/str/"hello" /m/str/plus(" world")<|im_end|>
<|im_start|>assistant
"hello world"<|im_end|>
mtron Language
mtron is a data-oriented functional language for the Metatron VM. Expressions follow a structural navigation pattern using URI-addressed spaces and instruction-based evaluation.
License
AGPL-3.0
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