Instructions to use npateman/fusewalk-models 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 npateman/fusewalk-models 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 npateman/fusewalk-models # Run inference directly in the terminal: llama cli -hf npateman/fusewalk-models
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf npateman/fusewalk-models # Run inference directly in the terminal: llama cli -hf npateman/fusewalk-models
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 npateman/fusewalk-models # Run inference directly in the terminal: ./llama-cli -hf npateman/fusewalk-models
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 npateman/fusewalk-models # Run inference directly in the terminal: ./build/bin/llama-cli -hf npateman/fusewalk-models
Use Docker
docker model run hf.co/npateman/fusewalk-models
- LM Studio
- Jan
- Ollama
How to use npateman/fusewalk-models with Ollama:
ollama run hf.co/npateman/fusewalk-models
- Unsloth Desktop
- Pi
How to use npateman/fusewalk-models with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf npateman/fusewalk-models
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "npateman/fusewalk-models" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use npateman/fusewalk-models with Docker Model Runner:
docker model run hf.co/npateman/fusewalk-models
- Lemonade
How to use npateman/fusewalk-models with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull npateman/fusewalk-models
Run and chat with the model
lemonade run user.fusewalk-models-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use npateman/fusewalk-models with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf npateman/fusewalk-models
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 npateman/fusewalk-models
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use npateman/fusewalk-models with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf npateman/fusewalk-models
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 "npateman/fusewalk-models" \ --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"
Fusewalk models
Fine-tuned models for Fusewalk, a crafting game: give a model two items and it answers what they make.
Each model is a 4-bit (Q4_K_M) GGUF for llama.cpp. index.json lists them with the exact prompt template and stop strings each one needs.
Modifications
Each model is a fine-tune of the base model listed below. It was trained (LoRA merged into the weights, or a full fine-tune) on the Fusewalk Codex, a hand-written set of item-combination recipes, so that it answers with the names of the items two items make. It was then converted to GGUF and quantised to 4-bit. The base models' licences and notices are in licenses/, copied unchanged from the base model repos.
Models
| Model | Fine-tune of | Training | Licence | Codex | Size | Plausible |
|---|---|---|---|---|---|---|
| 20261014-135949-qwen3.5-4b-v0.4 | Qwen/Qwen3.5-4B | LoRA | apache-2.0 | v0.4 | 2,583 MB | 74% |
Plausible is the share of answers that make sense, on pairs held out from training: an LLM judge rates each answer good, mash-up (the two names glued together), weak or nonsense, and this counts the good ones. There's often more than one sensible answer, so it says more than an exact match with the Codex would.
Try it
The prompt is two quoted item names, in alphabetical order, joined with +, inside the model's prompt template. The answer is one or two quoted names on the first line. With llama.cpp and the newest model:
llama-completion -m 20261014-135949-qwen3.5-4b-v0.4.gguf -no-cnv --temp 0 -n 24 \
-p '<|im_start|>user\n"Fire" + "Water"<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n'
It answers something like:
"Steam"
Each model's template is prompt_template in index.json, with {pair} where the items go. Stop at the first newline.
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We're not able to determine the quantization variants.