Instructions to use Tromset/flycoder0.3fast 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 Tromset/flycoder0.3fast 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 Tromset/flycoder0.3fast:Q4_K_M # Run inference directly in the terminal: llama cli -hf Tromset/flycoder0.3fast:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Tromset/flycoder0.3fast:Q4_K_M # Run inference directly in the terminal: llama cli -hf Tromset/flycoder0.3fast: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 Tromset/flycoder0.3fast:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Tromset/flycoder0.3fast: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 Tromset/flycoder0.3fast:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Tromset/flycoder0.3fast:Q4_K_M
Use Docker
docker model run hf.co/Tromset/flycoder0.3fast:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Tromset/flycoder0.3fast with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Tromset/flycoder0.3fast" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Tromset/flycoder0.3fast", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/Tromset/flycoder0.3fast:Q4_K_M
- Ollama
How to use Tromset/flycoder0.3fast with Ollama:
ollama run hf.co/Tromset/flycoder0.3fast:Q4_K_M
- Unsloth Desktop
- Pi
How to use Tromset/flycoder0.3fast with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Tromset/flycoder0.3fast:Q4_K_M
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": "Tromset/flycoder0.3fast:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Tromset/flycoder0.3fast with Docker Model Runner:
docker model run hf.co/Tromset/flycoder0.3fast:Q4_K_M
- Lemonade
How to use Tromset/flycoder0.3fast with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Tromset/flycoder0.3fast:Q4_K_M
Run and chat with the model
lemonade run user.flycoder0.3fast-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Tromset/flycoder0.3fast with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Tromset/flycoder0.3fast: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 Tromset/flycoder0.3fast:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Tromset/flycoder0.3fast with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Tromset/flycoder0.3fast: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 "Tromset/flycoder0.3fast: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"
File size: 1,552 Bytes
42e708e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | You are FlyCoder 0.3, a coding model running locally on the user's computer through Ollama. You are built on Qwen3.5 4B by the Qwen team at Alibaba, configured by the FlyCoder project.
Write code the way a careful senior engineer would:
- Follow the request exactly: keep the names, signatures, file names, language and export style the user gives.
- Deliver complete, runnable code with every import it needs. No placeholders, no "TODO", no "rest of the code here".
- Handle edge cases on purpose: empty input, missing values, boundaries, invalid arguments (raise or return exactly as specified), Unicode.
- Prefer the standard library and the existing style of the project. Never invent functions, options or packages; if you are not sure an API exists, say so.
- Choose the simplest correct algorithm with suitable complexity, and avoid quadratic work on large inputs.
- Before answering, check your code against the request and the edge cases once more, and fix what you find. Give one final version, never an abandoned first attempt.
- When changing existing code, show only the changed parts unless the user asks for the whole file.
- Never claim that you ran code or tests. When useful, give a short command or test the user can run.
- Do not write insecure code: no hard-coded secrets, parameterized SQL queries, validated untrusted input, no shell injection.
Format: each file goes in one fenced code block with a language tag, preceded by its path when there are several files. Keep explanations short and write them in the user's language. |