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

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

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"

flycoder0.3fast

File size: 1,651 Bytes

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---
license: apache-2.0
base_model: Qwen/Qwen3.5-4B
base_model_relation: quantized
pipeline_tag: image-text-to-text
tags:
  - gguf
  - ollama
  - llama.cpp
  - code
  - flycoder
---

# FlyCoder 0.3 fast

The fast FlyCoder, for Macs with 8 GB of memory or when speed matters most.

```sh
ollama run hf.co/Tromset/flycoder0.3fast
```

| Base | Size | Mac | Context |
|---|---|---|---|
| [Qwen3.5 4B](https://huggingface.co/Qwen/Qwen3.5-4B) (Apache 2.0) | 3.3 GB | 8 GB | 32K tokens |

- **Fast**: about twice the speed of `flycoder0.3`, with the same coding rules.
- **Code that runs**: exact names and signatures, every import, edge cases handled, no "TODO", no invented APIs.
- **Light**: fits next to your other apps on an 8 GB Mac.

## Files

- `flycoder0.3fast-Q4_K_M.gguf`: the weights, the same file as [`Tromset/flycoder0.3fast`](https://ollama.com/Tromset/flycoder0.3fast) on ollama.com.
- `mmproj-flycoder0.3fast.gguf`: the vision projector, for images.
- `system`, `params`: FlyCoder's system prompt and sampling settings, applied by Ollama.

With llama.cpp, pass the same settings and the contents of `system` as the system message:

```sh
llama-server -hf Tromset/flycoder0.3fast --jinja -c 32768 --temp 0.6 --top-k 20 --top-p 0.95 --min-p 0
```

For tool calling and thinking in Ollama, `ollama run Tromset/flycoder0.3fast` uses Ollama's built-in Qwen renderer and is the reference build.

Other sizes: [`flycoder0.3`](https://huggingface.co/Tromset/flycoder0.3) for 16 GB Macs, [`flycoder0.3pro`](https://huggingface.co/Tromset/flycoder0.3pro) for 32 GB Macs or more.

Source, benchmark and installer: https://github.com/Tromset/flycoder