Instructions to use Tromset/flycoder0.3pro 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/flycoder0.3pro 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/flycoder0.3pro:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Tromset/flycoder0.3pro: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/flycoder0.3pro:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Tromset/flycoder0.3pro: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/flycoder0.3pro:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Tromset/flycoder0.3pro: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/flycoder0.3pro:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Tromset/flycoder0.3pro:Q4_K_M

Use Docker

docker model run hf.co/Tromset/flycoder0.3pro:Q4_K_M

LM Studio
Jan

vLLM

How to use Tromset/flycoder0.3pro with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tromset/flycoder0.3pro"
# 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/flycoder0.3pro",
		"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/flycoder0.3pro:Q4_K_M

Ollama
How to use Tromset/flycoder0.3pro with Ollama:
```
ollama run hf.co/Tromset/flycoder0.3pro:Q4_K_M
```
Unsloth Desktop

How to use Tromset/flycoder0.3pro with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Tromset/flycoder0.3pro: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/flycoder0.3pro:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Tromset/flycoder0.3pro with Docker Model Runner:
```
docker model run hf.co/Tromset/flycoder0.3pro:Q4_K_M
```

Lemonade

How to use Tromset/flycoder0.3pro with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Tromset/flycoder0.3pro:Q4_K_M

Run and chat with the model

lemonade run user.flycoder0.3pro-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Tromset/flycoder0.3pro 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/flycoder0.3pro: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/flycoder0.3pro:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Tromset/flycoder0.3pro with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Tromset/flycoder0.3pro: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/flycoder0.3pro: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"

flycoder0.3pro / README.md

Tromset

FlyCoder 0.3: flycoder0.3pro from ollama.com/Tromset/flycoder0.3pro

f091428 verified 3 days ago

preview code

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metadata

license: apache-2.0
base_model: Qwen/Qwen3.8-27B
base_model_relation: quantized
pipeline_tag: image-text-to-text
tags:
  - gguf
  - ollama
  - llama.cpp
  - code
  - flycoder

FlyCoder 0.3 pro

The strongest FlyCoder, for hard coding work and coding agents, on Macs with 32 GB of memory or more.

ollama run hf.co/Tromset/flycoder0.3pro

Base	Size	Mac	Context
Qwen3.8 27B (Apache 2.0)	18 GB	32 GB	64K tokens

Code that runs: exact names and signatures, every import, edge cases handled, a final check before answering, no "TODO", no invented APIs.
Built for agents: reads files before editing, makes minimal changes, reports real test output, never runs a destructive command without asking.
Fast for its size: multi-token prediction is on in Ollama, and 64K tokens of context cost only about 4.4 GB thanks to Qwen3.8's hybrid attention.

Files

flycoder0.3pro-Q4_K_M.gguf: the weights, the same file as Tromset/flycoder0.3pro on ollama.com.
mmproj-flycoder0.3pro.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:

llama-server -hf Tromset/flycoder0.3pro --jinja -c 65536 --temp 0.6 --top-k 20 --top-p 0.95 --min-p 0

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

Other sizes: flycoder0.3 for 16 GB Macs, flycoder0.3fast for 8 GB Macs.

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