Instructions to use zaindanaharper/flywheel-local-coder-14b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use zaindanaharper/flywheel-local-coder-14b 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 zaindanaharper/flywheel-local-coder-14b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf zaindanaharper/flywheel-local-coder-14b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf zaindanaharper/flywheel-local-coder-14b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf zaindanaharper/flywheel-local-coder-14b: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 zaindanaharper/flywheel-local-coder-14b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf zaindanaharper/flywheel-local-coder-14b: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 zaindanaharper/flywheel-local-coder-14b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf zaindanaharper/flywheel-local-coder-14b:Q4_K_M

Use Docker

docker model run hf.co/zaindanaharper/flywheel-local-coder-14b:Q4_K_M

LM Studio
Jan

vLLM

How to use zaindanaharper/flywheel-local-coder-14b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zaindanaharper/flywheel-local-coder-14b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zaindanaharper/flywheel-local-coder-14b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zaindanaharper/flywheel-local-coder-14b:Q4_K_M

Ollama
How to use zaindanaharper/flywheel-local-coder-14b with Ollama:
```
ollama run hf.co/zaindanaharper/flywheel-local-coder-14b:Q4_K_M
```

Unsloth Studio

How to use zaindanaharper/flywheel-local-coder-14b 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 zaindanaharper/flywheel-local-coder-14b 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 zaindanaharper/flywheel-local-coder-14b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for zaindanaharper/flywheel-local-coder-14b to start chatting

How to use zaindanaharper/flywheel-local-coder-14b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf zaindanaharper/flywheel-local-coder-14b: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": "zaindanaharper/flywheel-local-coder-14b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use zaindanaharper/flywheel-local-coder-14b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf zaindanaharper/flywheel-local-coder-14b: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 zaindanaharper/flywheel-local-coder-14b:Q4_K_M

Run Hermes

hermes

OpenClaw new

How to use zaindanaharper/flywheel-local-coder-14b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf zaindanaharper/flywheel-local-coder-14b: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 "zaindanaharper/flywheel-local-coder-14b: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 zaindanaharper/flywheel-local-coder-14b with Docker Model Runner:
```
docker model run hf.co/zaindanaharper/flywheel-local-coder-14b:Q4_K_M
```

Lemonade

How to use zaindanaharper/flywheel-local-coder-14b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull zaindanaharper/flywheel-local-coder-14b:Q4_K_M

Run and chat with the model

lemonade run user.flywheel-local-coder-14b-Q4_K_M

List all available models

lemonade list

Atomic Chat

flywheel-local-coder-14b / safety.md

papacr0w

Ship user-facing docs: model card, benchmarks with evidence, spec sheet, portable usage and safety pages

a0101b6 verified 27 days ago

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Safety and Claims

This page states plainly what this model does and does not claim, so you can weigh it without reading between the lines.

What we claim, and the evidence

The artifact is what it says it is. The build is retraceable hash by hash: corpus content, packed training shards, adapter checkpoint, and the final GGUF are each recorded in provenance.json, and checksums.sha256 ties the chain to the exact file you downloaded.
Reruns are reproducible. At temperature 0 with a fixed seed, completions are byte-identical across runs (recorded output SHA-256 970af540244384407918aa3b0172b403c24d17800e3c514c3c19937d88c7e636).
Benchmark numbers carry their intervals. Everything we measured is on the benchmarks page with confidence intervals and the JSON artifacts beside it.

What we do not claim

No capability uplift over the base model. Our own measurement of that difference includes zero, and we say so rather than rounding up.
No public leaderboard standing. HumanEval, MBPP, and similar suites have not been run yet.
No safety tuning beyond the base model. Refusal behavior, bias, and content boundaries follow Qwen2.5-Coder-14B-Instruct. We have not measured or modified them, so treat them as inherited and unaudited here.

Sensible boundaries for use

Treat generated code the way you would treat code from any assistant: run your tests, review before shipping, and never execute generated code against production systems unreviewed.
The model runs entirely locally and sends nothing anywhere. Network behavior is a property of the runtime you choose (Ollama, llama.cpp), not the weights.
Keep secrets out of prompts as a habit. Nothing in this release requires secrets, keys, or private files to use.

If you find a problem

Open an issue on the model repo with the prompt, the runtime and version, and the observed output. A reproducible report at temperature 0 is the fastest path to a fix, because we can replay it exactly.