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

Libraries

How to use zaindanaharper/flywheel-local-coder-14b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="zaindanaharper/flywheel-local-coder-14b",
	filename="telos-coder-14b-cpt2020-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Atomic Chat new

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

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

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

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Usage Guide

Everything below assumes you downloaded this repo folder, so the GGUF and the Modelfile sit together in your working directory.

Verify your download (optional, 10 seconds)

sha256sum telos-coder-14b-cpt2020-q4_k_m.gguf

Compare against checksums.sha256. A match means you hold the exact bytes the provenance chain describes.

Ollama

ollama create flywheel-local-coder-14b -f Modelfile
ollama run flywheel-local-coder-14b

That gives you interactive chat. Ollama also exposes an OpenAI-compatible API the moment the model is created, so any tool that speaks the OpenAI chat format can use the model locally:

curl http://127.0.0.1:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"flywheel-local-coder-14b","messages":[{"role":"user","content":"Write a Python function that merges overlapping intervals."}]}'

Point your editor plugin, agent framework, or script at http://127.0.0.1:11434/v1 with model name flywheel-local-coder-14b and you have a private, zero-cost coding endpoint.

llama.cpp

Interactive chat, one command:

llama-cli -m telos-coder-14b-cpt2020-q4_k_m.gguf -cnv

Deterministic completion (the exact configuration our receipts use):

llama-completion -m telos-coder-14b-cpt2020-q4_k_m.gguf --temp 0 --seed 7 -n 256 -p "your prompt"

At temperature 0 with a fixed seed, reruns are byte-identical. That is not a nicety: it is what lets a benchmark number on the benchmarks page be re-checked by someone who is not us.

Tool calling

The model supports tool/function calling through Ollama's OpenAI-compatible endpoint: pass a tools array in the request as you would with any OpenAI-style API.

Tips

Give it the full contract. The model was benchmarked on prompts that state every rule (exact exception messages, edge cases, output format). It rewards precise asks.
Pair it with your tests. Its natural habitat is a propose-then-verify loop: let it write, run your tests, keep what passes.
32,768-token context: enough for a large file plus conversation, not an entire repository. Feed it the relevant slice.