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

A 14.8B coding model in a single file that runs entirely on your own machine. It takes Qwen2.5-Coder-14B-Instruct and continues its pretraining on a 66-million-token corpus drawn from a real working development ecosystem, then packs the result into one Q4_K_M GGUF just under 9 GB. Your prompts and your code never leave your disk. And if you ever care to look, the whole build, corpus to weights, can be retraced hash by hash.

Run it in two commands

hf download zaindanaharper/flywheel-local-coder-14b telos-coder-14b-cpt2020-q4_k_m.gguf --local-dir .
llama-cli -m telos-coder-14b-cpt2020-q4_k_m.gguf -cnv

Prefer Ollama? Download the repo folder so the GGUF and the Modelfile sit together, then:

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

No conversion step, no shards, no Python environment. The usage guide covers chat, deterministic completion, an OpenAI-compatible local endpoint, and how to verify your download against the published checksums.

Specs at a glance


Parameters	14.8B (qwen2 architecture)
Context length	32,768 tokens
Quantization	Q4_K_M, single GGUF file
File size	8.99 GB (8,988,110,880 bytes)
Capabilities	chat, code completion, tool calling
Base model	Qwen2.5-Coder-14B-Instruct
Training	QLoRA continued pretraining, 66.2M tokens across 17,997 files
License	Apache-2.0 (with Qwen attribution)
SHA-256	`613db240e3efc6730f24042a4602d1f12f1c6b397af1d5a4d74f4e064d4064be`

Full details in the spec sheet.

What to expect

This is a local-first daily coding companion: completions, small functions, refactors, and tool-calling on your own hardware, with your code staying home.

We publish measurements, not adjectives. On our internal evaluation sets the model passes 8 of 8 baseline tasks and 8 of 10 deliberately contract-heavy hard tasks in a single attempt, with confidence intervals attached to every number. We do not claim a capability uplift over the base model: our own measurement of that difference includes zero, and the benchmarks page says so plainly. Every number there ships with the JSON it came from and the method to re-run it.

The documents

Usage guide: run it with Ollama, llama.cpp, or as a local API.
Benchmarks: what we measured, the intervals, and how to re-run it.
Spec sheet: hardware guidance, training details, formats.
Safety and claims: what this model does and does not claim.
Model card: the full technical card.
provenance.json and checksums.sha256: the retraceable chain from corpus to the exact bytes you downloaded.

License and attribution

Apache-2.0. Built on Qwen2.5-Coder-14B-Instruct by the Qwen team; see LICENSE for the attribution notice. The training corpus is proprietary to the author; the shipped weights carry no third-party code beyond the base model.

Downloads last month: -

GGUF

Model size

15B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for zaindanaharper/flywheel-local-coder-14b

Base model

Qwen/Qwen2.5-14B

Finetuned

Qwen/Qwen2.5-Coder-14B

Finetuned

Qwen/Qwen2.5-Coder-14B-Instruct

Quantized

(111)

this model