Instructions to use MainStack/marvy-1-14B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use MainStack/marvy-1-14B-GGUF 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 MainStack/marvy-1-14B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MainStack/marvy-1-14B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MainStack/marvy-1-14B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MainStack/marvy-1-14B-GGUF: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 MainStack/marvy-1-14B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MainStack/marvy-1-14B-GGUF: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 MainStack/marvy-1-14B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MainStack/marvy-1-14B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/MainStack/marvy-1-14B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use MainStack/marvy-1-14B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MainStack/marvy-1-14B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MainStack/marvy-1-14B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MainStack/marvy-1-14B-GGUF:Q4_K_M

Ollama
How to use MainStack/marvy-1-14B-GGUF with Ollama:
```
ollama run hf.co/MainStack/marvy-1-14B-GGUF:Q4_K_M
```

Unsloth Studio

How to use MainStack/marvy-1-14B-GGUF 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 MainStack/marvy-1-14B-GGUF 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 MainStack/marvy-1-14B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MainStack/marvy-1-14B-GGUF to start chatting

How to use MainStack/marvy-1-14B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MainStack/marvy-1-14B-GGUF: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": "MainStack/marvy-1-14B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use MainStack/marvy-1-14B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MainStack/marvy-1-14B-GGUF: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 MainStack/marvy-1-14B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use MainStack/marvy-1-14B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MainStack/marvy-1-14B-GGUF: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 "MainStack/marvy-1-14B-GGUF: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 MainStack/marvy-1-14B-GGUF with Docker Model Runner:
```
docker model run hf.co/MainStack/marvy-1-14B-GGUF:Q4_K_M
```

Lemonade

How to use MainStack/marvy-1-14B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MainStack/marvy-1-14B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.marvy-1-14B-GGUF-Q4_K_M

List all available models

lemonade list

marvy-1-14B-GGUF / LICENSING.md

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Licensing — marvy-1-14B

marvy-1-14B uses a layered (dual) license that reflects what is built on top of an upstream open model versus what MainStack authored.

Component	License	What it covers
Model weights (`*.safetensors`, GGUF quants, LoRA adapter)	Apache-2.0	The fine-tuned weights. These are a derivative of Qwen2.5-14B-Instruct (Apache-2.0); per that license they remain Apache-2.0 and free to use, modify, and redistribute.
MainStack original contributions	CC-BY-4.0	The model cards, documentation (`USAGE.md`, `VALIDATION.md`, benchmark), the benchmark charts, the curated training-data methodology, and the pipeline framing authored by MainStack.

What this means in practice

You may (under Apache-2.0, for the weights)

Use marvy-1-14B commercially, privately, or in research.
Fine-tune, distill, quantize, merge, or otherwise build on the weights.
Redistribute the weights, including modified versions.

…provided you retain the NOTICE file in derivatives and redistributions (Apache-2.0 §4(d) — this is mandatory and carries the attribution request).

You must (under CC-BY-4.0, for our contributions)

If you reuse MainStack's documentation, model cards, benchmark, or charts — e.g. copying our eval methodology, reproducing our charts, or lifting card text into your own model — you must give attribution: credit "MainStack" and link to https://huggingface.co/MainStack/marvy-1-14B. This is a binding condition of CC-BY-4.0, not just a request.

We ask (attribution for the model)

If you use marvy-1-14B as a baseline, a starting point for your own fine-tune, a distillation source, or an evaluation comparison, please credit MainStack and cite the entry in the model card. See NOTICE and the card's Citation section.

Why the weights can't be more restricted

Qwen2.5-14B-Instruct is released under Apache-2.0, which grants every recipient an irrevocable, royalty-free right to use and redistribute. A fine-tune cannot revoke those rights on the resulting weights. MainStack's protection therefore lives where it legally can: (1) the CC-BY-4.0 license on our own authored materials, and (2) the Apache-2.0 NOTICE that must travel with the weights.

Files

LICENSE — Apache-2.0 (governs the weights; inherited from the base model).
LICENSE-CC-BY-4.0 — CC-BY-4.0 (governs MainStack's documentation and other original contributions).
NOTICE — required attribution notices (retain in derivatives).
CITATION.cff — citation metadata.