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

Libraries

How to use MainStack/marvy-1-14B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MainStack/marvy-1-14B-GGUF",
	filename="marvy-14B-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use MainStack/marvy-1-14B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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-server -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-server -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

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 / NOTICE

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	marvy-1-14B
	Copyright 2026 MainStack

	This product is licensed under the Apache License, Version 2.0 (the "License").
	You may obtain a copy of the License in the accompanying LICENSE file or at:

	http://www.apache.org/licenses/LICENSE-2.0

	================================================================================
	Attribution request (downstream use)
	================================================================================

	marvy-1-14B was created by MainStack (https://huggingface.co/MainStack).

	If you use marvy-1-14B as a baseline, fine-tune it, distill from it, evaluate
	against it, or otherwise build on it, please credit MainStack and link to:

	https://huggingface.co/MainStack/marvy-1-14B

	Under the Apache License, Version 2.0, this NOTICE file MUST be retained and
	reproduced in any derivative works and redistributions (License §4(d)).

	================================================================================
	Dual licensing
	================================================================================

	* Model weights (safetensors / GGUF / LoRA adapter): Apache-2.0 (LICENSE).
	* MainStack original contributions — model cards, documentation, benchmark,
	charts, and curated training methodology: CC-BY-4.0 (LICENSE-CC-BY-4.0).

	Reuse of MainStack's contributions requires attribution to MainStack under the
	terms of CC-BY-4.0. See LICENSING.md for the full breakdown.

	================================================================================
	Attribution
	================================================================================

	marvy-1-14B is a fine-tuned derivative of:

	Qwen2.5-14B-Instruct
	Copyright Alibaba Cloud / Qwen Team
	Licensed under the Apache License, Version 2.0
	https://huggingface.co/Qwen/Qwen2.5-14B-Instruct

	The base model weights are the property of their respective authors and are
	used and redistributed in modified (fine-tuned) form under the terms of the
	Apache License, Version 2.0.

	Citation for the base model:

	@misc{qwen2.5,
	title = {Qwen2.5: A Party of Foundation Models},
	author = {Qwen Team},
	year = {2024},
	url = {https://qwenlm.github.io/blog/qwen2.5/}
	}

	@article{qwen2,
	title = {Qwen2 Technical Report},
	author = {Qwen Team},
	journal= {arXiv preprint arXiv:2407.10671},
	year = {2024}
	}

	================================================================================
	Tooling
	================================================================================

	Trained and fused with MLX-LM (https://github.com/ml-explore/mlx-lm),
	Copyright Apple Inc., licensed under the MIT License.

	================================================================================
	Training data provenance
	================================================================================

	marvy-1-14B was fine-tuned on a corpus of anonymized ServiceNow delivery
	artifacts. All customer and partner names were replaced with stable aliases,
	and emails, hostnames, IP addresses, and credential-bearing files were removed
	or redacted prior to training. No customer-identifying information is present
	in the training corpus. See the model card for the full redaction methodology.