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 / README.md

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metadata

license: apache-2.0
base_model: MainStack/marvy-1-14B
base_model_relation: quantized
pipeline_tag: text-generation
language:
  - en
tags:
  - servicenow
  - itsm
  - csdm
  - delivery
  - gguf
  - llama.cpp
  - ollama
  - quantized
  - qwen2.5

marvy-1-14B-GGUF

GGUF quants of marvy-1-14B, the first open LLM for the full ServiceNow delivery lifecycle. Run it locally and privately on Apple Silicon, LM Studio, or Ollama.

GGUF quantizations of MainStack/marvy-1-14B for use with llama.cpp, Ollama, LM Studio, and compatible runtimes.

Released under Apache-2.0. Built with Qwen — see NOTICE.

Files

File	Quant	Size (approx)	Use when
`marvy-1-14B-Q4_K_M.gguf`	Q4_K_M	~9 GB	Default — best size/quality balance, laptops
`marvy-1-14B-Q8_0.gguf`	Q8_0	~16 GB	Highest fidelity, near-FP16 quality

Quick start

Ollama

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

llama.cpp

./llama-cli -hf MainStack/marvy-1-14B-GGUF:Q4_K_M \
  -p "Write a ServiceNow user story with acceptance criteria for P1 SLA escalation." \
  --temp 0.4

LM Studio

In the model browser, search MainStack/marvy-1-14B-GGUF and download a quant (Q4_K_M recommended), or drop the .gguf into ~/.lmstudio/models/MainStack/marvy-1-14B-GGUF/.
Load it, set the system prompt below, temperature ~0.4.

To use from code/OpenCode, start the local server:

lms server start          # OpenAI-compatible on http://localhost:1234/v1

Use in OpenCode

Point OpenCode at the local LM Studio (or llama.cpp) server as an OpenAI-compatible provider — see USAGE.md for the exact opencode.json snippet.

Recommended system prompt

You are a senior ServiceNow delivery consultant. You produce precise, implementation-grade
artifacts: business analyses, requirements, solution design documents, user stories with
acceptance criteria, test cases, and validation reviews. You favor out-of-the-box
capabilities, cite concrete tables/plugins/sys_ids when relevant, and write in clear
professional English.

📖 Full usage (all runtimes + OpenCode wiring): USAGE.md · Validate it works: VALIDATION.md

Provenance & limitations

See the merged model card for the full training data, anonymization methodology, evaluation (test ppl 13.107 on a project-disjoint split), and limitations. Quantization adds the usual minor quality reduction versus the FP16 model.

License & attribution

Dual-licensed: weights Apache-2.0, MainStack contributions (cards, docs, benchmark) CC-BY-4.0 — see LICENSING.md. If you use marvy-1-14B as a baseline, fine-tune it, distill from it, or evaluate against it, please credit MainStack and link to https://huggingface.co/MainStack/marvy-1-14B. Keep the NOTICE file intact (required by Apache-2.0 §4) and cite the entry on the merged model card.