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

tgetsov commited on Jun 1

Commit

cb513af

verified ·

1 Parent(s): 9b991d4

Upload VALIDATION.md with huggingface_hub

Browse files

Files changed (1) hide show

VALIDATION.md +6 -6

VALIDATION.md CHANGED Viewed

@@ -1,4 +1,4 @@
-# Validating marvy-14B
 This guide gives you three independent ways to confirm the fine-tune actually
 learned the ServiceNow delivery style — from a 60-second smoke test to a
@@ -11,7 +11,7 @@ quantitative base-vs-marvy comparison on a held-out, customer-disjoint test set.
 ## What "working" means here
-marvy-14B is a **specialist drafting model**. A successful fine-tune should show:
 1. **Format fidelity** — it emits the delivery artifact shape on cue (user
    stories with acceptance criteria, SDD sections, test cases with
@@ -34,13 +34,13 @@ structured artifact with no format coaching.
 ### LM Studio (local)
 ```bash
-lms load MainStack/marvy-14B
 lms server start          # OpenAI-compatible on http://localhost:1234/v1
 curl -s http://localhost:1234/v1/chat/completions \
   -H "Content-Type: application/json" \
   -d '{
-    "model": "marvy-14B",
     "temperature": 0.4,
     "messages": [
       {"role": "system", "content": "You are a senior ServiceNow delivery consultant. You produce precise, implementation-grade artifacts and favor out-of-the-box capabilities."},
@@ -52,7 +52,7 @@ curl -s http://localhost:1234/v1/chat/completions \
 ### MLX (Apple Silicon)
 ```bash
-python -m mlx_lm generate --model MainStack/marvy-14B \
   --system-prompt "You are a senior ServiceNow delivery consultant..." \
   --prompt "Write a user story with acceptance criteria for auto-escalating P1 incidents that breach a 15-minute response SLA." \
   --max-tokens 512 --temp 0.4
@@ -134,5 +134,5 @@ python -m mlx_lm lora \
 | Invents `sys_id`s / plugin IDs | expected limitation | verify against a real instance; never trust IDs blindly |
 | marvy ppl ≈ base ppl | adapter not applied / wrong checkpoint | confirm `--adapter-path` points at the trained adapter (iter-150) |
-marvy-14B is a first-draft assistant. All output must be reviewed by a qualified
 ServiceNow consultant before client delivery or production configuration.

+# Validating marvy-1-14B
 This guide gives you three independent ways to confirm the fine-tune actually
 learned the ServiceNow delivery style — from a 60-second smoke test to a
 ## What "working" means here
+marvy-1-14B is a **specialist drafting model**. A successful fine-tune should show:
 1. **Format fidelity** — it emits the delivery artifact shape on cue (user
    stories with acceptance criteria, SDD sections, test cases with
 ### LM Studio (local)
 ```bash
+lms load MainStack/marvy-1-14B
 lms server start          # OpenAI-compatible on http://localhost:1234/v1
 curl -s http://localhost:1234/v1/chat/completions \
   -H "Content-Type: application/json" \
   -d '{
+    "model": "marvy-1-14B",
     "temperature": 0.4,
     "messages": [
       {"role": "system", "content": "You are a senior ServiceNow delivery consultant. You produce precise, implementation-grade artifacts and favor out-of-the-box capabilities."},
 ### MLX (Apple Silicon)
 ```bash
+python -m mlx_lm generate --model MainStack/marvy-1-14B \
   --system-prompt "You are a senior ServiceNow delivery consultant..." \
   --prompt "Write a user story with acceptance criteria for auto-escalating P1 incidents that breach a 15-minute response SLA." \
   --max-tokens 512 --temp 0.4
 | Invents `sys_id`s / plugin IDs | expected limitation | verify against a real instance; never trust IDs blindly |
 | marvy ppl ≈ base ppl | adapter not applied / wrong checkpoint | confirm `--adapter-path` points at the trained adapter (iter-150) |
+marvy-1-14B is a first-draft assistant. All output must be reviewed by a qualified
 ServiceNow consultant before client delivery or production configuration.