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

Libraries

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

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MainStack/marvy-1-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MainStack/marvy-1-14B")
model = AutoModelForCausalLM.from_pretrained("MainStack/marvy-1-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

MLX

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

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("MainStack/marvy-1-14B")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Inference
Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

vLLM

How to use MainStack/marvy-1-14B 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "MainStack/marvy-1-14B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MainStack/marvy-1-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "MainStack/marvy-1-14B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MainStack/marvy-1-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "MainStack/marvy-1-14B"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "MainStack/marvy-1-14B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "MainStack/marvy-1-14B"

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

Run Hermes

hermes

MLX LM

How to use MainStack/marvy-1-14B with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "MainStack/marvy-1-14B"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "MainStack/marvy-1-14B"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "MainStack/marvy-1-14B",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use MainStack/marvy-1-14B with Docker Model Runner:
```
docker model run hf.co/MainStack/marvy-1-14B
```

marvy-1-14B / USAGE.md

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	# Using marvy-1-14B

	marvy-1-14B is a ServiceNow delivery specialist. This guide covers every common
	way to run it — cloud or fully local — plus how to wire it into OpenCode.

	- [Choosing a format](#choosing-a-format)
	- [Recommended system prompt & settings](#recommended-system-prompt--settings)
	- [Transformers (PyTorch)](#transformers-pytorch)
	- [vLLM (OpenAI-compatible server)](#vllm-openai-compatible-server)
	- [MLX (Apple Silicon, native)](#mlx-apple-silicon-native)
	- [LM Studio (GUI + local server)](#lm-studio-gui--local-server)
	- [Ollama / llama.cpp (GGUF)](#ollama--llamacpp-gguf)
	- [LoRA adapter (apply on the base)](#lora-adapter-apply-on-the-base)
	- [Use marvy-1-14B in OpenCode](#use-marvy-14b-in-opencode)
	- [Prompt recipes per task](#prompt-recipes-per-task)

	---

	## Choosing a format

	\| You want… \| Use \| Repo \|
	\|---\|---\|---\|
	\| Max quality, GPU/server \| Merged FP16 \| `MainStack/marvy-1-14B` \|
	\| Apple Silicon, native speed \| Merged (MLX) \| `MainStack/marvy-1-14B` \|
	\| Laptop / CPU / Ollama / LM Studio \| GGUF (Q4_K_M or Q8_0) \| `MainStack/marvy-1-14B-GGUF` \|
	\| Smallest download, compose yourself \| LoRA adapter (~175 MB) \| `MainStack/marvy-1-14B-lora` \|

	---

	## Recommended system prompt & settings

	Always lead with the delivery-consultant system prompt — marvy was trained with it:

	```
	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.
	```

	\| Use case \| temperature \| top_p \| max_tokens \|
	\|---\|---\|---\|---\|
	\| Structured artifacts (SDD, stories, test cases) \| 0.3 – 0.5 \| 0.9 \| 1024 – 4096 \|
	\| Exploratory brainstorming \| 0.7 – 0.9 \| 0.95 \| 1024 \|
	\| Validation / critique \| 0.2 – 0.4 \| 0.9 \| 1024 – 2048 \|

	---

	## Transformers (PyTorch)

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM

	model_id = "MainStack/marvy-1-14B"
	tok = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

	SYSTEM = "You are a senior ServiceNow delivery consultant. ..." # full prompt above
	messages = [
	{"role": "system", "content": SYSTEM},
	{"role": "user", "content": "Write a user story with acceptance criteria for P1 SLA escalation."},
	]
	inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
	out = model.generate(inputs, max_new_tokens=1024, temperature=0.4, top_p=0.9)
	print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
	```

	## vLLM (OpenAI-compatible server)

	```bash
	pip install vllm
	vllm serve MainStack/marvy-1-14B --served-model-name marvy-1-14B
	```

	```bash
	curl -s http://localhost:8000/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. ..."},
	{"role":"user","content":"Draft the Incident Management section of an SDD."}
	]}'
	```

	## MLX (Apple Silicon, native)

	```bash
	pip install mlx-lm

	# one-off
	python -m mlx_lm generate --model MainStack/marvy-1-14B \
	--system-prompt "You are a senior ServiceNow delivery consultant. ..." \
	--prompt "Write test cases for a Major Incident workflow." --max-tokens 1024 --temp 0.4

	# OpenAI-compatible server
	python -m mlx_lm server --model MainStack/marvy-1-14B --port 8080
	```

	## LM Studio (GUI + local server)

	1. Install the model — either search `MainStack/marvy-1-14B-GGUF` in the
	in-app model browser, or place a local copy under
	`~/.lmstudio/models/MainStack/marvy-1-14B/` (MLX or GGUF layout).
	2. Load it from the GUI, or:
	```bash
	lms load MainStack/marvy-1-14B
	lms server start # OpenAI-compatible on http://localhost:1234/v1
	```
	3. In the Chat tab, set the system prompt (above) and temperature ~0.4.

	## Ollama / llama.cpp (GGUF)

	```bash
	# Ollama — pull straight from the Hub
	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 user story with acceptance criteria for P1 SLA escalation." --temp 0.4
	```

	\| Quant \| Size \| Use when \|
	\|---\|---\|---\|
	\| `Q4_K_M` \| ~9 GB \| Default — best size/quality balance \|
	\| `Q8_0` \| ~16 GB \| Highest fidelity, near-FP16 \|

	## LoRA adapter (apply on the base)

	```bash
	# MLX
	python -m mlx_lm generate --model Qwen/Qwen2.5-14B-Instruct \
	--adapter-path . --system-prompt "You are a senior ServiceNow delivery consultant. ..." \
	--prompt "Validate this requirement and list follow-up questions: ..." --max-tokens 1024
	```

	```python
	# PEFT
	from peft import PeftModel
	from transformers import AutoModelForCausalLM, AutoTokenizer
	base = "Qwen/Qwen2.5-14B-Instruct"
	model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="auto", device_map="auto")
	model = PeftModel.from_pretrained(model, "MainStack/marvy-1-14B-lora")
	```

	---

	## Use marvy-1-14B in OpenCode

	marvy runs behind any OpenAI-compatible endpoint (LM Studio, mlx_lm server,
	vLLM). Register it as a custom provider in `opencode.json`.

	1. Start a local server (LM Studio shown; adjust port for others):
	```bash
	lms load MainStack/marvy-1-14B && lms server start # http://localhost:1234/v1
	```
	2. Add the provider to your project `opencode.json` (or global
	`~/.config/opencode/opencode.json`):
	```jsonc
	{
	"provider": {
	"lmstudio": {
	"npm": "@ai-sdk/openai-compatible",
	"name": "LM Studio (local)",
	"options": { "baseURL": "http://localhost:1234/v1" },
	"models": {
	"marvy-1-14B": { "name": "marvy-1-14B (ServiceNow delivery)" }
	}
	}
	}
	}
	```
	3. Select `lmstudio/marvy-1-14B` in the OpenCode model picker.

	> marvy-1-14B is a drafting specialist, not a tool-use/agentic fine-tune. It excels
	> at producing delivery artifacts inside chat; for MCP tool-calling agent loops,
	> keep a frontier model as the orchestrator and switch to marvy for drafting.

	---

	## Prompt recipes per task

	\| Task \| Prompt skeleton \|
	\|---\|---\|
	\| Business analysis \| "Produce a Business Analysis for the following engagement: <context>. Cover organization, IT landscape, scope, and risks." \|
	\| Requirements \| "Extract structured requirements (id, category, requirement, priority, target_phase, success_metric) from: <notes>." \|
	\| Stakeholders \| "Build a stakeholder register (role, name, interest, influence, RACI) for: <context>." \|
	\| SDD section \| "Write the <section> section of a Solution Design Document for a ServiceNow <module> implementation. Include design decisions and concrete tables/plugins." \|
	\| User story \| "Write a ServiceNow user story with acceptance criteria for: <capability>." \|
	\| Implementation plan \| "Given this story, describe the implementation: tables, plugins, configuration, records touched, manual follow-ups. Story: <story>." \|
	\| Test case \| "Write a test case (pre-conditions, steps, expected results, pass/fail) for the story: <story>." \|
	\| Validation \| "Validate this artifact against ServiceNow best practice and the SOW. List gaps, risks, and follow-up questions. Artifact: <artifact>." \|