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", device_map="auto")
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)

Notebooks
Google Colab
Kaggle
Local Apps Settings
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

OpenClaw new

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

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 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" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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 / 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.