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 / LICENSING.md

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	# Licensing — marvy-1-14B

	marvy-1-14B uses a layered (dual) license that reflects what is built on top
	of an upstream open model versus what MainStack authored.

	\| Component \| License \| What it covers \|
	\|---\|---\|---\|
	\| Model weights (`.safetensors`, GGUF quants, LoRA adapter) \| Apache-2.0* \| The fine-tuned weights. These are a derivative of Qwen2.5-14B-Instruct (Apache-2.0); per that license they remain Apache-2.0 and free to use, modify, and redistribute. \|
	\| MainStack original contributions \| CC-BY-4.0 \| The model cards, documentation (`USAGE.md`, `VALIDATION.md`, benchmark), the benchmark charts, the curated training-data methodology, and the pipeline framing authored by MainStack. \|

	## What this means in practice

	### You may (under Apache-2.0, for the weights)
	- Use marvy-1-14B commercially, privately, or in research.
	- Fine-tune, distill, quantize, merge, or otherwise build on the weights.
	- Redistribute the weights, including modified versions.

	…provided you retain the `NOTICE` file in derivatives and redistributions
	(Apache-2.0 §4(d) — this is mandatory and carries the attribution request).

	### You must (under CC-BY-4.0, for our contributions)
	If you reuse MainStack's documentation, model cards, benchmark, or charts —
	e.g. copying our eval methodology, reproducing our charts, or lifting card text
	into your own model — you must give attribution: credit "MainStack" and link
	to https://huggingface.co/MainStack/marvy-1-14B. This is a binding condition of
	CC-BY-4.0, not just a request.

	### We ask (attribution for the model)
	If you use marvy-1-14B **as a baseline, a starting point for your own fine-tune,
	a distillation source, or an evaluation comparison**, please credit MainStack
	and cite the entry in the model card. See `NOTICE` and the card's Citation
	section.

	## Why the weights can't be more restricted

	Qwen2.5-14B-Instruct is released under Apache-2.0, which grants every recipient
	an irrevocable, royalty-free right to use and redistribute. A fine-tune cannot
	revoke those rights on the resulting weights. MainStack's protection therefore
	lives where it legally can: (1) the CC-BY-4.0 license on our own authored
	materials, and (2) the Apache-2.0 NOTICE that must travel with the weights.

	## Files
	- `LICENSE` — Apache-2.0 (governs the weights; inherited from the base model).
	- `LICENSE-CC-BY-4.0` — CC-BY-4.0 (governs MainStack's documentation and other
	original contributions).
	- `NOTICE` — required attribution notices (retain in derivatives).
	- `CITATION.cff` — citation metadata.