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
```

tgetsov commited on 1 day ago

Commit

1266328

verified ·

1 Parent(s): 1bbfd88

Upload VALIDATION.md with huggingface_hub

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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.