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 / VALIDATION.md

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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 quantitative base-vs-marvy comparison on a held-out, customer-disjoint test set.

TL;DR: run bash docs/validate.sh (from the model repo) for the quick path, or follow the manual steps below.

What "working" means here

marvy-1-14B is a specialist drafting model. A successful fine-tune should show:

Format fidelity — it emits the delivery artifact shape on cue (user stories with acceptance criteria, SDD sections, test cases with pre-conditions/steps/expected results) without being told the structure.
Domain voice — OOTB-first framing, ServiceNow tables/plugins, ITIL/CSDM vocabulary, sys_id citations where relevant.
Lower loss than the base on held-out ServiceNow delivery text.

The base model (Qwen2.5-14B-Instruct) is a strong generalist and will produce plausible answers — the point of validation is to show marvy is more on-format, more domain-specific, and lower-perplexity on this task.

Test 1 — 60-second smoke test (qualitative)

Prompt the model with a bare instruction and check it produces a correctly structured artifact with no format coaching.

LM Studio (local)

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."},
      {"role": "user", "content": "Write a user story with acceptance criteria for auto-escalating P1 incidents that breach a 15-minute response SLA."}
    ]
  }' | python3 -c "import sys,json;print(json.load(sys.stdin)['choices'][0]['message']['content'])"

MLX (Apple Silicon)

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

Pass criteria

Output is a user story (As a … I want … so that …) followed by discrete, testable acceptance criteria.
References ServiceNow concretely (e.g. incident, SLA definitions, sla_definition, escalation/notification, assignment groups).
No meta-chatter ("Sure, here is…") dominating the answer; it reads like a backlog item, not a chatbot reply.

Test 2 — Task-coverage probes (qualitative, one per skill)

Run each prompt with the recommended system prompt. Each should yield the artifact named, in the right shape.

#	Prompt	Expect
1	"Draft the Incident Management section of an SDD for a greenfield ITSM implementation. Include assignment rules and SLA design."	SDD section: architecture/process, assignment rules (condition/action/order), SLA table
2	"Extract structured requirements (id, category, priority, target phase, success metric) from: 'We need to replace email-based access requests with a catalog item routed for manager approval.'"	Tabular/structured requirements with priorities & metrics
3	"Write a test case for the story: 'Restrict the Assignment Group field on incidents to groups with the itil role.'"	Test case: pre-conditions, steps, expected results, pass/fail
4	"We are migrating CMDB to CSDM. Produce the foundation-data load sequence and the CI classes involved."	CSDM/CMDB sequence, classes (cmdb_ci_*), foundation order
5	"Validate this requirement against best practice and list follow-up questions: 'All incidents must auto-close after 3 days.'"	Critique + concrete follow-up questions + risks

Pass criteria

At least 4 of 5 produce the correct artifact type with ServiceNow-specific, implementation-grade content (not generic ITSM prose).

Test 3 — Quantitative: base vs marvy on the held-out test set

This is the strongest signal. The test split is customer-disjoint — two customers that never appear in training or validation — so it measures generalization, not memorization.

With the MLX training kit (in the source repo)

cd training

# marvy (fine-tuned adapter on the base)
python -m mlx_lm lora \
  --model mlx-community/Qwen2.5-14B-Instruct-4bit \
  --adapter-path train/adapters \
  --data train/data --test --test-batches 50
# -> Test loss 2.573, Test ppl 13.107   (lower is better)

# base (no adapter) for comparison
python -m mlx_lm lora \
  --model mlx-community/Qwen2.5-14B-Instruct-4bit \
  --data train/data --test --test-batches 50
# -> expect a HIGHER loss/ppl than marvy

Pass criteria

marvy's test perplexity is meaningfully lower than the base on the same held-out split.
No data leakage: the test customers (Customer-CHEM-01, Customer-FININST-01) are absent from train.jsonl / valid.jsonl.

Reference result for this release: test loss 2.573 / ppl 13.107 on 50 batches of the project-disjoint test split (two sequences >2048 tokens are truncated by the eval harness, so this is a slight upper bound).

Interpreting results

Symptom	Likely cause	Action
Generic ITSM prose, no ServiceNow specifics	wrong/short system prompt	use the full recommended system prompt; temp 0.3–0.5
Rambling, no artifact structure	temperature too high	lower to 0.3–0.4
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.