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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

marvy-1-14B

The first open, fine-tuned LLM for the full ServiceNow delivery lifecycle — from business analysis to validation.

marvy-1-14B is an open-source language model fine-tuned for the complete ServiceNow delivery lifecycle: business analysis, requirements, stakeholder mapping, systems inventory, Solution Design Documents, user stories with acceptance criteria, implementation planning, test cases, and validation. Where general-purpose models treat ServiceNow as one topic among many, marvy is built to draft the actual artifacts a delivery team produces — in the structure and sequence real engagements follow. It is a first-draft specialist, not a consultant replacement, and it is not an agentic or tool-use fine-tune.

It was built by MainStack, a consultancy specializing in ServiceNow Agentic Delivery. marvy is a LoRA SFT fine-tune of Qwen2.5-14B-Instruct (Apache-2.0), trained on ~~1,958 anonymized artifacts from real engagements (~~887k tokens), rigorously redacted to zero residual PII per an automated leakage scanner. Its test perplexity of 13.107 was measured on a project- and customer-disjoint held-out split — the model generalizes to unseen work rather than memorizing the training set.

Released under Apache-2.0. Built with Qwen — see NOTICE.

Why marvy-1-14B

Drafts the full lifecycle, not just snippets. Business analysis through validation — the artifacts and sequence real delivery teams actually work in.
OOTB-first and implementation-grade. Tuned to favor out-of-the-box correctness and produce drafts you can review, not rewrite.
Runs locally and privately. Merged FP16, a LoRA adapter, and GGUF quants — run it on Apple Silicon via LM Studio or Ollama, with your engagement data never leaving your machine.
Trained on real, anonymized delivery work. ~~1,958 redacted engagement artifacts (~~887k tokens), with zero residual PII verified by an automated leakage scanner.
Open and Apache-2.0. Built on Qwen2.5-14B-Instruct — inspect it, fine-tune it, and deploy it on your own terms.

📖 Full docs: USAGE.md (every runtime + OpenCode wiring) · VALIDATION.md (prove the fine-tune works) · validate.sh (one-command probe harness)

Quick start

Transformers

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. 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."
)

messages = [
  {"role": "system", "content": SYSTEM},
  {"role": "user", "content": "Write a ServiceNow user story with acceptance criteria for SLA escalation on P1 incidents."},
]
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)
print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

vLLM

pip install vllm
vllm serve MainStack/marvy-1-14B

Ollama (via GGUF)

Use the companion repo MainStack/marvy-1-14B-GGUF:

ollama run hf.co/MainStack/marvy-1-14B-GGUF:Q4_K_M

MLX (Apple Silicon native)

pip install mlx-lm
python -m mlx_lm generate --model MainStack/marvy-1-14B \
  --system-prompt "You are a senior ServiceNow delivery consultant..." \
  --prompt "Draft the Platform Architecture section of an ITSM SDD." \
  --max-tokens 1024 --temp 0.4

LoRA-only (apply on top of the base)

If you prefer a tiny adapter (~175 MB) on top of the BF16 base, see MainStack/marvy-1-14B-lora.

Intended use

marvy-1-14B is designed to produce implementation-grade first drafts across the ServiceNow delivery lifecycle — accelerating the artifacts a practitioner would otherwise write from scratch, then review and refine. Built for solution architects, business analysts, technical consultants, and project managers. Typical tasks:

Task family	What it produces
`business_analysis`	Structured BA reports from SOWs / discovery notes
`requirements_extraction`	Functional/non-functional requirements with acceptance bullets
`stakeholder_mapping`	RACI / influence-interest grids from raw notes
`systems_inventory`	CMDB-shaped systems inventories from architecture inputs
`sdd_design`	Solution Design Document sections (architecture, integrations, data model)
`story_authoring`	User stories with crisp acceptance criteria
`implementation_planning`	Story-level implementation plans citing tables/plugins
`test_case_generation`	Test cases per story, mapped to acceptance criteria
`validation_critique`	Gap analysis, follow-up questions, assumption checks against source docs
`delivery_chain`	Multi-turn: story → implementation → test, end-to-end

Recommended system prompt

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.

Recommended generation settings

Use case	temperature	top_p	max_new_tokens
Structured artifacts (SDD, stories)	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

Training data

The training dataset is proprietary to MainStack and is not publicly released. It is derived from confidential, anonymized client engagement artifacts. The statistics below describe the corpus for transparency; the data itself is not distributed with the model.

Item	Value
Source	Anonymized real engagement artifacts (`.md`, `.csv`, `.json`, `.mmd`, `.txt`)
Availability	Proprietary — not released
Total records	1,958 (after schema + exact-dedupe)
Estimated tokens	~887k
Splits (project-disjoint)	train 1,359 · val 347 · test 252
Tasks	11 task families (see table above)
Multi-turn share	`delivery_chain` (158 records) — story→implementation→test

Privacy & redaction

All customer/partner names → stable aliases (e.g. Customer-FIN-03, Customer-ENERGY-01).
Emails → user@example.com; hostnames → instance.example.service-now.com; IPs → RFC 5737 range; key: value secrets → [REDACTED].
Credential/login/VPN files excluded entirely; bulk CMDB dumps >1.5 MB excluded.
ServiceNow sys_ids and table/plugin names preserved (instance-local, technically valuable, low risk).
A leakage scanner asserts 0 residual emails, hostnames, or mapped real names in message content.

Split integrity

Train / val / test are split by project, so no customer appears in more than one split. The largest project is forced into train to keep eval honest:

val projects: Customer-ENERGY-01
test projects: Customer-CHEM-01, Customer-FININST-01

Training procedure

Setting	Value
Method	LoRA SFT (QLoRA-style: LoRA on 4-bit base)
Base model	`mlx-community/Qwen2.5-14B-Instruct-4bit` (training) → fused onto `Qwen/Qwen2.5-14B-Instruct` BF16 (release)
Framework	MLX-LM 0.31.3
Hardware	Apple Silicon (M-series), Metal
Max sequence length	8,192
Batch size / grad accum	1 / 16 (effective batch 16)
Iterations	350 (~4 epochs over 1,359 train records)
Optimizer	AdamW, cosine decay, warmup 20, lr 1e-4 → 1e-6
LoRA rank / scale / dropout	32 / 20.0 / 0.0
LoRA target keys	`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
Adapted layers	top 16 transformer layers
Prompt masking	yes — loss computed only on assistant turns
Seed	42

Evaluation

Fine-tuned vs. base — efficiency on the held-out test set

The cleanest measure of the fine-tune's value is to score the same base model twice — plain vs. with the marvy adapter — on the project-disjoint test split (252 records from two customers never seen in training/val), using per-token cross-entropy/perplexity on the assistant tokens only (prompt-masked, the same objective used in training). Lower perplexity = the model assigns higher probability to the real, human-authored delivery artifact.

Overall: perplexity 8.91 → 6.03, a 32.3% reduction on unseen customers.

Task	Base ppl	marvy-1-14B ppl	Improvement
Systems inventory	77.07	10.53	−86.3%
Requirements extraction	46.76	9.39	−79.9%
Stakeholder mapping	27.81	6.91	−75.2%
Story authoring	15.38	7.86	−48.9%
Validation / critique	9.72	8.23	−15.3%
Business analysis	7.14	6.66	−6.6%
SDD design	4.48	4.40	−1.7%
Overall	8.91	6.03	−32.3%

The gains are largest on structured, format-heavy artifacts (inventories, requirements, stakeholder registers, stories) where the base model wanders from the expected schema; they are smaller on long-form prose (SDD sections, business analysis) where the base was already competent. This is the honest, expected shape of a domain SFT.

Notes: the test customers (Customer-CHEM-01, Customer-FININST-01) appear in neither train nor val, so this reflects generalization, not memorization. The test split happens to cover 7 of the 11 task families. An earlier MLX batch-eval reported aggregate ppl ≈ 13.1 with 2,048-token truncation; the figures above recompute per-task with full assistant-token masking, so the base-vs-marvy delta is the result of interest.

Reproduce it yourself: bash benchmark/run_benchmark.sh (see VALIDATION.md for qualitative probes too).

Limitations & known issues

Text-only sources. SOWs/SDDs/workbooks in .docx/.pptx/.pdf/.xlsx are not parsed in this build. Coverage of binary-only engagements is therefore thin.
Project concentration. ~95% of records come from ~12 data-rich projects; the long tail contributes a single case study each. Some task families (e.g. case_study, validation_critique) are smaller and may exhibit higher variance.
Synthetic instructions. User prompts are templated paraphrases (3–5 variants per task); assistant outputs are the original human-authored artifacts.
English-only. The corpus is English.
Not a replacement for a consultant. Output is first-draft, implementation-grade content that requires expert review before client delivery or production use.
No tool use / function calling fine-tune. marvy-1-14B is a text-completion specialist; agentic tool use is left to the orchestrator.
Hallucination risk on instance-specific facts. The model will confidently invent sys_ids, plugin IDs, and table fields if asked about specifics it has not seen. Always verify against an actual ServiceNow instance.
No safety fine-tune beyond the base. Inherits Qwen2.5-14B-Instruct safety behavior; no additional RLHF.

License

marvy-1-14B is dual-licensed — see LICENSING.md for the full breakdown:

Component	License
Model weights (safetensors / GGUF / LoRA)	Apache-2.0 (`LICENSE`) — inherited from the Qwen2.5-14B-Instruct base; free to use, fine-tune, and redistribute, with `NOTICE` retained.
MainStack contributions (model cards, docs, benchmark, charts, training methodology)	CC-BY-4.0 (`LICENSE-CC-BY-4.0`) — reuse requires attribution to MainStack.

The model weights are a derivative of Qwen2.5-14B-Instruct (Apache-2.0). Per Apache-2.0, the weights cannot be placed under a more restrictive license; MainStack's protection is the CC-BY-4.0 license on our own authored materials plus the mandatory NOTICE retention. See NOTICE for attribution.

Attribution

marvy-1-14B is free to use, fine-tune, and redistribute under Apache-2.0. 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 back to this model:

Built on / evaluated against marvy-1-14B by MainStack — https://huggingface.co/MainStack/marvy-1-14B

Concretely, we ask that derivatives and comparisons:

keep the NOTICE file intact (this is required by Apache-2.0 §4),
name MainStack/marvy-1-14B in the model card, paper, or README, and
cite the entry below.

Per Apache-2.0, you must also continue to attribute the upstream base model (Qwen2.5-14B-Instruct) — see NOTICE.

Citation

If you use marvy-1-14B (as a baseline, a starting point, or in evaluation), please cite:

@software{marvy_1_14b_2026,
  title     = {marvy-1-14B: An open fine-tuned model for the full ServiceNow delivery lifecycle},
  author    = {MainStack},
  year      = {2026},
  publisher = {Hugging Face},
  url        = {https://huggingface.co/MainStack/marvy-1-14B},
  note      = {Fine-tune of Qwen2.5-14B-Instruct},
  license   = {Apache-2.0}
}

@misc{qwen2.5,
  title  = {Qwen2.5: A Party of Foundation Models},
  author = {Qwen Team},
  year   = {2024},
  url    = {https://qwenlm.github.io/blog/qwen2.5/}
}

@software{marvy_14b_2026,
  title  = {marvy-1-14B: A ServiceNow delivery lifecycle fine-tune of Qwen2.5-14B-Instruct},
  author = {MainStack},
  year   = {2026},
  url    = {https://huggingface.co/MainStack/marvy-1-14B},
  license= {Apache-2.0}
}

@misc{qwen2.5,
  title  = {Qwen2.5: A Party of Foundation Models},
  author = {Qwen Team},
  year   = {2024},
  url    = {https://qwenlm.github.io/blog/qwen2.5/}
}

Acknowledgements

Qwen team at Alibaba Cloud for the Qwen2.5 family.
Apple MLX team for mlx and mlx-lm, enabling native Apple Silicon training.
Hugging Face for hosting and the surrounding ecosystem.

Downloads last month: 110

Safetensors

Model size

15B params

Tensor type

BF16

MLX

Hardware compatibility

Quantized

Model tree for MainStack/marvy-1-14B

Base model

Qwen/Qwen2.5-14B

Finetuned

Qwen/Qwen2.5-14B-Instruct

Finetuned

(407)

this model

Quantizations

1 model

Evaluation results

Test perplexity on ServiceNow Delivery SFT (project-disjoint test split)
self-reported

13.107
Test cross-entropy loss on ServiceNow Delivery SFT (project-disjoint test split)
self-reported

2.573