Why marvy-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)

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Quick start

Transformers

python
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from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "MainStack/marvy-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

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

Ollama (via GGUF)

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

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

MLX (Apple Silicon native)

bash
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pip install mlx-lm
python -m mlx_lm generate --model MainStack/marvy-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-14B-lora.

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Intended use

marvy-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:

Recommended system prompt

markdown
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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

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Training data

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

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Training procedure

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Evaluation

Test-set evaluation on the project-disjoint test split (252 records from two customers never seen in training/val), 50 batches:

Note: two test sequences exceed 2,048 tokens and are truncated by the MLX eval harness. The reported figure is therefore a slight upper bound on true loss. Full-length scoring is planned for v2.

To reproduce or validate these results yourself — including a base-vs-marvy comparison and qualitative task probes — see VALIDATION.md and run validate.sh.

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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-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.

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License

Released under the Apache License 2.0 (see LICENSE).

This model is a derivative of Qwen2.5-14B-Instruct (Apache-2.0). See NOTICE for attribution.

Citation

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