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README.md
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[mainstack.co.uk](https://www.mainstack.co.uk/) · [LinkedIn](https://linkedin.com/company/mainstack-it)
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MainStack is a consultancy specializing in ServiceNow Agentic Delivery. We build the AI that does the drafting work of a delivery engagement — and we release it openly. **marvy-14B** is our first public model: a fine-tuned LLM that drafts artifacts across the entire ServiceNow delivery lifecycle, from business analysis through Solution Design Documents, user stories, test cases, and validation. It's a first-draft specialist built for the people who do the work — solution architects, business analysts, technical consultants, project managers, and delivery leads.
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## 🔒 Built responsibly
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| Repo | Format | Use case |
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| [MainStack/marvy-14B](https://huggingface.co/MainStack/marvy-14B) | Merged FP16 | Full-precision inference and serving |
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| [MainStack/marvy-14B-lora](https://huggingface.co/MainStack/marvy-14B-lora) | LoRA adapter | Compose on top of Qwen2.5-14B-Instruct |
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| [MainStack/marvy-14B-GGUF](https://huggingface.co/MainStack/marvy-14B-GGUF) | GGUF quants | Local runs on Apple Silicon, LM Studio, Ollama |
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```bash
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ollama run hf.co/MainStack/marvy-14B-GGUF:Q4_K_M
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```
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## About
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[mainstack.co.uk](https://www.mainstack.co.uk/) · [LinkedIn](https://linkedin.com/company/mainstack-it)
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MainStack is a consultancy specializing in ServiceNow Agentic Delivery. We build the AI that does the drafting work of a delivery engagement — and we release it openly. **marvy-1-14B** is our first public model: a fine-tuned LLM that drafts artifacts across the entire ServiceNow delivery lifecycle, from business analysis through Solution Design Documents, user stories, test cases, and validation. It's a first-draft specialist built for the people who do the work — solution architects, business analysts, technical consultants, project managers, and delivery leads.
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## 📊 Measured impact
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On a **project- and customer-disjoint** held-out test set, `marvy-1-14B` cuts
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perplexity on real delivery artifacts by **32% overall vs. the unmodified base
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model** — and by **75–86%** on structured artifacts like systems inventories,
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requirements, and stakeholder registers. Same weights, adapter on vs. off; the
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difference is the fine-tune. See the charts on the
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[model card](https://huggingface.co/MainStack/marvy-1-14B#evaluation).
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## 🔒 Built responsibly
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| Repo | Format | Use case |
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|------|--------|----------|
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| [MainStack/marvy-1-14B](https://huggingface.co/MainStack/marvy-1-14B) | Merged FP16 | Full-precision inference and serving |
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| [MainStack/marvy-1-14B-lora](https://huggingface.co/MainStack/marvy-1-14B-lora) | LoRA adapter | Compose on top of Qwen2.5-14B-Instruct |
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| [MainStack/marvy-1-14B-GGUF](https://huggingface.co/MainStack/marvy-1-14B-GGUF) | GGUF quants | Local runs on Apple Silicon, LM Studio, Ollama |
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```bash
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ollama run hf.co/MainStack/marvy-1-14B-GGUF:Q4_K_M
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```
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## About
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