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README.md
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title: MainStack
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emoji: π οΈ
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# MainStack
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**Specializing in ServiceNow Agentic Delivery.**
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MainStack builds AI systems and open models for the full ServiceNow delivery
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lifecycle β from business analysis and solution design through user stories,
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implementation, testing, and validation.
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## π Featured release
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### [marvy-14B](https://huggingface.co/MainStack/marvy-14B) β the first open fine-tuned model for full-cycle ServiceNow delivery
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`marvy-14B` is a fine-tune of [Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct)
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(Apache-2.0) specialized for end-to-end ServiceNow delivery work. It was trained
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on a curated, fully anonymized corpus of real engagement artifacts spanning the
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delivery chain practiced by our agents:
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> business-analyst β solutions-architect β technical-consultant β validation-architect
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It produces implementation-grade first drafts of:
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|---|---|
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| π Business analyses | π Solution Design Documents (SDD) |
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| β
Requirements & acceptance criteria | π User stories |
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| π₯ Stakeholder maps | π§ Implementation plans (tables, plugins, sys_ids) |
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| ποΈ Systems inventories | π§ͺ Test cases |
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| π Validation & gap analysis | π End-to-end delivery chains (story β build β test) |
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**Available formats:**
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- [`MainStack/marvy-14B`](https://huggingface.co/MainStack/marvy-14B) β merged FP16 (Transformers / vLLM / MLX)
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- [`MainStack/marvy-14B-lora`](https://huggingface.co/MainStack/marvy-14B-lora) β LoRA adapter (~175 MB)
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- [`MainStack/marvy-14B-GGUF`](https://huggingface.co/MainStack/marvy-14B-GGUF) β Q4_K_M & Q8_0 quants for Ollama / llama.cpp / LM Studio
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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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## π Built responsibly
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Our training corpus is rigorously anonymized: customer/partner names aliased,
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emails/hostnames/IPs scrubbed, credential files excluded, and a leakage scanner
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that asserts **zero** residual PII in training content.
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## π About
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MainStack β [mainstack.co.uk](https://www.mainstack.co.uk/) Β· [LinkedIn](https://linkedin.com/company/mainstack-it)
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*Released under Apache-2.0. Built with Qwen.*
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