--- license: apache-2.0 language: - en tags: - document-intelligence - rag - extraction - enterprise - docwain pipeline_tag: text-generation base_model: muthugsubramanian/DocWain-14B-v2-unified --- # DocWain-14B-v2-unified-dpo (GGUF Q5_K_M and Q4_K_M — DPO refinement, for Ollama / llama.cpp / 16GB GPU) DocWain is an **enterprise document intelligence agent** built for extraction, analysis, comparison, and grounded response generation over user-uploaded document profiles. This **unified** variant has identity, capability awareness, and behavioural discipline (verbatim quoting, refusal on missing data, currency preservation, anti-tailoring) baked into the weights via a focused LoRA SFT finetune on synthetic data. ## What's in this release - **Format:** GGUF Q5_K_M and Q4_K_M — DPO refinement, for Ollama / llama.cpp / 16GB GPU - **Base model:** muthugsubramanian/DocWain-14B-v2 (vision-grafted Qwen3-14B) - **Identity:** baked-in — model self-identifies as DocWain regardless of system prompt - **Behaviour:** trained to quote verbatim from evidence, say "not specified in the documents" rather than fabricate, preserve currency symbols (₹/£/$), and refuse to add skills/education/experience that aren't in the source ## Capabilities - Accurate extraction from invoices, contracts, resumes, policies, research papers, and other enterprise document types - Document intelligence — summaries, key findings, cross-document relationships, anomaly surfacing - Layout and context understanding — tables, charts, multi-page references - Grounded response generation with verbatim quoting and explicit "not specified" handling - Document generation — structured reports, comparison tables, executive briefs derived from the user's documents ## Training data Synthetic-only per project policy. The training corpus contains: - Identity / persona examples (no customer data) - Capability awareness Q&A - Synthetic invoices / contracts / resumes / research-paper snippets paired with ideal grounded responses - Domain-mismatch refusal examples - General-instruction mix-in to preserve breadth No customer documents, no scraped private data. ## Recommended runtime | Variant | Runtime | GPU floor | |---------|---------|-----------| | FP16 | vLLM, transformers | A100 80GB | | AWQ INT4 | vLLM `--quantization compressed-tensors` | 16GB+ | | GGUF Q5_K_M | Ollama / llama.cpp | 16GB GPU or CPU | | GGUF Q4_K_M | Ollama / llama.cpp | 12GB GPU or CPU | ## Prompting A short system prompt is enough at runtime — identity is in the weights: ``` You are DocWain — an enterprise document intelligence agent. ``` For full behaviour (RAG-aware, currency-preserving, anti-tailoring), provide your standard DocWain system prompt; the model will respect both its baked-in identity and the prompt-specified rules.
