license, language, tags, pipeline_tag, base_model
| license | language | tags | pipeline_tag | base_model | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| apache-2.0 |
|
|
text-generation | muthugsubramanian/DocWain-14B-v2 |
DocWain-14B-v2-unified (FP16)
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: FP16
- 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.