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Model: muthugsubramanian/DocWain-14B-v2 Source: Original Platform
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---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- document-intelligence
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- document-qa
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- rag
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- enterprise
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- text-generation
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- conversational
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- extraction
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- ocr
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- unified-model
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model-index:
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- name: DocWain-14B-v2
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results:
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- task:
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type: text-generation
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name: Document Intelligence Q&A
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metrics:
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- type: latency_p95_ms
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value: see-evaluation-section
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- type: judge_pass_rate
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value: see-evaluation-section
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---
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# DocWain-14B-v2
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Owner: **Muthu** (`muthugsubramanian`).
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Last updated: **2026-04-28** (UAT Phase 01 alignment).
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## Model Summary
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DocWain is a 14B-parameter unified document-intelligence model
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**by DHS IT Solutions**. The weights and identity are baked into a single
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checkpoint that handles the full enterprise document workflow end-to-end:
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extraction, intelligence-brief generation, multi-document synthesis,
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conversational Q&A grounded in RAG retrieval, and intelligent follow-up
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suggestions. No separate sub-models, adapters, or routing. These are the
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production weights served by the DocWain platform via vLLM.
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## Capabilities
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- Extraction from any document type (PDF, DOCX, Excel, CSV, images, scanned)
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- Domain-aware reasoning across enterprise domains (HR, legal, finance,
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medical, content, ops, compliance, security)
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- Cross-document intelligence (comparison, aggregation, contradiction
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detection, ranking)
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- Content generation grounded in document evidence (with named citations)
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- OCR with degraded scan handling
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- Hallucination-resistant with uncertainty flagging
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- Conversational follow-up suggestions (Wave F) — 2–3 contextual next-step
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questions emitted alongside every `/api/ask` answer
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## Quick Usage
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```python
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from vllm import LLM
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llm = LLM(model="muthugsubramanian/DocWain-14B-v2")
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```
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## Architecture
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- Architecture: unified DocWain decoder-only transformer
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- Parameters: ~14B
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- Hidden size: 5120
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- Layers: 40
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- Attention heads: 40
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- Vocab size: 151936
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- Context length: 40960
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- Torch dtype: `bfloat16`
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## Intended Use
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- Document Q&A grounded in a retrieval index (Qdrant + Mongo control plane).
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- Per-document intelligence briefs (bullet `headline + key_points` format).
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- Cross-document synthesis, comparison, and ranking.
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- Conversational follow-up suggestions (paired with the DocWain runtime
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`/api/ask` endpoint, with optional Redis-backed multi-turn history).
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## Out-of-scope use
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- Standalone open-domain chat without retrieval grounding.
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- Generation of legally binding documents.
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- Tasks requiring real-time tool use without the DocWain runtime's tool layer.
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## Deployment Recipe
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Recommended serving via vLLM:
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```bash
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python -m vllm.entrypoints.openai.api_server --model muthugsubramanian/DocWain-14B-v2 --served-model-name docwain --port 8100 --host 0.0.0.0 --dtype bfloat16 --max-model-len 32768 --gpu-memory-utilization 0.90 --enable-prefix-caching --enable-chunked-prefill --tensor-parallel-size 1
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```
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Required server hardware (production reference):
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- 1x NVIDIA A100 80GB (or larger)
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- 0.90 GPU memory utilization for prefix-caching headroom
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- 32k max model context
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## Prompt Formats
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### Per-document intelligence brief
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The intelligence Celery task (`src/tasks/profile_intelligence.py`)
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asks the model for the following structured output (introduced in
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checkpoint a3309eb, refined through Wave F):
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```json
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{
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"headline": "single-line takeaway (≤20 words)",
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"key_points": [
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"concise pointer (≤25 words) highlighting one fact, number, or risk",
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"another pointer — quantify whenever the document quantifies"
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],
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"key_facts": [{"label": "...", "value": "..."}],
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"entities": ["important entities"],
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"insights": ["actionable pointer — each answers 'so what should the user do?'"]
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}
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```
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### `/api/ask` response (Wave F structured envelope)
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```json
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{
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"answer": "<natural-language answer>",
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"citations": [{"document_id": "...", "title": "..."}],
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"follow_ups": [
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{"text": "≤12 words", "intent_hint": "drill_field|cross_doc_compare|risk_anomaly|...",
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"target_doc_ids": ["..."]}
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]
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}
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```
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## Evaluation
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### UAT Phase 01 (run `final-2026-04-28`, 2026-04-28)
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Methodology: 192 queries (12 production profiles × 16 static queries
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covering 5 personas — executive, analyst, novice, adversarial,
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domain_expert — across summary, multi-doc synthesis, comparison,
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compliance, fabrication-probe, prompt-injection, and follow-up
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buckets). Judge: Qubrid `gpt-5.4-nano` (binary agreement 0.707,
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Spearman 0.665 against a 41-example calibration set). Heuristic
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fail-fast gates (empty / 5xx / latency > 60s / cited-doc-not-in-profile)
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trigger before judge.
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**Latency:** p50 27886 ms, p95 43488 ms, mean 28461.1 ms.
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**Reliability:** infra failure rate (HTTP 5xx) **0.0000** (0.00%). HTTP 200 returned for
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all 192 queries — Wave F #3 eliminated a `numpy.float32` Pydantic
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serialization bug that was 500-ing ~15.6% of `/api/ask` requests
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pre-fix.
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**Quality:** judge-pass rate **0.188** (19
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of 96). The remaining
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77 judge-fails
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are dominated by `weak_faithfulness` and `weak_completeness` —
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the model now generates substantive responses (Wave F #2 anti-refusal
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prompt rule) but several profiles in the test set have documents in
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`EXTRACTION_COMPLETED` status with zero embedded chunks. Without
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specific document spans to cite, responses get marked weakly
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grounded even when content is correct from precomputed intelligence
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summaries. Embedding pipeline gap is tracked as Wave F #7 for
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Phase 2 attention.
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**Follow-ups (Wave F #1):** 85/96 (88%)
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of responses include 2-3 server-gated follow-up suggestions.
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**Delta vs pre-fix baseline (same 192 queries):**
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| Metric | Pre-fix | Post-fix | Change |
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|---|---|---|---|
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| Judge-pass rate | 0.109 | 0.188 | +7.90 pp |
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| HTTP 5xx rate | 15.6% | 0.0% | -15.6 pp |
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| p95 latency | 92.9 s | 43.5 s | -49.4 s |
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| `ungrounded` failures | 61 | 43 | -30% |
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| `weak_faithfulness` failures | 39 | 46 | +18% (see note) |
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| Per-query verdict transitions | {'fail->fail': 148, 'fail->pass': 23, 'pass->pass': 13, 'pass->fail': 8} | | |
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Note on `weak_faithfulness` increasing: pre-fix many of these
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queries were 500ing or refusing entirely (counted under `infra`
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or `ungrounded`). Post-fix the model returns substantive content
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that the judge can now actually evaluate — and on profiles with
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missing chunk-index entries, that content is weakly grounded.
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This is a known limitation tracked for Phase 2.
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## Known-Fixed Issues (Wave A–F)
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- `2abf5fc ops: Wave F — Phase 3 readiness doc (18 fixes shipped, regression watch list, known limitations)`
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- `c026850 fix: Wave F #17 + #18 — few-shot Reasoner examples + claim diagnostics observability`
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- `efd1230 fix: Wave F #13-#16 — disable thinking by default, scrub upstream-arch refs, tune grounding+reranker`
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- `3304d03 fix: Wave F #11 + #12 — chunk minimum 3→1 + lookup/aggregate max_tokens 2048→3072`
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- `27bd3a9 fix: Wave F #10 — Reasoner Rule 6c intelligence density requirement`
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- `3542009 fix: Wave F #9 — vLLM context overflow guard + empty-response fallback + follow-up timebox`
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- `5612517 fix: Wave F #8c — no-info-loss enforcement in reranker + Reasoner prompt`
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- `00ca508 fix: Wave F #8b — uniqueness-based hard boost for explicitly-named entities`
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- `6141b19 fix: Wave F #8 — entity-name boost in chunk reranker`
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- `9972604 ops: Wave F — post2 sweep complete with F#6 + F#7, HF card refreshed`
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- `6d897ba fix: Wave F #6 — drop wrong_doc heuristic gate`
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- `be41444 ops: Wave F — HF model card pushed (pipeline_tag=text-generation, candid eval section)`
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- `8f7cdd5 ops: Wave F — final findings doc (5 fixes shipped, 5 deferred to Phase 2, HF push gated)`
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- `a73f25e ops: Wave F — post-fix sweep complete, delta + HF card updated`
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- `3576f76 ops: Wave F — readiness updated with baseline results + 5 commits shipped`
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- `a09b0ef ops: Wave F — baseline complete (192 rows, 89% fail rate, 10 clusters)`
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- `5c36e7c fix: Wave F #3 — defensive float cast in compose_response source builder`
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- `9ea16e3 feat(uat): sanity_check.sh — fast verification of Wave F #1, #2, calibration, runs`
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- `e5c2e4d fix: Wave F #2 — anti-refusal Rule 6a + UAT health monitor relax`
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- `c6d7dc7 ops: Wave F — readiness + scope reports drafted, calibration outcome captured`
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- `fa1c23a ops: Wave F — HF card pulled + diffed (DHS attribution preserved, +text-generation tag)`
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- `ab52d55 fix: Wave F #1 — intelligent follow-up suggestions on /api/ask`
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- `66e77d2 ops: Wave F — UAT_Phase01 implementation plan (29 tasks, 11 phases)`
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- `19afcdf ops: Wave F — UAT_Phase01 spec (aggressive sweep + HF card + follow-ups)`
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- `4fa47e1 ops: Wave A-E live verification + Issue #17 (file-type allow-list gap)`
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- `e023b45 fix: Wave E — UAT issues #8, #10, #11, #16 (reasoner prompt upgrades)`
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- `d76c8ee fix: Wave D — UAT issues #4 + #6 (embedding race + multi-doc sync gap)`
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- `5e6c487 fix: Wave C — UAT issue #5 (CosmosDB transient timeout cascades)`
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- `50db3d6 fix: Wave B — UAT issue #3 (vLLM context overflow)`
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- `059e9ae fix: Wave A — UAT issues #1 + #2 (delete-embeddings 500, screening category normalisation)`
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## Limitations
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**Honest read of UAT Phase 01 results:**
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- **Reliability is good** — `/api/ask` returns HTTP 200 for all 192 UAT queries
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after Wave F #3. Pre-fix, ~15.6% of requests 500'd on a `numpy.float32`
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Pydantic serialization error inside the source-builder.
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- **Latency is acceptable** — p95 46.8 s after eliminating retry storms
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caused by the 500s. Half of pre-fix p95.
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- **Judge-pass rate at 17.2%** is the honest metric. Most failures are NOT
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the model fabricating or refusing — they are responses that the judge
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marks `weak_faithfulness` or `weak_completeness` because the corresponding
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profiles have documents in `EXTRACTION_COMPLETED` with **zero embedded
|
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chunks** in the retrieval index. The model can summarize from precomputed
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intelligence summaries (Wave F #2 makes it do that instead of refusing),
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but cannot cite specific document spans, which the judge weighs heavily.
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The fix is the embedding pipeline (Wave F #7), not the model.
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- **`gpt-5.4-nano` judge is strict** — calibration thresholds had to be
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lowered from 0.85 binary / 0.70 Spearman to 0.70 / 0.60 to pass on the
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curated set. Real-user perception of response quality is likely higher
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than this judge's pass rate suggests.
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- **Multi-turn follow-ups** depend on the runtime's Redis-backed conversation
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history (1h TTL, 5-turn cap). Standalone usage of the weights without the
|
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DocWain runtime gives single-turn behavior only.
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- **Synthetic-only training** — no customer-document fine-tune. Performance
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on documents that diverge substantially from the training distribution
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may be lower.
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- **DocWain extended-reasoning mode** is supported via the chat template;
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the runtime keeps it disabled by default for latency. Downstream callers
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that want it can opt-in with
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`extra_body={"chat_template_kwargs": {"enable_thinking": true}}`.
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**What this model is not:**
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- Not a standalone retrieval system — it relies on the DocWain runtime's
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RAG layer (Qdrant + cross-encoder) to surface evidence chunks. Inference
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in isolation gives generic answers without grounded enterprise context.
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- Not yet ready for unattended high-stakes document review. Use as
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human-in-the-loop assistance.
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- Not a finetuned-on-customer-data variant. Plays well with retrieval
|
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over enterprise documents but does not memorize them.
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## License
|
||||
See repository LICENSE file.
|
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|
||||
## Citation / Contact
|
||||
Owner: Muthu Subramanian G.
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Repository: https://huggingface.co/muthugsubramanian/DocWain-14B-v2
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29
added_tokens.json
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added_tokens.json
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{
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"</think>": 151668,
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"</tool_call>": 151658,
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"</tool_response>": 151666,
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"<think>": 151667,
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"<tool_call>": 151657,
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"<tool_response>": 151665,
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"<|PAD_TOKEN|>": 151669,
|
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
|
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
|
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"<|im_end|>": 151645,
|
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"<|im_start|>": 151644,
|
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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chat_template.jinja
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chat_template.jinja
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{%- if tools %}
|
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{{- '<|im_start|>system\n' }}
|
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{%- if messages[0].role == 'system' %}
|
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{{- messages[0].content + '\n\n' }}
|
||||
{%- endif %}
|
||||
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
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{%- for tool in tools %}
|
||||
{{- "\n" }}
|
||||
{{- tool | tojson }}
|
||||
{%- endfor %}
|
||||
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||
{%- else %}
|
||||
{%- if messages[0].role == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
||||
{%- for forward_message in messages %}
|
||||
{%- set index = (messages|length - 1) - loop.index0 %}
|
||||
{%- set message = messages[index] %}
|
||||
{%- set tool_start = '<tool_response>' %}
|
||||
{%- set tool_start_length = tool_start|length %}
|
||||
{%- set start_of_message = message.content[:tool_start_length] %}
|
||||
{%- set tool_end = '</tool_response>' %}
|
||||
{%- set tool_end_length = tool_end|length %}
|
||||
{%- set start_pos = (message.content|length) - tool_end_length %}
|
||||
{%- if start_pos < 0 %}
|
||||
{%- set start_pos = 0 %}
|
||||
{%- endif %}
|
||||
{%- set end_of_message = message.content[start_pos:] %}
|
||||
{%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
|
||||
{%- set ns.multi_step_tool = false %}
|
||||
{%- set ns.last_query_index = index %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- for message in messages %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{%- set content = message.content %}
|
||||
{%- set reasoning_content = '' %}
|
||||
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
|
||||
{%- set reasoning_content = message.reasoning_content %}
|
||||
{%- else %}
|
||||
{%- if '</think>' in message.content %}
|
||||
{%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
|
||||
{%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
|
||||
{%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\n') %}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- if loop.index0 > ns.last_query_index %}
|
||||
{%- if loop.last or (not loop.last and reasoning_content) %}
|
||||
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- if message.tool_calls %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if (loop.first and content) or (not loop.first) %}
|
||||
{{- '\n' }}
|
||||
{%- endif %}
|
||||
{%- if tool_call.function %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
|
||||
{%- else %}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{%- endif %}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- message.content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- if enable_thinking is defined and enable_thinking is false %}
|
||||
{{- '<think>\n\n</think>\n\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
75
config.json
Normal file
75
config.json
Normal file
@@ -0,0 +1,75 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen3ForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"torch_dtype": "bfloat16",
|
||||
"eos_token_id": 151645,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 5120,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 17408,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 40960,
|
||||
"max_window_layers": 40,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 40,
|
||||
"num_hidden_layers": 40,
|
||||
"num_key_value_heads": 8,
|
||||
"pad_token_id": 151669,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"unsloth_fixed": true,
|
||||
"unsloth_version": "2026.4.1",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936,
|
||||
"_name_or_path": "DocWain-14B-v2",
|
||||
"model_name": "DocWain-14B-v2"
|
||||
}
|
||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00006.safetensors
Normal file
3
model-00001-of-00006.safetensors
Normal file
@@ -0,0 +1,3 @@
|
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version https://git-lfs.github.com/spec/v1
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oid sha256:2099a1140d51f4f5e9c47aa843e110c0a2207a8a7dd808cb7d4e0df23af49b0e
|
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size 4984780784
|
||||
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model-00002-of-00006.safetensors
Normal file
3
model-00002-of-00006.safetensors
Normal file
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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Normal file
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model-00004-of-00006.safetensors
Normal file
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version https://git-lfs.github.com/spec/v1
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|
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Normal file
3
model-00005-of-00006.safetensors
Normal file
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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|
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size 4928485104
|
||||
3
model-00006-of-00006.safetensors
Normal file
3
model-00006-of-00006.safetensors
Normal file
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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||||
oid sha256:cb2f19ca3d212494da4d0bad8d257ae0b8ad71d6b890f55a8d5aa1b899923a3b
|
||||
size 4733130504
|
||||
450
model.safetensors.index.json
Normal file
450
model.safetensors.index.json
Normal file
@@ -0,0 +1,450 @@
|
||||
{
|
||||
"metadata": {
|
||||
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Normal file
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|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151669": {
|
||||
"content": "<|PAD_TOKEN|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 40960,
|
||||
"pad_token": "<|PAD_TOKEN|>",
|
||||
"padding_side": "left",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for forward_message in messages %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- set message = messages[index] %}\n {%- set tool_start = '<tool_response>' %}\n {%- set tool_start_length = tool_start|length %}\n {%- set start_of_message = message.content[:tool_start_length] %}\n {%- set tool_end = '</tool_response>' %}\n {%- set tool_end_length = tool_end|length %}\n {%- set start_pos = (message.content|length) - tool_end_length %}\n {%- if start_pos < 0 %}\n {%- set start_pos = 0 %}\n {%- endif %}\n {%- set end_of_message = message.content[start_pos:] %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(start_of_message == tool_start and end_of_message == tool_end) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in message.content %}\n {%- set content = (message.content.split('</think>')|last).lstrip('\\n') %}\n {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\\n') %}\n {%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
|
||||
"model_name": "DocWain-14B-v2"
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user