README.md

---
language:
  - en
license: apache-2.0
base_model:
  - Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2
base_model_relation: adapter
datasets:
  - fever/fever
  - hotpotqa/hotpot_qa
library_name: transformers
pipeline_tag: text-generation
tags:
  - reasoning
  - evidence-grounding
  - grounded-qa
  - attribution
  - fever
  - hotpotqa
  - lora
  - peft
  - distillation
  - research
---

# Quotebound 27B

**A 27B LoRA adapter for evidence-faithful reasoning over closed packets of
source text.**

Quotebound 27B is the standalone model release from the
Evidence-Faithful Reasoning project. It is trained to read a bounded evidence
packet, identify the supporting units, copy exact quotes, and abstain with
`Insufficient evidence.` when the packet does not justify an answer.

The project asks a stricter question than "did the model get the answer right?"
It asks whether the answer is recoverably grounded in the supplied text.

![Fresh public holdout: Quotebound 27B versus the prior bridge model](./assets/standalone_holdout_comparison.svg)

*On a fresh 36-task public holdout, Quotebound 27B improves task accuracy,
evidence F1, and quote F1 over the prior bridge model. The largest raw gain is
quote faithfulness: `0.3343` -> `0.6815`.*

## Result snapshot

| Question | Answer |
|---|---|
| What ships here? | A PEFT/LoRA adapter for `Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2`. |
| What changed inside the model? | Raw quote F1 roughly doubled on the fresh public holdout: `0.3343` -> `0.6815`. |
| Best standalone-system row on that holdout | Quotebound + `deterministic_v3`: task `0.8889`, strict `0.5833`, evidence F1 `0.9093`, quote F1 `0.9093`. |
| Output reliability | Zero invalid outputs across every reported evaluation surface. |
| Important boundary | Perfect `probe_v0` belongs to the benchmark-winning hybrid stack, not to this adapter alone. |

## Why this model exists

Reasoning-tuned models can sound structured while grounding badly: they may
answer correctly but cite the wrong evidence, corrupt a quote, or keep going
when the packet is actually insufficient.

Quotebound 27B is trained for a narrower, auditable behavior:

1. choose the smallest sufficient evidence units,
2. quote those units verbatim,
3. answer only from those units,
4. refuse cleanly when the packet runs out.

Correctness alone is not credited. The model is meant for settings where a user
needs the answer and the support to survive inspection together.

## Quick start

Install the usual Transformers + PEFT stack, then load the base model and
attach the adapter:

```bash
pip install -U transformers peft accelerate bitsandbytes
```

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_id = "Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2"
adapter_id = "darcar0/quotebound-27b"

tokenizer = AutoTokenizer.from_pretrained(base_id)
base = AutoModelForCausalLM.from_pretrained(
    base_id,
    device_map="auto",
    torch_dtype="auto",
)
model = PeftModel.from_pretrained(base, adapter_id)
model.eval()
```

The base is a 27B-parameter model. Use the quantization and serving setup your
hardware requires; 4-bit loading with `bitsandbytes` is a practical inference
path on constrained GPUs.

## Model details

| Field | Value |
|---|---|
| Adapter | `darcar0/quotebound-27b` |
| Base model | [`Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2`](https://huggingface.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2) |
| Artifact type | LoRA / PEFT adapter |
| Primary behavior | Closed-packet grounded QA, claim verification, exact quote attribution, and abstention |
| Output style | JSON with answer, evidence IDs, verbatim quotes, and short justification |
| Training sources | Public FEVER-style verify-claim data, public HotpotQA-style grounded-QA data, and project-local packet scaffolding derived from those sources |
| License | Apache 2.0 |

## Prompt contract

The model is trained for an evidence-first prompt that makes the answer
subordinate to the cited text. A minimal version:

```text
You are answering from a bounded evidence packet only.

Work in this order:
1. Identify the smallest set of packet units that matters.
2. Copy exact quote(s) from those units.
3. Only then give the final answer.

Rules:
- No outside facts.
- Return valid JSON only.
- Every quote must be a verbatim substring of the cited unit.
- Do not paraphrase, ellipsize, or stitch quotes.
- If the packet is insufficient, the answer field must be exactly
  "Insufficient evidence."
```

Expected output shape:

```json
{
  "task_id": "<task id>",
  "label": "support|contradict|insufficient|null",
  "answer": "<one-sentence answer>",
  "evidence_ids": ["unit_id_1", "unit_id_2"],
  "quotes": [
    {"unit_id": "unit_id_1", "quote": "<exact quote>"}
  ],
  "justification": "<one short sentence tied to the cited evidence>"
}
```

## Evaluation

### Fresh 36-task mixed public holdout

The main standalone comparison uses a fresh 36-task public holdout: 18 FEVER
verify-claim tasks and 18 HotpotQA grounded-QA tasks. Source rows were
de-duplicated against training, dev, and `probe_v0` rows.

| Stack | Task | Strict | Evidence F1 | Quote F1 |
|---|---:|---:|---:|---:|
| Bridge raw | 0.8611 | 0.2222 | 0.8815 | 0.3343 |
| Quotebound raw | 0.8889 | 0.4444 | 0.9093 | 0.6815 |
| Bridge + `deterministic_v3` | 0.8611 | 0.5833 | 0.8815 | 0.8815 |
| **Quotebound + `deterministic_v3`** | **0.8889** | **0.5833** | **0.9093** | **0.9093** |

How to read this table:

- **Raw rows** measure the model outputs before quote repair.
- **`deterministic_v3` rows** add the packet-local quote normalizer from the
  project repository.
- Quotebound improves task accuracy, evidence F1, and quote F1 in both raw and
  normalized form; it also ties normalized strict success.
- The largest model-side gain is raw quote faithfulness, from `0.3343` to
  `0.6815`.

### Fixed dev triage slice

| Stack | Task | Strict | Evidence F1 | Quote F1 |
|---|---:|---:|---:|---:|
| Quotebound + `deterministic_v3` | 1.0000 | 0.6190 | 0.8320 | 0.7095 |

### Untouched 104-task HotpotQA shadow slice

On a 104-task HotpotQA shadow slice that was never touched during selection,
Quotebound raw improved quote-faithful behavior over the prior bridge model.
Quotebound plus `deterministic_v3` matched bridge plus `deterministic_v3` at
the system level. This surface is reported as a narrative parity result because
the freeze memo does not publish per-metric cells for it.

## Release architecture

The project ends in two finished results that are intentionally reported
separately:

| Result | What it is | What it proves |
|---|---|---|
| **Quotebound 27B** | The downloadable LoRA adapter on this page. | More of the evidence-faithful behavior moved into the model itself, with gains across non-`probe_v0` surfaces. |
| **Benchmark-winning hybrid stack** | A trained bridge checkpoint plus the `deterministic_v3` packet-local quote normalizer. | The full system clears every gate of the strict contract on frozen held-out `probe_v0`. |

These are connected, but they are not the same claim. Quotebound 27B is the
standalone model release. The hybrid stack is the benchmark-facing winner.
Perfect `probe_v0` belongs to the hybrid stack, not to this adapter alone.

## Intended use

Use this release when answers must stay inside a fixed body of supplied text:

- bounded document QA with explicit evidence requirements,
- claim verification over closed packets of source text,
- policy, compliance, contract, and internal-document review where answers
  need source-text support,
- research on evidence-faithful reasoning, quote fidelity, and abstention.

## Limitations

- This is not a general chatbot. Open-domain QA, open chat, and free-form
  generation outside the closed-packet setup are not characterized.
- The downloadable artifact is the LoRA adapter only; the 27B base model is
  required.
- `deterministic_v3` is not shipped as part of this model repo. It is a
  separate packet-local post-processing step in the project repository.
- Perfect `probe_v0` belongs to the benchmark-winning hybrid stack, not to this
  adapter alone.
- Raw item-level contents of the frozen held-out probe are intentionally not
  published; the held-out gate has to stay closed to remain meaningful.
- For high-stakes use, treat the model as an evidence-grounding component that
  still requires human review and application-specific validation.

## Read next

- [Technical note](./technical_note_evidence_faithful_reasoning.md) - full
  method, release boundary, and result discussion.
- [Frozen benchmark progression chart](./assets/benchmark_progression.svg)
- [Fresh holdout comparison chart](./assets/standalone_holdout_comparison.svg)

## Citation

```bibtex
@misc{quotebound_27b_2026,
  title        = {Quotebound 27B: Evidence-Faithful Reasoning Standalone Release},
  author       = {{darcar0}},
  year         = {2026},
  howpublished = {Hugging Face model release},
  url          = {https://huggingface.co/darcar0/quotebound-27b}
}
```

## References

- Base model:
  [Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2](https://huggingface.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2)
- Datasets:
  [fever/fever](https://huggingface.co/datasets/fever/fever),
  [hotpotqa/hotpot_qa](https://huggingface.co/datasets/hotpotqa/hotpot_qa)
- Technical note:
  [technical_note_evidence_faithful_reasoning.md](./technical_note_evidence_faithful_reasoning.md)