Text Generation
Transformers
Safetensors
PEFT
English
reasoning
evidence-grounding
grounded-qa
attribution
fever
hotpotqa
lora
distillation
research
conversational
Instructions to use darcar0/quotebound-27b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use darcar0/quotebound-27b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="darcar0/quotebound-27b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("darcar0/quotebound-27b", device_map="auto") - PEFT
How to use darcar0/quotebound-27b with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use darcar0/quotebound-27b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "darcar0/quotebound-27b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "darcar0/quotebound-27b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/darcar0/quotebound-27b
- SGLang
How to use darcar0/quotebound-27b with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "darcar0/quotebound-27b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "darcar0/quotebound-27b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "darcar0/quotebound-27b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "darcar0/quotebound-27b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use darcar0/quotebound-27b with Docker Model Runner:
docker model run hf.co/darcar0/quotebound-27b
Upload README.md with huggingface_hub
Browse files
README.md
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# Evidence-Faithful Reasoning
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earn every answer from a bounded evidence packet.**
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a claim.
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Pilot 3 is the strongest standalone model from that project. The same project
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also produced a benchmark-winning hybrid system (bridge `checkpoint-2` plus a
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deterministic packet-local normalizer); **this page is the front door for the
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standalone model.**
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## Resources & Guides
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- [Technical brief (PDF)](./evidence_faithful_reasoning_release_brief.pdf)
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- [Technical note](./technical_note_evidence_faithful_reasoning.md)
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- [Fresh public holdout chart](./standalone_holdout_comparison.svg)
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evidence F1, and quote F1, while tying normalized strict after
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checkpoint to hold up across multiple non-`probe_v0` evaluation
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surfaces.
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- **Roughly doubles raw quote-faithfulness** over the earlier bridge
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model on a fresh public holdout (raw quote F1 `0.3343` → `0.6815`).
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- **Beats bridge on a fresh 36-task mixed public holdout** in task
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accuracy, evidence F1, and quote F1, while tying normalized strict
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grounded success.
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- **Zero invalid outputs** on every reported evaluation surface.
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- LoRA adapter on top of
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[`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).
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## Quick start
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model = PeftModel.from_pretrained(base, adapter_id)
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The base model is 27B parameters
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##
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copy verbatim quotes from those units, and emit a structured JSON
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response. A short version of the recommended prompt:
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```
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You are answering from a bounded evidence packet only.
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benchmark-winning configuration, the JSON output is then passed through a
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deterministic packet-local quote normalizer; *Project context* explains what
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that adds and what it does not.
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## Evaluation results
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### Fresh 36-task mixed public holdout
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A held-out slice of 18 FEVER verify-claim tasks plus 18 HotpotQA grounded
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comparison report.
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| Stack | Task | Strict | Evidence F1 | Quote F1 |
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|---|---:|---:|---:|---:|
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| Bridge + `deterministic_v3` | 0.8611 | 0.5833 | 0.8815 | 0.8815 |
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| **Pilot 3 + `deterministic_v3`** | **0.8889** | **0.5833** | **0.9093** | **0.9093** |
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raw and normalized form, ties normalized
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raw quote F1 at the model level.
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`1.0000`.
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`deterministic_v3` at the system level.
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1. **
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2. **Pilot 3 standalone model** — this Hugging Face release. The
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strongest version of the project's evidence-faithful behavior that
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moved into the model itself, evaluated across multiple non-`probe_v0`
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surfaces.
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The
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## Intended use
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chatbot replacement. It is built for:
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- bounded document QA with explicit evidence requirements,
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- claim verification and grounded QA from
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- policy, compliance, contract, and internal-document workflows where
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- research on evidence-faithful reasoning and abstention behavior.
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The contract is strict by design: correctness alone does not count as
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success. Every answer must come with recoverable support, and the model
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must abstain when the packet does not justify a claim.
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## Training data
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Training and evaluation surfaces are public-data-backed and derived from:
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labels.
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- **HotpotQA** — multi-hop grounded QA over short evidence packets.
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- Project-local bounded packet scaffolding built on top of those
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upstream sources.
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The held-out benchmark `probe_v0` was kept frozen and was **not** used as
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a tuning surface for the standalone selection cycle that produced this
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adapter.
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## Limitations
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configuration is
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reproduces the standalone-model
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the closed-packet setting is not characterized.
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`probe_v0` item-level contents are intentionally withheld to preserve
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the held-out gate.
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is proof that the system meets the strict contract on a single frozen
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bounded benchmark.
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## Why pilot 3 is the release checkpoint
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The project deliberately stopped at pilot 3.
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A targeted follow-up, pilot 4, was built to fix one specific FEVER
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month/date temporal-insufficiency error. It fixed that single row, but
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it weakened broader behavior on the larger evaluation surfaces. That
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made pilot 4 a stop signal — useful negative evidence that further
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local-fix iteration was trading visible gains for wider regressions —
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not a better release. The project froze at the last point where the
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standalone model was strongest across multiple surfaces.
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## References
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- 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)
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- FEVER: [fever/fever](https://huggingface.co/datasets/fever/fever)
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- HotpotQA: [hotpotqa/hotpot_qa](https://huggingface.co/datasets/hotpotqa/hotpot_qa)
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- Technical brief (PDF): [evidence_faithful_reasoning_release_brief.pdf](./evidence_faithful_reasoning_release_brief.pdf)
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- Technical note: [technical_note_evidence_faithful_reasoning.md](./technical_note_evidence_faithful_reasoning.md)
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## Citation
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```bibtex
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@misc{
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title = {Evidence-Faithful Reasoning
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author = {
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year = {2026},
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howpublished = {Hugging Face model release},
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url = {https://huggingface.co/darcar0/evidence-faithful-reasoning-pilot-3}
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note = {Standalone LoRA adapter from the evidence-faithful reasoning project.}
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}
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```
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- research
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---
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# Evidence-Faithful Reasoning
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*Pilot 3 is the standalone model release.*
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This adapter turns its reasoning-distilled 27B base model into an
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evidence-first reader for closed packets of text. I built it because I wanted
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a release where reasoning had to prove itself: every answer has to land on the
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right evidence, quote that evidence verbatim, and stop with
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`Insufficient evidence.` when the packet does not justify a claim. The result
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is the strongest standalone model from the project, packaged here as a LoRA
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adapter you can load directly tonight.
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## Resources & Guides
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- [Technical brief (PDF)](./evidence_faithful_reasoning_release_brief.pdf)
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- [Technical note](./technical_note_evidence_faithful_reasoning.md)
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- [Fresh public holdout chart](./standalone_holdout_comparison.svg)
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- [Frozen benchmark progression chart](./benchmark_progression.svg)
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- [Release architecture chart](./project_release_arc.svg)
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+
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+

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*Fresh 36-task mixed public holdout: the standalone release beats the earlier
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bridge model on task accuracy, evidence F1, and quote F1, while the
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packet-local normalizer lifts the full stack to `0.9093` quote F1.*
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## Why this release exists
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I built this project to force reasoning models to show their work in the only
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place that counts: the evidence itself. Fluent answers were not enough. I
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wanted a model that had to retrieve the right units, quote them exactly, and
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fail closed when the packet ran out. This page leads with the standalone
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release because it is the artifact you can load immediately, inspect directly,
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and use without reconstructing the whole benchmark stack.
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## At a glance
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- LoRA adapter on top of
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[`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).
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- Strongest standalone model from the project and the release I want people to
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download first.
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- On a fresh 36-task public holdout, raw task improves from `0.8611` to
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`0.8889`, raw strict from `0.2222` to `0.4444`, and raw quote F1 from
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`0.3343` to `0.6815` over the earlier bridge model.
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- Zero invalid outputs on every reported evaluation surface.
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- The project also produced a benchmark-winning hybrid stack, but that is a
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separate result described under *Release architecture*.
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## Quick start
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model = PeftModel.from_pretrained(base, adapter_id)
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```
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The base model is 27B parameters, so load it in your usual quantization.
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## Prompt format
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This release works best with an evidence-first prompt that makes the answer
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subordinate to the cited text. A minimal version:
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```
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You are answering from a bounded evidence packet only.
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}
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```
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## Evaluation
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### Fresh 36-task mixed public holdout
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A held-out slice of 18 FEVER verify-claim tasks plus 18 HotpotQA grounded-QA
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tasks, drawn from public sources and de-duplicated against every training,
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dev, and `probe_v0` row.
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| Stack | Task | Strict | Evidence F1 | Quote F1 |
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|---|---:|---:|---:|---:|
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| Bridge + `deterministic_v3` | 0.8611 | 0.5833 | 0.8815 | 0.8815 |
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| **Pilot 3 + `deterministic_v3`** | **0.8889** | **0.5833** | **0.9093** | **0.9093** |
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The standalone release beats the earlier bridge model on task accuracy,
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evidence F1, and quote F1 in both raw and normalized form, ties normalized
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strict, and roughly doubles raw quote F1 at the model level.
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### Fixed dev triage slice (21 tasks)
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### Untouched 104-task Hotpot shadow slice
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Pilot 3 raw improved quote-faithful behavior over the raw bridge model on this
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slice, and pilot 3 + `deterministic_v3` matched bridge +
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`deterministic_v3` at the system level. That surface remains a narrative
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parity result because the report does not publish per-metric cells for it.
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## Release architecture
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This project ends in two finished artifacts, not one:
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1. **Standalone model release** — this page. Pilot 3 is the strongest
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version of the project's evidence-faithful behavior that moved into the
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model itself, evaluated across multiple non-`probe_v0` surfaces.
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2. **Benchmark-facing hybrid stack** — bridge `checkpoint-2` plus the
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`deterministic_v3` packet-local normalizer. That stack is the benchmark
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winner and the only configuration that clears every gate on frozen held-out
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`probe_v0`.
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The separation is deliberate. This page is for the standalone release you can
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download now. The benchmark winner is documented here because it explains the
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project's full result, not because those perfect `probe_v0` numbers belong to
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the adapter alone.
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## Intended use
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Use this release for work that has to stay inside a fixed body of text:
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- bounded document QA with explicit evidence requirements,
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- claim verification and grounded QA from closed evidence packets,
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- policy, compliance, contract, and internal-document workflows where each
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answer must be justified from the provided text,
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- research on evidence-faithful reasoning and abstention behavior.
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## Limitations
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+
- The downloadable artifact is the LoRA adapter only. The base model is
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+
required.
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+
- The `deterministic_v3` packet-local normalizer is not included in this
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+
download. The benchmark-winning configuration is adapter + normalizer, while
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+
the adapter alone reproduces the standalone-model results shown above.
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+
- Perfect `probe_v0` belongs to the benchmark-facing hybrid stack, not to this
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+
adapter alone.
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+
- Specialized for closed-packet reasoning, not open-ended chat or open-domain
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+
QA.
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+
- Frozen `probe_v0` item-level contents are intentionally not published with
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+
the release.
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| 199 |
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| 200 |
## Citation
|
| 201 |
|
| 202 |
+
References:
|
| 203 |
+
|
| 204 |
+
- Base model:
|
| 205 |
+
[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)
|
| 206 |
+
- Datasets:
|
| 207 |
+
[fever/fever](https://huggingface.co/datasets/fever/fever),
|
| 208 |
+
[hotpotqa/hotpot_qa](https://huggingface.co/datasets/hotpotqa/hotpot_qa)
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| 209 |
+
- Technical brief (PDF):
|
| 210 |
+
[evidence_faithful_reasoning_release_brief.pdf](./evidence_faithful_reasoning_release_brief.pdf)
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| 211 |
+
- Technical note:
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| 212 |
+
[technical_note_evidence_faithful_reasoning.md](./technical_note_evidence_faithful_reasoning.md)
|
| 213 |
+
|
| 214 |
```bibtex
|
| 215 |
+
@misc{darcar0_evidence_faithful_reasoning_pilot_3_2026,
|
| 216 |
+
title = {Evidence-Faithful Reasoning: Pilot 3},
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| 217 |
+
author = {darcar0},
|
| 218 |
year = {2026},
|
| 219 |
howpublished = {Hugging Face model release},
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| 220 |
+
url = {https://huggingface.co/darcar0/evidence-faithful-reasoning-pilot-3}
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| 221 |
}
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| 222 |
```
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