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)# pip install -U transformers accelerate # 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
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Download README.md from darcar0/quotebound-27b: direct link, hf CLI and curl.
- Browser
- Download file 8.29 kB
-
https://huggingface.co/darcar0/quotebound-27b/resolve/e5e90d1d72f18c2ebcf7d2ca04e9dbdb537e24e7/README.md
- Command line
-
hf download hf://darcar0/quotebound-27b@e5e90d1d72f18c2ebcf7d2ca04e9dbdb537e24e7/README.md
-
curl -L -o README.md https://huggingface.co/darcar0/quotebound-27b/resolve/e5e90d1d72f18c2ebcf7d2ca04e9dbdb537e24e7/README.md
8.29 kB
| language: | |
| - en | |
| license: apache-2.0 | |
| base_model: | |
| - Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2 | |
| datasets: | |
| - fever/fever | |
| - hotpotqa/hotpot_qa | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| tags: | |
| - reasoning | |
| - evidence-grounding | |
| - attribution | |
| - fever | |
| - hotpotqa | |
| - lora | |
| - peft | |
| - distillation | |
| - research | |
| # Evidence-Faithful Reasoning | |
| *Pilot 3 is the standalone model release.* | |
| This adapter turns its reasoning-distilled 27B base model into an | |
| evidence-first reader for closed packets of text. I built it because I wanted | |
| a release where reasoning had to prove itself: every answer has to land on the | |
| right evidence, quote that evidence verbatim, and stop with | |
| `Insufficient evidence.` when the packet does not justify a claim. The result | |
| is the strongest standalone model from the project, packaged here as a LoRA | |
| adapter you can load directly tonight. | |
| ## Resources & Guides | |
| - [Technical brief (PDF)](./evidence_faithful_reasoning_release_brief.pdf) | |
| - [Technical note](./technical_note_evidence_faithful_reasoning.md) | |
| - [Fresh public holdout chart](./standalone_holdout_comparison.svg) | |
| - [Frozen benchmark progression chart](./benchmark_progression.svg) | |
| - [Release architecture chart](./project_release_arc.svg) | |
|  | |
| *Fresh 36-task mixed public holdout: the standalone release beats the earlier | |
| bridge model on task accuracy, evidence F1, and quote F1, while the | |
| packet-local normalizer lifts the full stack to `0.9093` quote F1.* | |
| ## Why this release exists | |
| I built this project to force reasoning models to show their work in the only | |
| place that counts: the evidence itself. Fluent answers were not enough. I | |
| wanted a model that had to retrieve the right units, quote them exactly, and | |
| fail closed when the packet ran out. This page leads with the standalone | |
| release because it is the artifact you can load immediately, inspect directly, | |
| and use without reconstructing the whole benchmark stack. | |
| ## At a glance | |
| - LoRA adapter on top of | |
| [`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). | |
| - Strongest standalone model from the project and the release I want people to | |
| download first. | |
| - On a fresh 36-task public holdout, raw task improves from `0.8611` to | |
| `0.8889`, raw strict from `0.2222` to `0.4444`, and raw quote F1 from | |
| `0.3343` to `0.6815` over the earlier bridge model. | |
| - Zero invalid outputs on every reported evaluation surface. | |
| - The project also produced a benchmark-winning hybrid stack, but that is a | |
| separate result described under *Release architecture*. | |
| ## Quick start | |
| Pilot 3 is a LoRA adapter. Load the base model and attach the adapter: | |
| ```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/evidence-faithful-reasoning-pilot-3" | |
| tokenizer = AutoTokenizer.from_pretrained(base_id) | |
| base = AutoModelForCausalLM.from_pretrained(base_id, device_map="auto") | |
| model = PeftModel.from_pretrained(base, adapter_id) | |
| ``` | |
| The base model is 27B parameters, so load it in your usual quantization. | |
| ## Prompt format | |
| This release works best with an evidence-first prompt that makes the answer | |
| subordinate to the cited text. A minimal version: | |
| ``` | |
| 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.` | |
| ``` | |
| The model then writes a JSON object with this 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 | |
| A held-out slice of 18 FEVER verify-claim tasks plus 18 HotpotQA grounded-QA | |
| tasks, drawn from public sources and de-duplicated against every training, | |
| dev, and `probe_v0` row. | |
| | Stack | Task | Strict | Evidence F1 | Quote F1 | | |
| |---|---:|---:|---:|---:| | |
| | Bridge raw | 0.8611 | 0.2222 | 0.8815 | 0.3343 | | |
| | Pilot 3 raw | 0.8889 | 0.4444 | 0.9093 | 0.6815 | | |
| | Bridge + `deterministic_v3` | 0.8611 | 0.5833 | 0.8815 | 0.8815 | | |
| | **Pilot 3 + `deterministic_v3`** | **0.8889** | **0.5833** | **0.9093** | **0.9093** | | |
| The standalone release beats the earlier bridge model on task accuracy, | |
| evidence F1, and quote F1 in both raw and normalized form, ties normalized | |
| strict, and roughly doubles raw quote F1 at the model level. | |
| ### Fixed dev triage slice (21 tasks) | |
| | Stack | Task | Strict | Evidence F1 | Quote F1 | | |
| |---|---:|---:|---:|---:| | |
| | Pilot 3 + `deterministic_v3` | 1.0000 | 0.6190 | 0.8320 | 0.7095 | | |
| ### Untouched 104-task Hotpot shadow slice | |
| Pilot 3 raw improved quote-faithful behavior over the raw bridge model on this | |
| slice, and pilot 3 + `deterministic_v3` matched bridge + | |
| `deterministic_v3` at the system level. That surface remains a narrative | |
| parity result because the report does not publish per-metric cells for it. | |
| ## Release architecture | |
| This project ends in two finished artifacts, not one: | |
| 1. **Standalone model release** — this page. Pilot 3 is the strongest | |
| version of the project's evidence-faithful behavior that moved into the | |
| model itself, evaluated across multiple non-`probe_v0` surfaces. | |
| 2. **Benchmark-facing hybrid stack** — bridge `checkpoint-2` plus the | |
| `deterministic_v3` packet-local normalizer. That stack is the benchmark | |
| winner and the only configuration that clears every gate on frozen held-out | |
| `probe_v0`. | |
| The separation is deliberate. This page is for the standalone release you can | |
| download now. The benchmark winner is documented here because it explains the | |
| project's full result, not because those perfect `probe_v0` numbers belong to | |
| the adapter alone. | |
| ## Intended use | |
| Use this release for work that has to stay inside a fixed body of text: | |
| - bounded document QA with explicit evidence requirements, | |
| - claim verification and grounded QA from closed evidence packets, | |
| - policy, compliance, contract, and internal-document workflows where each | |
| answer must be justified from the provided text, | |
| - research on evidence-faithful reasoning and abstention behavior. | |
| ## Limitations | |
| - The downloadable artifact is the LoRA adapter only. The base model is | |
| required. | |
| - The `deterministic_v3` packet-local normalizer is not included in this | |
| download. The benchmark-winning configuration is adapter + normalizer, while | |
| the adapter alone reproduces the standalone-model results shown above. | |
| - Perfect `probe_v0` belongs to the benchmark-facing hybrid stack, not to this | |
| adapter alone. | |
| - Specialized for closed-packet reasoning, not open-ended chat or open-domain | |
| QA. | |
| - Frozen `probe_v0` item-level contents are intentionally not published with | |
| the release. | |
| ## Citation | |
| 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 brief (PDF): | |
| [evidence_faithful_reasoning_release_brief.pdf](./evidence_faithful_reasoning_release_brief.pdf) | |
| - Technical note: | |
| [technical_note_evidence_faithful_reasoning.md](./technical_note_evidence_faithful_reasoning.md) | |
| ```bibtex | |
| @misc{darcar0_evidence_faithful_reasoning_pilot_3_2026, | |
| title = {Evidence-Faithful Reasoning: Pilot 3}, | |
| author = {darcar0}, | |
| year = {2026}, | |
| howpublished = {Hugging Face model release}, | |
| url = {https://huggingface.co/darcar0/evidence-faithful-reasoning-pilot-3} | |
| } | |
| ``` | |