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 evidence_faithful_reasoning_release_brief.md with huggingface_hub
Browse files
evidence_faithful_reasoning_release_brief.md
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# Evidence-Faithful Reasoning
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## Release Brief
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Released: 2026-04-07
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Author: darcar0
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Hugging Face model release:
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[`darcar0/evidence-faithful-reasoning-pilot-3`](https://huggingface.co/darcar0/evidence-faithful-reasoning-pilot-3)
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Public GitHub release repo: `PUBLIC_REPO_URL`
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## Executive summary
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I built this project because I wanted a model release where reasoning had to
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cash out into recoverable evidence instead of hiding behind fluent language.
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The result is a strict evidence-faithful reasoning benchmark, a
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benchmark-winning hybrid system, and pilot 3: the strongest standalone model
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from the same project, released as a LoRA adapter on Hugging Face.
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The contract is strict. On every bounded evidence packet, the system has to:
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1. answer correctly,
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2. identify the right evidence,
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3. quote the exact supporting text, and
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4. abstain with `Insufficient evidence.` when the packet does not justify a
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claim.
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This release ships two finished outputs. The benchmark-facing winner is a
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hybrid stack — bridge `checkpoint-2` plus `deterministic_v3` packet-local
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quote normalization — that clears every gate on the frozen held-out
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`probe_v0` benchmark. The main downloadable artifact is pilot 3, the
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standalone model release, which beats the earlier bridge model on a fresh
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mixed public holdout and roughly doubles raw quote-faithful behavior at the
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model level.
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## Benchmark-facing winner
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The benchmark-facing winner is the strongest full system from the project.
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Training moved the model past the older frozen baseline. Deterministic
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packet-local normalization closed the remaining quote-faithfulness and strict
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grounded-success gap without leaving the bounded-packet setting.
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| Metric | Frozen probe_v0 |
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|---|---:|
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| Task success | **1.0000** |
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| Strict grounded success | **1.0000** |
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| Mean evidence F1 | **1.0000** |
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| Mean quote F1 | **1.0000** |
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| Verify label accuracy | **1.0000** |
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| Grounded QA accuracy | **1.0000** |
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| Contrastive consistency | **1.0000** |
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| Invalid / missing rate | **0.0000** |
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Canonical memo:
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[`reports/sft_v1_final_artifact_status.md`](../reports/sft_v1_final_artifact_status.md)
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## Standalone model release: pilot 3
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Pilot 3 is the strongest standalone model from the project and the release I
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want people to download first. It is the first standalone checkpoint in the
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project to hold up across multiple non-`probe_v0` evaluation surfaces, and it
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moves a meaningful amount of the winning behavior into the model itself.
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Fresh 36-task mixed public holdout:
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| Stack | Task | Strict | Evidence F1 | Quote F1 |
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|---|---:|---:|---:|---:|
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| Bridge raw | 0.8611 | 0.2222 | 0.8815 | 0.3343 |
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| Pilot 3 raw | 0.8889 | 0.4444 | 0.9093 | 0.6815 |
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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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Pilot 3 beats bridge on task accuracy, evidence F1, and quote F1 in both raw
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and normalized form, ties normalized strict, and roughly doubles raw quote F1
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(`0.3343` → `0.6815`) at the model level.
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Canonical memos:
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- [`reports/standalone_model_v2_freeze_memo.md`](../reports/standalone_model_v2_freeze_memo.md)
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- [`reports/standalone_model_v2_holdout_v1_bridge_vs_pilot3_status.md`](../reports/standalone_model_v2_holdout_v1_bridge_vs_pilot3_status.md)
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## Project arc and stopping point
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The project followed a full research-engineering loop: baseline mapping,
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benchmark design, prompt and structure interventions, a training-backed bridge
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model, deterministic quote normalization, and then a teacher-student
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distillation cycle to move the winning behavior into the model itself.
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A targeted follow-up, pilot 4, fixed one specific FEVER
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month/date temporal-insufficiency case but weakened broader behavior on larger
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evaluation surfaces. I treated that as a stop signal rather than churning for
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one more pilot. The release froze at the point where the standalone model was
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strongest and the benchmark-facing system was already complete.
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## Intended use and boundaries
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This is a specialized grounded reasoning release, not a general-purpose
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chatbot replacement. It is built for bounded document QA, claim verification,
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policy/compliance workflows, and other settings where every answer has to be
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justified from a closed body of text.
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Important boundaries:
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- Perfect `probe_v0` belongs to the hybrid stack, not to pilot 3 alone.
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- The Hugging Face download is the LoRA adapter only; the benchmark-winning
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configuration is adapter + `deterministic_v3`.
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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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## Release surfaces
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- Pilot 3 on Hugging Face:
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[`darcar0/evidence-faithful-reasoning-pilot-3`](https://huggingface.co/darcar0/evidence-faithful-reasoning-pilot-3)
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- Technical note:
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[`docs/technical_note_evidence_faithful_reasoning.md`](./technical_note_evidence_faithful_reasoning.md)
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- Release page:
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[`release/index.html`](../release/index.html)
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- Public GitHub release repo: `PUBLIC_REPO_URL`
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