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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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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## Executive summary
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I built this project because I wanted a
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The contract is strict. On every bounded evidence packet, the system has to:
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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
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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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mixed public holdout and roughly doubles raw quote-faithful behavior at the
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model level.
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##
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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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| 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
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project
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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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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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## Project arc and stopping point
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The
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benchmark
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A targeted follow-up, pilot 4, fixed one specific FEVER
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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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[`
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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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Companion files on this page:
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- [`technical_note_evidence_faithful_reasoning.md`](./technical_note_evidence_faithful_reasoning.md)
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- [`standalone_holdout_comparison.svg`](./standalone_holdout_comparison.svg)
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- [`benchmark_progression.svg`](./benchmark_progression.svg)
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- [`project_release_arc.svg`](./project_release_arc.svg)
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## Executive summary
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I built this project because I wanted a release where reasoning had to prove
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itself from the evidence instead of hiding behind fluent language. The result
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is a strict evidence-faithful benchmark, a benchmark-winning hybrid system,
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and pilot 3: the standalone model release, shipped here as a LoRA adapter on
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Hugging Face.
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The contract is strict. On every bounded evidence packet, the system has to:
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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 ends in 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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mixed public holdout and roughly doubles raw quote-faithful behavior at the
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model level.
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## Standalone model release
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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 open first. It is the first standalone checkpoint in the
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project that holds up across multiple non-`probe_v0` evaluation surfaces.
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Fresh 36-task mixed public holdout:
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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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## Benchmark-facing winner
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+
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The benchmark-facing hybrid stack is the strongest full system from the
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project. Training moved the model past the older frozen baseline.
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Deterministic packet-local normalization closed the remaining quote-faithful
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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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## Project arc and stopping point
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The release has two public faces: a standalone model you can load directly and
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a benchmark-facing hybrid stack that closes the last quote-faithfulness gap.
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That split is part of the project story, not something hidden behind the fine
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print.
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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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[`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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- Fresh public holdout chart:
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[`standalone_holdout_comparison.svg`](./standalone_holdout_comparison.svg)
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- Frozen benchmark progression chart:
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[`benchmark_progression.svg`](./benchmark_progression.svg)
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- Release architecture chart:
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[`project_release_arc.svg`](./project_release_arc.svg)
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