43 lines
2.9 KiB
Markdown
43 lines
2.9 KiB
Markdown
# Quintus Documentation
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Quintus-1.7B is a compact assistant built from the Qwen3-1.7B-Base architecture. The project uses online full-vocabulary knowledge distillation from a Qwen3-8B teacher, followed by targeted SFT for instruction style, identity grounding, and generation stability.
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This documentation summarizes the public architecture, training decisions, evaluation controls, and release artifacts for the showcase branch.
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## Reading Order
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- [Architecture](architecture.md): End-to-end pipeline, modules, data flow, and training phases.
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- [Experiment Timeline](experiment_timeline.md): How the project moved from offline top-k KD to final online full-vocabulary KD.
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- [Training Playbook](training_playbook.md): Practical training choices, memory rules, packing, kernels, and checkpointing.
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- [Pipeline Hardening](pipeline_hardening.md): Silent-failure classes and the safeguards added around artifacts, provenance, and runtime.
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- [Evaluation Methodology](evaluation_methodology.md): Benchmark controls, parser traps, raw/chat comparisons, and qualitative evaluation rules.
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- [Engineering Insights](engineering_insights.md): Condensed technical lessons and design decisions.
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- [Benchmarks](benchmarks.md): Verified evaluation results and interpretation.
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- [Weight Audit](weight_audit.md): Structural checkpoint verification and what the audit means.
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- [Hugging Face Model Card](huggingface_model_card.md): Release-page text for the public model card.
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## Project Summary
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The core thesis is simple: a small base model can absorb useful reasoning behavior from a larger instruction model if the distillation signal is dense enough and the evaluation controls are fair.
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The project initially explored sparse offline top-k distillation, but that approach hit a ceiling because the student only saw a tiny fraction of the teacher vocabulary distribution. The final pipeline pivots to online KD, where teacher and student are run together and the student receives the teacher's full-vocabulary probability distribution during training.
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After KD, a small SFT stage teaches the model how to expose that knowledge in a conversational interface. This separation matters: KD transfers capability; SFT and later preference training improve behavior, style, and confidence calibration.
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## Repository Map
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```text
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configs/ Training configuration and DeepSpeed template.
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src/ Online KD, data loading, losses, checkpointing, and packing.
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sft/ Post-KD supervised fine-tuning, chat, and consolidated evaluation runner.
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weight_audit/ Checkpoint structure and weight-divergence audit.
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docs/ Public architecture, training, evaluation, and release notes.
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```
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## Main Public Artifact
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The final model weights are available at: [Quintus](https://huggingface.co/iamrahulreddy/Quintus)
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The Colab quickstart is available at: [Colab Quick Chat](https://colab.research.google.com/drive/1TdMSN5HzD1mToCFVf_qQoj10NGZLy2V0?usp=sharing)
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