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Pure Reasoning Engine πŸ”

A small, fast LLM fine-tuned on distilled frontier reasoning traces, conditioned to know nothing and treat the internet as its only source of truth.

Architecture

User Query β†’ Decompose β†’ Search β†’ Filter β†’ Reason β†’ Answer with Citations
Component Model Size Purpose
Reasoning Engine SmolLM3-3B (fine-tuned) 3B params Step-by-step reasoning over evidence
Epistemic Filter DistilBERT (fine-tuned) 67M params Pre-screens all retrieved text for quality
Internet Search DuckDuckGo Free API Retrieves ground-truth evidence

Core Principle

"I know nothing. The internet is ground truth. My only job is to reason over what I retrieve."

The model NEVER guesses β€” it searches, retrieves, then reasons over verified sources.

Training Data

The reasoning model was fine-tuned on ~800K rows of distilled DeepSeek-R1 reasoning traces:

  • open-r1/OpenR1-Math-220k β€” Math reasoning (220K rows)
  • a-m-team/AM-DeepSeek-R1-Distilled-1.4M β€” Diversified reasoning (500K rows)
  • open-r1/codeforces-cots β€” Code reasoning (competitive programming)

The epistemic filter was trained on TurkuNLP/finerweb-10bt line-level quality labels.

Quick Start

pip install -r requirements.txt
python inference_loop.py --query "What is the latest research on CRISPR?"

Interactive Mode

python inference_loop.py --interactive

Training

Reasoning Model (requires GPU with β‰₯24GB VRAM)

python train_reasoning_sft.py

Epistemic Filter (works on single GPU)

python train_epistemic_filter.py

Key Design Decisions

  • Small is Beautiful: 3B parameters is plenty when training is focused on reasoning
  • Humility Over Hubris: The model's strength is knowing it knows nothing
  • Reasoning > Memorization: Distill the skill of thinking, not the burden of facts
  • Defense First: The filter and source-critical training make the internet safe as ground truth
  • Cheap to Build: Base model is open-weight (Apache 2.0), training data is distilled, search is free

Model Links

License

Apache 2.0 (same as SmolLM3 base model)

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