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FrameXlabs/fragment-2

AI model by FrameXlabs

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A 36M-parameter System-One decision model, trained entirely from scratch by FrameXlabs — own BPE tokenizer, own encoder, own RLCD loop. Built on the original Fragment build (fragment-1 v2.29). Not a fine-tune of Laya or any other model — no pretrained weights, no teacher. It reads a state (any text: email, ticket, review, JSON dump) plus typed questions and answers in a single forward pass — no autoregression, no generation, nothing to parse, nothing to hallucinate. Every answer carries a calibrated probability, so the model can say "I'm not sure" honestly. (fragment-1 had 3 types; fragment-2 adds multi and rank.) public datasets (SST-2, BoolQ, Amazon polarity, AG News, DBpedia-14, Yelp-5), with anti-position-bias option shuffling. 2 epochs, effective batch 128 (micro-batch 64 × grad accumulation — fits a free 16 GB T4). logit-noise (σ 1.0 → 0.25) and the reward is a proper scoring rule (log score / Brier / RPS), so…

FrameXlabs/fragment-2 on Hugging Face A short preview, not the full document.

Read the full model card ↗ · Preview checked 2026-09-26T07:43:46.848Z

Inside the original model card — Document outline
  1. fragment-2
  2. What is a System-One decision model?
  3. Five question types
  4. Architecture — F3Net (the original F2Net, scaled and modernized)
  5. Training recipe (the original fragment-1 recipe, extended)
  6. Why it exists — vs Laya
  7. Files
  8. Reproduce it
  9. Honest limits
  10. Acknowledgements

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Model overview

FrameXlabs/fragment-2 is a task-unspecified model repository published by FrameXlabs on Hugging Face. A library was not reported.

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License
apache-2.0
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Not gated by Hugging Face
Created
2026-09-26T06:26:53.000Z
Last modified
2026-09-26T06:31:12.000Z

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Reported languages: en

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