dimitarpg13/semsimula-ladder-live-code-d384-l8-none-norc-sr2
text-generation model by dimitarpg13
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Model card & documentation
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For comparison, on the same code tokens and schedule: The mean difference in log perplexity (nats) between two models over a span of evaluations, with the standard deviation of the per-evaluation differences. Every validation target (262,144 tokens in 512 windows of 512) is put in groups by what the context offers for it: a first occurrence does not occur earlier in the window, a repeat does, an induction target follows a token pair seen earlier; and by how far back its last earlier occurrence lies. For each group, the table gives the share of SO-CODE's per-token gap to the matched GPT-2 that a model closes (debug/copysplit.py, best checkpoints). Vθ is a bank of eight anisotropic Gaussian wells whose centres, widths and depths are computed from the context, shared by all layers and told apart by a learned depth code. The nearest-well ratio is the distance from a token's state to…
Read the full model card ↗ · Preview checked 2026-11-03T07:42:21.442Z
Inside the original model card — Document outline
- SO-CODE at eight layers (L8-CODE): depth keeps paying on code, with diminishing returns (Gen 3)
- Result
- Paired by step
- Through training
- Where the gain is: the copy split
- The wells, layer by layer
- Diagnostics
- A reading
- Reproduction
- Glossary
- Files
- Companion notes
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Model overview
dimitarpg13/semsimula-ladder-live-code-d384-l8-none-norc-sr2 is a text-generation model repository published by dimitarpg13 on Hugging Face. The source reports the pytorch library.
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Model facts
- Task
- text-generation
- Library
- pytorch
- Recent downloads (30 days)
- 0
- Cumulative likes
- 0
- Hugging Face trending score
- 0
- Reported safetensors parameters
- Not reported
- Architecture
- Not reported
- License
- cc-by-4.0
- Access
- Not gated by Hugging Face
- Created
- 2026-11-03T06:33:32.000Z
- Last modified
- 2026-11-03T06:35:19.000Z
Compatibility and lineage
Reported languages: en
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-03T06:52:20.270Z. Daily imports are snapshots, not real-time monitoring.
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