LLM’s By (WithIn Us AI)
Collection
Classic Large Language Models built by “WithIn Us Ai” from scratch focused on Deep COT in tiny models • 5 items • Updated
Nano.Deep.Reasoner.11m-HyperMini
An approximately 11,094,003-parameter decoder-only adaptive recurrent reasoning language model.
Architecture
- Parameters: 11,094,003
- Context: 1096
- Vocabulary: 16,000
- Hidden size: 240
- Base Transformer blocks: 6
- Attention heads: 8
- Head dimension: 30
- Intermediate size: 1072
- Shared recurrent reasoning block
- Learned latent reasoning memory: 8 tokens
- Adaptive reasoning depth: 2-16
- Gated recurrent memory updates
- Adaptive halting controller
- Verification head
- Revision head
- RoPE
- Tied input/output embeddings
- Padding-aware causal attention
- Explicit padded-state suppression
Reasoning tokens
Training
Dataset:
Plans11/Organized_PreTrain_1k_Context
Each session contains up to 200,000 NEW examples.
Examples are protected by SHA-256 hashes.
Session reservations are committed before training so a hard Kaggle interruption cannot cause the same reserved examples to be selected again.
Resume safety
The checkpoint contains:
- model.safetensors
- optimizer.pt
- rng_state.pt
- training_state.json
- example_ledger.json
- tokenizer.json
- tokenizer_config.json
- token_id_manifest.json
- config.json
Dataset fingerprint and tokenizer artifact hashes are verified before resume.
Current state
Completed sessions: 5
Unique examples reserved/trained: 200,000
Unique completed examples: 460,000
Global optimizer steps: 7,189
Last session loss: 0.17293200694084168
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