---
license: mit
library_name: transformers
tags:
- causal-lm
- decoder-only
- reasoning
- deep-reasoning
- recurrent-transformer
- adaptive-computation
- chain-of-thought
- adaptive-reasoning
---

# 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

<think>
</think>

<thought>
</thought>

<reasoning>
</reasoning>

<answer>
</answer>

## 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