---
license: agpl-3.0
language:
- en
tags:
- text-generation
- smol
- permacomputer
- bandit-curriculum
pipeline_tag: text-generation
---

# ANDREA-12M

**A**utonomous **N**eural **D**ata **R**ecipe for **E**ducation and **A**gency

A 12.8M parameter language model grown on a single RTX 4090 using a bandit-controlled curriculum.
Part of the permacomputer project — open source, open data, open weights.

## Model Details

| Property | Value |
|----------|-------|
| Parameters | 12.8M |
| Architecture | Transformer decoder, 384d/12h/6L |
| Embedding dim | 384 |
| Heads | 12 |
| Layers | 6 |
| Context | 1024 tokens |
| Tokenizer | Harris morpheme (2048 segments, 2305 vocab) |
| Training steps | 43,587 |
| Final SMMA loss | 2.0 |
| Best single-step loss | 0.21 |
| Training time | ~72 hours |
| Hardware | Single NVIDIA RTX 4090 (24GB VRAM, 1.4GB used) |
| CUDA engine | microgpt_cuda.cu (custom, FP32) |
| Born | 2026-03-21 12:53 UTC / 08:53 EST |
| License | AGPL-3.0 |

## Files

| File | Step | Description |
|------|------|-------------|
| `ANDREA-12M.bin` | 43,587 | Final checkpoint (SMMA 2.0) |
| `ANDREA-12M-best.bin` | 42,300 | Best checkpoint (lowest loss during training) |
| `harris_segments.json` | — | Harris tokenizer segments (required for inference and fine-tuning) |

### Checkpoint format

Binary, little-endian: `[int32 step][int32 n_params][n_params × float32 weights][n_params × float32 m][n_params × float32 v]`

- **Weights**: model parameters (12.8M floats, ~49MB)
- **m**: Adam first moment (same size)
- **v**: Adam second moment (same size)
- Total: ~147MB per checkpoint

Use either checkpoint to resume fine-tuning (weights + optimizer state preserved)
or extract weights only for inference (first `n_params` floats after the 8-byte header).

## Training Data

Trained on a curated mix of open conversational and educational data:

- **NousResearch/Hermes-3-Dataset** (general, creative, roleplay) — 590K conversations
- **Dictionary** — 88K word definitions distilled from Hermes 3 8B
- **Gutenberg** — public domain literature (Project Gutenberg)
- Additional: chat, smoltalk, oasst, dolly, IRC, repo-docs

Data mix controlled by a UCB1 multi-armed bandit with dice-based phase control.
The bandit dynamically adjusts source weights during training based on per-source
loss trajectories. Full curriculum specification in the white paper.

## Training Recipe

- Harris morpheme tokenizer (2048 segments)
- Cosine LR schedule with warm restart at step 25K (0.0004 peak)
- Phase-based bandit: 2 focus arms, 1d3 dice, source floors
- Checkpoints every 100 steps, SIGTERM-safe
- Per-source reward attribution, epoch penalty, coverage tracking

## Capabilities

ANDREA-12M learns patterns, not facts. At 12.8M parameters it produces:
- Correct Q&A turn structure (`> question / < answer`)
- Definition-style responses
- Multi-sentence outputs with plausible grammar
- Instruction-following scaffolding ("explain", "define", "describe")

It does NOT produce factually accurate content — it's a pattern machine.
Factual accuracy requires scaling to ANDREA-120M (planned).

## Usage

```python
# Inference via microgpt
from microgpt import load_model, generate_fast

model = load_model('ANDREA-12M.json')
results = generate_fast(model['state_dict'], model['uchars'], model['bos'],
                        384, 12, 6, 1024, prefix='> what is an apple? / <')
print(results[0][0])
```

## White Paper

[ANDREA-12M-WHITEPAPER.pdf](ANDREA-12M-WHITEPAPER.pdf) — full technical paper covering architecture, bandit curriculum, data sources, training recipe, and results.

Source: `whitepaper/ANDREA/WHITEPAPER.rst` in the [uncloseai-cli repository](https://git.unturf.com/engineering/unturf/uncloseai-cli).

## Citation

```
ANDREA: Autonomous Neural Data Recipe for Education and Agency
TimeHexOn, foxhop, russell@unturf
March 2026, permacomputer.com
```

## License

AGPL-3.0. Code outlasts authors. Infrastructure outlasts builders.

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