README.md

---
library_name: transformers
pipeline_tag: text-generation
base_model: HuggingFaceTB/SmolLM2-135M
tags:
- next-concept-prediction
- conceptlm
- causal-lm
- smollm2
- tessera
license: apache-2.0
---

# Tessera-135M-Gate

The smallest member of the Tessera family, and the reason the bigger ones exist.
This 135M `architecture-gate checkpoint` (a `HuggingFaceTB/SmolLM2-135M` backbone
with the full Next Concept Prediction path) was trained on a deliberately overfit
TinyStories subset before any large spend, to prove the whole path - concept
pooling, quantized codes, causal concept blocks, feedback - trains end to end.
It is not a language-model quality result, and it is not meant to be: what makes
it worth publishing is what broke, and what the break predicted at scale.

## TL;DR

Small and cheap on purpose: this gate run exists to de-risk the 1B-token comparison before spending on it. It earned its keep in both directions.

- **All three objectives train** (NTP, NCP, VQ fell 41 to 43% over the run),
  and the decoder causally uses the concept channel even at 135M: zeroing the
  feedback costs +0.0205 nats of held-out NTP loss.
- **It also caught a real failure mode**: effective codebook perplexity stayed
  near 2.4 with usage around a third of the codebook, and shuffled feedback  cost nothing - a low-entropy shortcut the 1B-token run later left far behind.  Gate small, then scale: some behaviors only appear above a scale threshold.


## Numbers

The raw record this narrative is built from; per-arm READMEs and the whitepaper hold the
full analysis.

| Metric | Value |
| --- | ---: |
| Held-out NTP loss | 1.8751 |
| Held-out perplexity | 6.5213 |
| Zero feedback delta | 0.0205 |
| Shuffled feedback delta | 0.0000 |
| Codebook perplexity / usage | 2.4087 / 33.0% |
| Training tokens | 16,777,216 |
| Tracked compute estimate | $1.38 |

Intervention deltas are increases in held-out NTP loss relative to normal predicted concept
feedback, evaluated on identical batches.

## Architecture

- chunk size: 4
- product code: 9 segments x 64 entries
- causal concept blocks: 2
- injection point: before token decoder block 2
- NCP target: next continuous concept
- loss: `L_ntp + 1 L_ncp + 1 L_vq`

This is a compact ConceptLM-style implementation, not an 8.9B NCP-ArchPreview replica. It
omits iterative residual coding, cross-scale residual connections, and the large-scale
training recipe.

## Training data and provenance

The matched corpus and the training record are pinned in the ncp-smol repository:
packed-cache SHA256 hashes, the complete metric log, trainer state, and the raw
intervention evaluation JSON (also shipped in this repository as `eval.json` and
`metrics.jsonl`).

## Loading

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("yava-code/Tessera-135M-Gate")
model = AutoModelForCausalLM.from_pretrained("yava-code/Tessera-135M-Gate", trust_remote_code=True)
```

## The Tessera family

Three checkpoints, one story, in reading order:

- [Tessera-135M-Gate](https://huggingface.co/yava-code/Tessera-135M-Gate) - 135M architecture gate (overfit, published for the scale story) **(this model)**
- [Tessera-1B-Nano-Base](https://huggingface.co/yava-code/Tessera-1B-Nano-Base) - 1B-token matched NTP-only control
- [Tessera-1B-Nano](https://huggingface.co/yava-code/Tessera-1B-Nano) - 1B-token matched concept arm

All cards are generated from the run artifacts by the same `build_card`; the
[study repository](https://github.com/yava-code/Tessera-1B-Nano) holds the
whitepaper and full records.
## References

- Code and study: https://github.com/yava-code/Tessera-1B-Nano
- ConceptLM: https://arxiv.org/abs/2602.08984
- NCP-ArchPreview: https://arxiv.org/abs/2609.10715