---
language: fr
license: apache-2.0
tags:
  - text-generation
  - from-scratch
  - looped-transformer
  - adaptive-computation
  - french
pipeline_tag: text-generation
---

# Focal-15M-fr 🎯

> ⚠️ **Preliminary results — 1 seed, variance not controlled.** Every number below is a single training run evaluated once (50 prompts × 200 tokens, fixed seed). Directions are consistent, magnitudes are not validated. Treat as a lab notebook, not a benchmark.

**Focal** is a 15M-parameter French language model trained **from scratch** (no 🤗 Transformers, no pretrained weights) on a single GTX 1080 Ti. It is the **in-domain** member of a three-model family: a looped transformer with **learned per-token adaptive halting** using an **absolute entropy threshold**.

## Identity

- **Architecture:** decoder-only, LLaMA-style (RoPE, RMSNorm, SwiGLU, QK-Norm, Flash/SDPA attention), **looped R=4** (effective depth 32) + **per-token adaptive halting**: a token stops looping once its output entropy falls below a fixed threshold (1.0 nats). **Zero added parameters** — the entropy comes from logits already computed. Halting is **active during training**, so the model learns to be good at whatever depth each token stops.
- **Size:** 15.01M parameters · `n_embd 256 / n_layer 8 / n_head 4` · context 768.
- **Language:** French, from scratch.
- **Tokenizer:** custom ByteLevel BPE, 32k vocab (`bpe_tokenizer_32k.json`).
- **Data:** French corpus (AI-rewritten Wikipedia + filtered web via RDTextract), ~425M tokens total. **These runs used 20% of the train split, 2 epochs** (fast research protocol).

## What Focal is good at

Focal is the **in-domain** model: learned halting acts as a **training-time regularizer** that sharpens quality on prompts close to the training distribution.

- **Best in-domain coherence: 44.1** (vs 35.2 baseline) — the highest of the four models.
- **Best prompt overlap: 0.176** (stays most on-topic).
- Trade-off: it is the **weakest out-of-domain** (coherence 29.1). On far-off prompts, the sharpening becomes a liability.
- Note: on real text the halting **rarely fires at inference** (most tokens run full depth) — the gain is a *training* effect, not an inference speedup.

## Robust evaluation (50×200, 1 seed)

Same table on all three model cards, so the trade-off is visible everywhere. Auto = held-out corpus prompts (in-domain); Fixed = generic hand-written prompts (out-of-domain).

| Model | Val PPL ↓ | Coherence auto ↑ | Coherence fixed ↑ | Invented names auto ↓ | Prompt overlap auto ↑ |
|---|---|---|---|---|---|
| Baseline (vanilla) | 31.2 | 35.2 | 40.7 | 0.137 | 0.139 |
| Cadence (looped R=4) | **28.9** | 39.3 | 32.5 | 0.121 | 0.147 |
| **Focal** (absolute halt) | 29.1 | **44.1** | 29.1 | 0.103 | **0.176** |
| Nomade (percentile halt) | 31.0 | 36.3 | **41.8** | **0.094** | 0.126 |

**No model wins everything — that is the result.** Recurrence helps in-domain and hurts out-of-domain; the halting variants trade one for the other. Factuality is *not* improved by any of them (15M capacity ceiling).

## Lineage (this is not novel)

- **ACT** — Adaptive Computation Time, Graves 2016 (per-token variable depth) · [arXiv:1603.08983](https://arxiv.org/abs/1603.08983)
- **Universal Transformer** — Dehghani et al. 2018 (the looped/weight-shared ancestor) · [arXiv:1807.03819](https://arxiv.org/abs/1807.03819)
- **Recurrent-Depth Transformer** — Geiping et al. 2025 (Huginn-3.5B; the canonical RDT this family follows) · [arXiv:2502.05171](https://arxiv.org/abs/2502.05171)
- **CALM** — Confident Adaptive Language Modeling, Schuster et al. 2022 (confidence early-exit across layers) · [arXiv:2207.07061](https://arxiv.org/abs/2207.07061)
- **LoopViT** — Shu et al. 2026 (parameter-free entropy exit + weight-tied loop) · [arXiv:2602.02156](https://arxiv.org/abs/2602.02156)

Direct basis — the two from-scratch looped studies this work reproduces in French: **Kohli et al. 2026** ([arXiv:2604.07822](https://arxiv.org/abs/2604.07822), COLM) and **Chen 2026** ([arXiv:2603.21676](https://arxiv.org/abs/2603.21676)).

Focal is a **variant of LoopViT for autoregressive language, with halting learned during training** (per token, entropy criterion). No claim of novelty — the ingredients are all prior work.

## Limitations

- **1 seed, no variance control** — magnitudes may reorder with more seeds.
- **20% of data, 2 epochs** — a short research protocol, not a full training run.
- **15M capacity** — hallucinations are expected; the model learns *form and coherence*, not *facts*. It will not reliably tell you the capital of France.
- Absolute entropy threshold does **not transfer across model sizes** (a bigger model is more confident) — for scale-up, use the percentile variant (Nomade).
- No instruction tuning — a pure completion model.

## Related

- ✍️ Write-up (Article 3): *Teaching a 15M French LLM to think deeper* — [Hugging Face blog](https://huggingface.co/blog/RDTvlokip/teaching-a-15m-french-llm-to-think-deeper)
- 🔁 **Cadence** (looped reference): [RDTvlokip/Cadence-15M-fr](https://huggingface.co/RDTvlokip/Cadence-15M-fr)
- 🧭 **Nomade** (out-of-domain, size-invariant halting): [RDTvlokip/Nomade-15M-fr](https://huggingface.co/RDTvlokip/Nomade-15M-fr)

## Usage

This model uses **custom from-scratch code** (not 🤗 Transformers). The code ships with the repo (`model/`, `utils/`), so a snapshot download is self-contained. Requires `torch`, `huggingface_hub`, `tokenizers`.

```python
import sys, torch
from huggingface_hub import snapshot_download

repo = snapshot_download("RDTvlokip/Focal-15M-fr")  # code + weights + tokenizer
sys.path.insert(0, repo)
from model.gpt2 import GPT2
from utils.tokenizer import GPT2Tokenizer

# The absolute-halting config travels inside the checkpoint — nothing extra to set.
model = GPT2.from_pretrained(f"{repo}/best_model.pt", device="cuda"); model.eval()
tok = GPT2Tokenizer(f"{repo}/bpe_tokenizer_32k.json")

ids = tok.encode("Au Moyen Âge, les chevaliers", add_special_tokens=True)
if ids and ids[-1] == tok.eos_token_id:
    ids = ids[:-1]                         # drop trailing <eos> (keeps it on-topic)
out = model.generate(
    torch.tensor([ids], device="cuda"),
    max_length=80, temperature=0.8, top_k=40, top_p=0.9,
    repetition_penalty=1.3, eos_token_id=tok.eos_token_id,
)
print(tok.decode(out[0].tolist(), skip_special_tokens=True))
```

**Théo CHARLET** — TSSR Graduate, AI/ML · Creator of AG-BPE · [rdtvlokip.fr](https://rdtvlokip.fr)