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Model: openhonest/babylm-2026-fr-92m Source: Original Platform
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README.md
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README.md
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---
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license: mit
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language:
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- fr
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tags:
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- babylm
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- babylm-2026
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- gpt2
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- causal-lm
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- french
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- cross-linguistic
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- low-resource
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- right-tool-right-job
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library_name: transformers
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pipeline_tag: text-generation
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---
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# BabyLM 2026 Strict, French (92M words)
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A 125M-parameter GPT-2 trained from scratch on 92,469,402 words of French text. Submitted to the BabyLM 2026 Strict track and the primary checkpoint reported in *Right Tool, Right Job: Why Training Language Matters More Than Training Data* (Wasserman & Beauchemin, BabyLM 2026 / ACL Rolling Review submission).
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## Headline result
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QFrBLiMP (Quebec French native minimal-pair benchmark, 1761 pairs): **85.97% overall**.
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| Subset | Pairs | Accuracy |
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|---|---:|---:|
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| Anglicism | 267 | 80.15% |
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| Morphology | 716 | 85.47% |
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| Semantic | 398 | 87.19% |
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| Syntax | 380 | 89.74% |
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| **Overall** | **1761** | **85.97%** |
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QFrCoLA (Quebec French acceptability classification, fine-tuned with LoRA rank 16): test accuracy ~72%, MCC ~0.24 (epoch 3 of fine-tune).
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## Argument supported by this model
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The companion paper develops the cross-linguistic argument that training-language morphological richness, not neural architecture or pretraining scale, is the load-bearing variable for grammar acquisition. This checkpoint is the child-scale (under 100M words) French anchor; the broader argument is also supported by the *Scaling Hypothesis Is Language-Contingent* and *English Considered Harmful* deposits cited below, which test the same claim at different scales and with different ablations.
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## Model details
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- Architecture: GPT-2 decoder-only, causal LM (`GPT2LMHeadModel`)
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- Parameters: ~125M
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- Layers: 12
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- Attention heads: 12
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- Hidden size: 768
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- Max sequence length: 512
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- Vocabulary: 50,000 BPE, French Wikipedia source
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- Precision: float32
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## Training data
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- 92,469,402 words of French (under the BabyLM 2026 Strict 100M-word cap)
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- Custom corpus assembled from CHILDES French subsets and the Orléans corpus as a developmental base, with lemma-frequency oversampling guided by a Haitian Creole vocabulary oracle (high-frequency, high-composability lemmas surviving pidginization)
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- Training data is 100% morphologically rich French; Haitian Creole sentences are not mixed in
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- See *Right Tool, Right Job* §3 for full corpus curation methodology
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## Training procedure
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- Peak learning rate: 1.0e-4
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- LR schedule: cosine decay to ~1.9e-7
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- Epoch: 3 (of a 5-epoch trajectory; epoch 3 is the grammatical-competence peak reported in §4.2 of the paper)
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- Tokens/sec: ~94,000 (CUDA)
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- Approximate GPU hours through epoch 3: ~3
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- Final training loss: 3.19, perplexity 24.4
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## Intended use
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Suitable for:
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- Replicating *Right Tool, Right Job* results
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- Cross-linguistic emergence research
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- Quebec French native-benchmark development
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- Studies of morphological redundancy and training-data efficiency at child scale
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Not suitable for:
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- General-purpose French text generation at production quality (corpus is developmental, not web-scale)
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- Any English-language task (the model has zero English training exposure)
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## Limitations
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- French-only training; zero exposure to English or other non-French data
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- Child-scale corpus (92M words) is far below typical web-scale pretraining
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- BPE tokenizer trained on French Wikipedia, which differs in register from the CHILDES / Orléans developmental sources
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- LoRA fine-tuning was used in downstream evaluation grids (see *Right Tool, Right Job* §5)
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## Citation
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```bibtex
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@inproceedings{wasserman_beauchemin_2026_right_tool,
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title = {Right Tool, Right Job: Why Training Language Matters More Than Training Data},
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author = {Wasserman, Adam Z. and Beauchemin, David},
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booktitle = {BabyLM 2026 Workshop / ACL Rolling Review submission},
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year = {2026}
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}
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```
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Companion deposits supporting the broader cross-linguistic argument:
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- Wasserman, Adam Z. (2026). *The Scaling Hypothesis Is Language-Contingent.* Zenodo DOI [10.5281/zenodo.19423151](https://doi.org/10.5281/zenodo.19423151).
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- Wasserman, Adam Z. (2026). *English Considered Harmful: How Morphological Poverty Pollutes Language Model Training.* Zenodo DOI [10.5281/zenodo.19443357](https://doi.org/10.5281/zenodo.19443357).
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Pre-registrations on OSF: [SJ48B](https://osf.io/sj48b) (Language-Only Hypothesis), [PCX2D](https://osf.io/pcx2d) (morphological complexity gradient).
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## Acknowledgments
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The QFrBLiMP and QFrCoLA evaluation benchmarks are by David Beauchemin and collaborators (Université Laval, Institut Intelligence et Données).
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