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moral1/babylm_strict_small_2026
babylm_strict_small_2026 is a fill-mask model from moral1. Use it when you need the model to fill a missing word. It is set up for transformers. The card lists the license as other.
- Model name: P7 - Challenge: BabyLM 2026 - Track: Strict-Small - Backend: causal - Repository: moral1/babylmstrictsmall2026 - Final full-eval checkpoint: 80M (wordsseen=80,010,749) - Training checkpoints: 1M-100M cum…
Downloads · 30 days
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9% of all-time downloads
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From the Hugging Face model README
moral1/babylm_strict_small_2026words_seen=80,010,749)final_compliant endpoint (words_seen=99,992,923)3d57ddc8c40ee795c0b5e41b3a20251a9457a5931e41f721edd432379a8d253dc4936e5a7ea4f003662d037fa9d8c927f77ceefetokenizer.json, tokenizer_config.json, special_tokens_map.jsonchck_1M through chck_9M, then chck_10M through chck_100M every 10M wordsThe originally overshooting 100,002,969-word checkpoint is not used as the official 100M branch. The chck_100M branch points to the strict compliant endpoint with 99,992,923 words seen.
This model is intended for research and BabyLM 2026 Strict-Small evaluation workflows.
The model was trained under the BabyLM Strict-Small data budget and is not intended as a general-purpose production language model. Outputs may be unreliable, biased, or inappropriate, and the model should be evaluated in context before any downstream use.
Training data follows the BabyLM 2026 Strict-Small challenge data constraints. Dataset licensing and attribution follow the BabyLM challenge materials. Model code and weights are provided for research and evaluation use.