Add RecGPT-10M model card

README.md

@@ -1,3 +1,59 @@
 ---
+language:
+- en
 license: mit
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- babylm
+- babylm-2026
+- strict-small
+- custom_code
 ---
+
+# RecGPT-10M
+
+RecGPT-10M is a 34.17M-parameter recursive causal language model trained for the BabyLM 2026 Strict-Small track. It was trained for 10 epochs on a custom 10M-word English corpus using a 32,768-token BPE vocabulary.
+
+The model applies a shared Transformer block recursively for 16 iterations. Its hidden size is 768, embedding size is 192, and feed-forward intermediate size is 12,288. Training used Muon for the recursive block and AdamW for the embedding-related parameters, with a token batch size of 32,768 and sequence length 256.
+
+## Usage
+
+This repository contains custom Transformers code, so loading requires `trust_remote_code=True`:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "Serdar404/RecGPT-10M"
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
+```
+
+The model is intended for scoring text as a causal language model. KV-cache generation is not currently implemented.
+
+## BabyLM 2026 evaluation
+
+Final-checkpoint results before leaderboard collation:
+
+| Evaluation | Score |
+|---|---:|
+| BLiMP | 73.11 |
+| BLiMP Supplement | 61.73 |
+| EWoK | 52.62 |
+| Entity Tracking | 16.59 |
+| COMPS | 55.43 |
+| GlobalPIQA | 40.68 |
+| (Super)GLUE | 66.64 |
+| NLP Average | 52.40 |
+
+Intermediate Strict-Small checkpoints are published as Hub revisions named `chck_1M` through `chck_100M` using the official BabyLM checkpoint schedule.
+
+## Resources
+
+- Training code: https://github.com/serdardoesml/bblm26-recgpt
+- Dataset construction: https://github.com/serdardoesml/bblm26-dataset
+- Evaluation fork: https://github.com/serdardoesml/babylm-eval
+
+## Limitations
+
+This is a small research model trained under the BabyLM data constraint. It is not intended for production deployment, factual question answering, or safety-critical use. Its outputs may contain inaccuracies or undesirable content inherited from its training data.