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README.md

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+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+{}
+---
+
+# Model Card for DeterministicShuffle(s=84) GPT-2
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This is one model in a collection of models trained on the impossible
+languages of [Kallini et al. 2024](https://arxiv.org/abs/2401.06416).
+
+This model is a GPT-2 Small model trained from scratch on the *DeterministicShuffle(s=84)*
+language. We include a total of 30 checkpoints over the course of
+model training, from step 100 to 3000 in increments of 100 steps.
+The main branch contains the final checkpoint (3000), and the other
+checkpoints are accessible as revisions.
+
+![languages.png](https://cdn-uploads.huggingface.co/production/uploads/6268bc06adb1c6525b3d5157/pBt38YYQL1gj8DqjyorWS.png)
+
+## Model Details
+
+- **Developed by:** Julie Kallini, Isabel Papadimitriou, Richard Futrell, Kyle Mahowald, Christopher Potts
+- **Model type:** Causal Language Model
+- **Language(s) (NLP):** English
+- **GitHub Repository:** https://github.com/jkallini/mission-impossible-language-models
+- **Paper:** https://arxiv.org/pdf/2401.06416
+
+## Uses
+
+This artefact is solely intended for the study of language learning
+and acquisition in computational models. It should not be
+used in any production setting.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import torch
+
+# Load model and tokenizer
+model_id = "mission-impossible-lms/deterministic-shuffle-s84-gpt2"
+model = GPT2LMHeadModel.from_pretrained(model_id)
+tokenizer = GPT2Tokenizer.from_pretrained(model_id)
+
+# Set up the prompt and encode it
+prompt = "He clean"
+inputs = tokenizer(prompt, return_tensors="pt")
+
+# Generate text
+output = model.generate(inputs.input_ids, max_length=20)
+
+# Decode and print the generated text
+generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+print(generated_text)
+```
+
+By default, the `main` branch of this model repo loads the
+last model checkpoint (3000). To access the other checkpoints,
+use the `revision` argument:
+
+```
+model = GPT2LMHeadModel.from_pretrained(model_id, revision="checkpoint-500")
+```
+This loads the model at checkpoint 500.
+
+## Training Details
+
+### Training Data
+
+This model was trained on the [100M-word BabyLM dataset](https://babylm.github.io/).
+Before training, we first transform the dataset into
+the corresponding impossible language, as described in
+our paper.
+
+### Training Procedure
+
+This model was trained for 3,000 gradient steps with
+a batch size of 2^19 tokens. We train with a learning
+rate that linearly warms up from 0 to 6e-4 over 300 steps.
+
+## Environmental Impact
+
+- **Hardware Type:** NVIDIA RTX 3090 (24GB) + NVIDIA RTX A6000 (48GB) GPUs.
+- **Hours used:** ~24 hours.
+
+## Citation 
+
+```bibtex
+@inproceedings{kallini-etal-2024-mission,
+    title = "Mission: Impossible Language Models",
+    author = "Kallini, Julie  and
+      Papadimitriou, Isabel  and
+      Futrell, Richard  and
+      Mahowald, Kyle  and
+      Potts, Christopher",
+    editor = "Ku, Lun-Wei  and
+      Martins, Andre  and
+      Srikumar, Vivek",
+    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = aug,
+    year = "2024",
+    address = "Bangkok, Thailand",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.acl-long.787",
+    doi = "10.18653/v1/2024.acl-long.787",
+    pages = "14691--14714",
+}
+```
+
+## Model Card Authors
+
+Julie Kallini
+
+## Model Card Contact
+
+kallini@stanford.edu