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README.md
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---
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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
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# Doc / guide: https://huggingface.co/docs/hub/model-cards
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{}
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---
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# Model Card for *LocalShuffle(w=3)* GPT-2 (without Positional Encodings)
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<!-- Provide a quick summary of what the model is/does. -->
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This is one model in a collection of models trained on the impossible
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languages of [Kallini et al. 2024](https://arxiv.org/abs/2401.06416).
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This model is a GPT-2 Small model trained *without positional encodings*
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from scratch on the ***LocalShuffle(w=3)***
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language. We include a total of 30 checkpoints over the course of
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model training, from step 100 to 3000 in increments of 100 steps.
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The main branch contains the final checkpoint (3000), and the other
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checkpoints are accessible as revisions.
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![languages.png](https://cdn-uploads.huggingface.co/production/uploads/6268bc06adb1c6525b3d5157/pBt38YYQL1gj8DqjyorWS.png)
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## Model Details
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- **Developed by:** Julie Kallini, Isabel Papadimitriou, Richard Futrell, Kyle Mahowald, Christopher Potts
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- **Model type:** Causal Language Model
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- **Language(s) (NLP):** English
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- **GitHub Repository:** https://github.com/jkallini/mission-impossible-language-models
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- **Paper:** https://arxiv.org/pdf/2401.06416
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## Uses
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This artefact is solely intended for the study of language learning
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and acquisition in computational models. It should not be
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used in any production setting.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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**Important:** This will download our modified GPT-2 code that does
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not have absolute positional encodings. If using this model in the
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same environment as another GPT-2 model with positional encodings,
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load the second model as a `GPT2Model` explicitly.
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```python
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from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
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import torch
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# Load model and tokenizer
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model_id = "mission-impossible-lms/local-shuffle-w3-gpt2-no-pos"
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model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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# Set up the prompt and encode it
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prompt = "He clean"
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inputs = tokenizer(prompt, return_tensors="pt")
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# Generate text
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output = model.generate(inputs.input_ids, max_length=20)
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# Decode and print the generated text
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generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
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print(generated_text)
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```
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By default, the `main` branch of this model repo loads the
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last model checkpoint (3000). To access the other checkpoints,
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use the `revision` argument:
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```
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model = GPT2LMHeadModel.from_pretrained(model_id, revision="checkpoint-500")
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```
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This loads the model at checkpoint 500.
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## Training Details
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### Training Data
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This model was trained on the [100M-word BabyLM dataset](https://babylm.github.io/).
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Before training, we first transform the dataset into
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the corresponding impossible language, as described in
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our paper.
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### Training Procedure
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This model was trained for 3,000 gradient steps with
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a batch size of 2^19 tokens. We train with a learning
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rate that linearly warms up from 0 to 6e-4 over 300 steps.
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## Environmental Impact
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- **Hardware Type:** NVIDIA RTX 3090 (24GB) + NVIDIA RTX A6000 (48GB) GPUs.
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- **Hours used:** ~24 hours.
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## Citation
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```bibtex
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@inproceedings{kallini-etal-2024-mission,
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title = "Mission: Impossible Language Models",
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author = "Kallini, Julie and
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Papadimitriou, Isabel and
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Futrell, Richard and
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Mahowald, Kyle and
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Potts, Christopher",
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editor = "Ku, Lun-Wei and
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Martins, Andre and
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Srikumar, Vivek",
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booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
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month = aug,
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year = "2024",
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address = "Bangkok, Thailand",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2024.acl-long.787",
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doi = "10.18653/v1/2024.acl-long.787",
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pages = "14691--14714",
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}
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```
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## Model Card Authors
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Julie Kallini
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## Model Card Contact
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