
### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/NathanGodey/headless-lm
- **Paper:** https://arxiv.org/abs/2309.08351


### Model Architecture and Objective

This model is a Pythia-70m architecture trained on OpenWebText-2 using the Contrastive Weight Tying objective, and briefly fine-tuned for language generation on the same dataset.

## Citation

**BibTeX:**

```bibtex
@misc{godey2023headless,
      title={Headless Language Models: Learning without Predicting with Contrastive Weight Tying}, 
      author={Nathan Godey and Éric de la Clergerie and Benoît Sagot},
      year={2023},
      eprint={2309.08351},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


## Contact

nathan.godey@inria.fr
{"language": ["en"], "license": "mit", "datasets": ["the_pile_openwebtext2"], "pipeline_tag": "text-generation"}
transformers pytorch gpt_neox text-generation en dataset:the_pile_openwebtext2 arxiv:2309.08351 license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt_neox #text-generation #en #dataset-the_pile_openwebtext2 #arxiv-2309.08351 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


### Model Sources



- Repository: URL
- Paper: URL


### Model Architecture and Objective

This model is a Pythia-70m architecture trained on OpenWebText-2 using the Contrastive Weight Tying objective, and briefly fine-tuned for language generation on the same dataset.

BibTeX:




## Contact

URL@URL
### Model Sources



- Repository: URL
- Paper: URL ### Model Architecture and Objective

This model is a Pythia-70m architecture trained on OpenWebText-2 using the Contrastive Weight Tying objective, and briefly fine-tuned for language generation on the same dataset.

BibTeX: ## Contact

URL@URL
TAGS
#transformers #pytorch #gpt_neox #text-generation #en #dataset-the_pile_openwebtext2 #arxiv-2309.08351 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Model Sources



- Repository: URL
- Paper: URL ### Model Architecture and Objective

This model is a Pythia-70m architecture trained on OpenWebText-2 using the Contrastive Weight Tying objective, and briefly fine-tuned for language generation on the same dataset.

BibTeX: ## Contact

URL@URL