Create README.md

README.md

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+---
+license: apache-2.0
+---
+license: apache-2.0
+---
+
+**Paper**: [Adapting Language Models to Compress Contexts](https://arxiv.org/abs/2305.14788)  
+
+**Code**: https://github.com/princeton-nlp/AutoCompressors
+
+**Models**: 
+- Llama-2-7b fine-tuned models: [AutoCompressor-Llama-2-7b-6k](https://huggingface.co/princeton-nlp/AutoCompressor-Llama-2-7b-6k/), [FullAttention-Llama-2-7b-6k](https://huggingface.co/princeton-nlp/FullAttention-Llama-2-7b-6k)
+- OPT-2.7b fine-tuned models:  [AutoCompressor-2.7b-6k](https://huggingface.co/princeton-nlp/AutoCompressor-2.7b-6k), [AutoCompressor-2.7b-30k](https://huggingface.co/princeton-nlp/AutoCompressor-2.7b-30k), [RMT-2.7b-8k](https://huggingface.co/princeton-nlp/RMT-2.7b-8k), [FullAttention-2.7b-4k](https://huggingface.co/princeton-nlp/FullAttention-2.7b-4k)
+- OPT-1.3b fine-tuned models: [AutoCompressor-1.3b-30k](https://huggingface.co/princeton-nlp/AutoCompressor-1.3b-30k), [RMT-1.3b-30k](https://huggingface.co/princeton-nlp/RMT-1.3b-30k)
+
+---
+
+RMT-1.3b-30k is a model fine-tuned from [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) following the RMT method as described in [Recurrent Memory Transformer](https://arxiv.org/abs/2207.06881) and [Adapting Language Models to Compress Contexts](https://arxiv.org/abs/2305.14788). 
+This model is fine-tuned on 2B tokens from Books3 in [The Pile](https://pile.eleuther.ai). The pre-trained OPT-1.3b model is fine-tuned on sequences of 30,720 tokens with 50 summary vectors, summary accumulation, randomized segmenting, and stop-gradients. 
+
+To get started, download the [`AutoCompressor`](https://github.com/princeton-nlp/AutoCompressors) repository and load the model as follows:
+
+```
+from auto_compressor import AutoCompressorModel
+
+model = AutoCompressorModel.from_pretrained("princeton-nlp/RMT-1.3b-30k")
+``` 
+
+**Evaluation**
+
+We record the perplexity achieved by our 30k-fine-tuned OPT models on segments of 2,048 tokens sampled from Books3 and ArXiv in The Pile, conditioned on different amounts of context. 
+
+
+| Context Tokens               | 0    |14,336  | 28,672 |
+| -----------------------------|------|--------|--------|
+| RMT-1.3b-30k                 | 13.18|12.50   |12.50   |
+| AutoCompressor-1.3b-30k      | 13.21|12.49   |12.47   |
+| AutoCompressor-2.7b-30k      | 11.86|11.21   |11.18   |
+
+
+
+
+## Bibtex
+```
+@misc{chevalier2023adapting,
+      title={Adapting Language Models to Compress Contexts}, 
+      author={Alexis Chevalier and Alexander Wettig and Anirudh Ajith and Danqi Chen},
+      year={2023},
+      eprint={2305.14788},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```