README.md

---
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}
}
```