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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)
---
AutoCompressor-2.7b-6k is a model fine-tuned from [facebook/opt-2.7b](https://huggingface.co/facebook/opt-2.7b) following the AutoCompressor method in [Adapting Language Models to Compress Contexts](https://arxiv.org/abs/2305.14788).
This model is fine-tuned on 2B tokens from [The Pile](https://pile.eleuther.ai). The pre-trained OPT-2.7b model is fine-tuned on sequences of 6,144 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/AutoCompressor-2.7b-6k")
```
**Evaluation**
We record the perplexity achieved by our OPT-2.7b models on segments of 2048 tokens, conditioned on different amounts of context.
FullAttention-2.7-4k uses full uncompressed contexts whereas AutoCompressor-2.7b-6k and RMT-2.7b-8k compress segments of 2048 tokens into 50 summary vectors.
*In-domain Evaluation*
| Context Tokens | 0 |512 | 2048 | 4096 | 6144 |
| -----------------------------|-----|-----|------|------|------|
| FullAttention-2.7b-4k | 6.57|6.15 |5.94 |- |- |
| RMT-2.7b-8k | 6.34|6.19 |6.02 | 6.02 | 6.01 |
| AutoCompressor-2.7b-6k | 6.31|6.04 | 5.98 | 5.94 | 5.93 |
*Out-of-domain Evaluation*
| Context Tokens | 0 |512 | 2048 | 4096 | 6144 |
| -----------------------------|-----|-----|------|------|------|
| FullAttention-2.7b-4k | 8.94|8.28 |7.93 |- |- |
| RMT-2.7b-8k | 8.62|8.44 |8.21 | 8.20 | 8.20 |
| AutoCompressor-2.7b-6k | 8.60|8.26 | 8.17 | 8.12 | 8.10 |
See [Adapting Language Models to Compress Contexts](https://arxiv.org/abs/2305.14788) for more evaluations, including evaluation on 11 in-context learning tasks.
## 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}
}
``` |