princeton-nlp/RMT-1.3b-30k

license: apache-2.0

Paper: Adapting Language Models to Compress Contexts

Code: https://github.com/princeton-nlp/AutoCompressors

Models:

• Llama-2-7b fine-tuned models: AutoCompressor-Llama-2-7b-6k, FullAttention-Llama-2-7b-6k
• OPT-2.7b fine-tuned models: AutoCompressor-2.7b-6k, AutoCompressor-2.7b-30k, RMT-2.7b-8k, FullAttention-2.7b-4k
• OPT-1.3b fine-tuned models: AutoCompressor-1.3b-30k, RMT-1.3b-30k

---

RMT-1.3b-30k is a model fine-tuned from facebook/opt-1.3b following the RMT method as described in Recurrent Memory Transformer and Adapting Language Models to Compress Contexts. This model is fine-tuned on 2B tokens from Books3 in The Pile. 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 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}
}