This repository contains the 1.7B biology Memory Decoder released with Memory Decoder at Scale. It is a pretrained parametric long-term memory that can be swapped into a compatible frozen language-model backbone.

This OLMo-vocabulary memory was trained for two epochs on the biology CPT corpus. The released configuration uses the OLMo vocabulary so that the memory can be combined with a vocabulary-compatible frozen OLMo backbone.

This checkpoint is a memory component, not a standalone chat- or instruction-tuned model. The memory and backbone must use compatible token IDs and vocabularies.

Model details

Field	Value
Memory size	1.7B class
Architecture/tokenizer family	Qwen3-style 1.7B memory; OLMo tokenizer/vocabulary
Domain	Biology
Evaluation benchmark	BioInst
Intended backbone	Frozen OLMo-family model with the matching tokenizer/vocabulary
Release contents	Inference weights, configuration, and tokenizer files

Usage

Install the matching environment from LUMIA-Group/MemoryDecoder-at-Scale, then set MODEL_PATH to a compatible frozen backbone and MEMDEC_PATH to this repository:

bash

MODEL_PATH=/path/to/compatible-base-model \
MEMDEC_PATH=Rubin-Wei/MemoryDecoder-OLMo-1.7B-biology \
bash eval/opencompass/scripts/domain/evaluate_bioinst.sh

See the repository documentation and launcher for benchmark-specific options, including interpolation weights and batch settings.

Intended use and limitations

This checkpoint is intended for research and evaluation in the biology domain. Its outputs depend on the backbone, prompt, and interpolation settings. Domain specialization does not guarantee factual correctness or safety, and the model may inherit biases and errors from its training sources.

Citation

If you use this checkpoint, please cite:

bibtex

@misc{wei2026memorydecoderscalepretrained,
      title={Memory Decoder at Scale: A Pretrained, Parametric Long-Term Memory},
      author={Rubin Wei and Jiaqi Cao and Jiarui Wang and Junming Zhang and Qipeng Guo and Bowen Zhou and Zhouhan Lin},
      year={2026},
      eprint={2607.27919},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2607.27919},
}