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Rubin-Wei/MemoryDecoder-Pythia-6.9B-general

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MemoryDecoder-Pythia-6.9B-general

Resources

This repository contains the 6.9B general Memory Decoder released with Memory Decoder at Scale. It is a pretrained parametric long-term memory designed to be paired with a frozen Pythia-family language model. The general-memory suite was pretrained on 300B tokens and scales memory capacity independently from the backbone.

This checkpoint is a memory component, not a chat- or instruction-tuned model. Use it through the Memory Decoder integration in the released codebase rather than treating it as a standalone assistant.

Model details

Field	Value
Memory size	approximately 6.9B parameters
Architecture/tokenizer family	GPT-NeoX / Pythia
Memory scope	General
Training scale	300B-token general-memory pretraining suite
Intended backbone	Frozen, tokenizer-compatible Pythia model
Release contents	Inference weights, configuration, and tokenizer files

Usage

Install the environments from LUMIA-Group/MemoryDecoder-at-Scale, then use the hf-memdec adapter. For example:

bash

cd eval/lm-evaluation-harness

BACKBONE=EleutherAI/pythia-410m-deduped
MEMORY=Rubin-Wei/MemoryDecoder-Pythia-6.9B-general

lm-eval \
  --model hf-memdec \
  --model_args pretrained=$BACKBONE,memdec_path=$MEMORY \
  --tasks arc_easy,piqa,mmlu \
  --batch_size 1

The adapter provides the task-specific interpolation settings used for the released Pythia memories; an explicit lmbda can be supplied as an override.

Intended use and limitations

This checkpoint is intended for research on parametric memory, memory scaling, and evaluation with frozen language-model backbones. Its behavior depends on the selected backbone and interpolation settings. It may reproduce biases or errors present in its training data and should not be treated as an authoritative knowledge source.

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},
}