DeepSeek's Engram Module Made Tokenizer-Agnostic

Researchers have developed a tokenizer-agnostic engram module for large language models, building upon DeepSeek's original design. The new approach replaces XOR-based hashing with polynomial hashing, creating a joint embedding space that allows Engram embeddings to be reused across models with different tokenizers. This modification enables hash equivalence for byte-equivalent token sequences, maintaining comparable performance while improving the reusability of Engram embeddings. AI

IMPACT Enhances the reusability of memory modules in LLMs, potentially reducing training costs and improving model adaptability.

RANK_REASON The cluster describes a modification to an existing AI module presented in an academic paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• DeepSeek
• engram
• n-gram
• polynomial hashing

• paper
• model release

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