MemTensor · Memory LLM

MemT-1.0-Nano

The smaller model in MemTensor's MemT-1.0 family, which builds persistent memory into the model itself. A Native Memory Block next to attention keeps a fixed-size memory state across turns, so past conversation can be dropped from the context; MemTensor says this cuts storage about 66x versus a full KV cache at 128K history. Nano is fine-tuned from Qwen3.6-35B-A3B with 36B total and 3B active parameters, 451M trainable memory parameters and a 10M memory state, trained on 34B tokens. Weights are Apache 2.0 on Hugging Face (uploaded 15 October). On 11 memory benchmarks it ranks second among parametric-memory methods, behind MemT-1.0-Flash (self-reported).