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What you can do with MemT 1 0

MemT-1.0 is an official research repository for a memory foundation model that keeps persistent, evolving memory inside the model's computation, served through SGLang with a Python client.

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MemTensor/MemT-1.0 on GitHub

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What do you want to do?

3 use cases

  • Run a chat that remembers facts across turns

    Serve MemT-1.0-Nano with SGLang and chat with native memory that is kept after each reply.

    View prompt
    Use the MemTensor/MemT-1.0 repo to start the MemT-1.0-Nano server from /models/<checkpoint-folder> and run the Quick Start chat example. Expected output: a reply that restates the kitten name, age and favorite food from earlier turns. Verify that the answer names facts that were given only in the first message.

  • Save a memory snapshot and resume it in a new session

    Store a model's memory state to a file and restore it later without the old chat transcript.

    View prompt
    Using the MemTensor/MemT-1.0 repo and a running MemT-1.0-Nano server, save memory from a session to <snapshot-name>.safetensors, then restore it in a new MemTSession. Expected output: restored.messages has length 0 and a reply to a fact-based question is correct. Verify the returned snapshot path is used as the state argument.

  • Compare replies with native memory on and off

    Run the same question with memory reading disabled to see what the memory layer changes.

    View prompt
    Use the MemTensor/MemT-1.0 repo with a running MemT-1.0-Nano server to ask <question> with memory reading on and off. Expected output: two answers, where only the memory-on answer uses facts from the saved conversation. Verify that the memory-off session was created without restoring a snapshot.

How to use MemT 1 0

  1. Check you have what it needs: Linux server with NVIDIA CUDA and Python 3.11 or newer; uv installed for the inference install step; A complete local MemT-1.0-Nano checkpoint with BF16 weights, configuration and tokenizer assets.
  2. Open MemTensor/MemT-1.0 on GitHub and read the README and license.
  3. Run cd inference in a clone of the MemTensor/MemT-1.0 repository.
  4. Run uv pip install '.[server]' on the Linux GPU server.
  5. Run sh start_memt_nano.sh /models/MemT-1.0-Nano and keep it running; it serves at http://127.0.0.1:30000.
  6. In another terminal, run the Python connection code from the Quick Start, pointing model_path at the same checkpoint.

These steps come from our top use case, Run a chat that remembers facts across turns, which also has a copy-paste agent prompt.

MemT 1 0 alternatives · Best AI repos for Research

About this repo

  • Who it is forWritten for ML engineers and researchers who can run a Linux GPU server and want to test persistent model memory in conversations.
  • Why it is hot
  • LanguagePython
  • Sourcegithub.com/MemTensor/MemT-1.0 · Back to Hot repos

Requirements

  • Linux server with NVIDIA CUDA and Python 3.11 or newer
  • uv installed for the inference install step
  • A complete local MemT-1.0-Nano checkpoint with BF16 weights, configuration and tokenizer assets
  • Two GPUs for the Nano example in the README
  • Write access to ./memt-state on the server for memory snapshots
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Questions about MemT 1 0

What is MemT 1 0?

MemT-1.0 is an official research repository for a memory foundation model that keeps persistent, evolving memory inside the model's computation, served through SGLang with a Python client. It is an open-source project on GitHub: https://github.com/MemTensor/MemT-1.0.

Who is MemT 1 0 for?

Written for ML engineers and researchers who can run a Linux GPU server and want to test persistent model memory in conversations.

What can I do with MemT 1 0?

3 use cases so far, for example: Run a chat that remembers facts across turns; Save a memory snapshot and resume it in a new session; Compare replies with native memory on and off. Each has steps and a copy-paste agent prompt.

What do I need to run MemT 1 0?

Linux server with NVIDIA CUDA and Python 3.11 or newer; uv installed for the inference install step; A complete local MemT-1.0-Nano checkpoint with BF16 weights, configuration and tokenizer assets; Two GPUs for the Nano example in the README.

What are alternatives to MemT 1 0?

Other hot AI repos in Research: Scientific Agent Skills, Speaker Mem R1, DSH Council, Evo Bot, AI Engineering Course.

Is MemT 1 0 safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.