MemTensor/MemT-1.0

选用指南

AI 根据 README 整理 · 2026/10/20 11:01:34

使用方式

通过 SGLang 服务器部署模型，并使用 Python 客户端进行对话和记忆管理。

查看 README 依据

The examples below serve MemT-1.0-Nano with SGLang and use the `memt_sglang` Python client for conversations with native memory. For deployment details and MemT-1.0-Flash setup, see the inference guide in [English](inference/README.md) or [简体中文](inference/README_zh.md). ### Installation On a Linux server with NVIDIA CUDA and Python 3.11 or newer, install from the repository's `inference` directory: ```bash cd inference uv pip install '.[server]' ``` If the Python client runs in a separate environment, install the client extra from the same directory in that environment: ```bash

uv pip install '.[client]' ``` ### Start the Server Use a complete local MemT-1.0-Nano checkpoint with matching BF16 weights, configuration and tokenizer assets: ```bash sh start_memt_nano.sh /models/MemT-1.0-Nano ``` This example uses two GPUs and serves at `http://127.0.0.1:30000`, with memory snapshots stored in `./memt-state` on the server. Keep the server running and run the Python examples below in another terminal. For Flash, use [start_memt_flash.sh](inference/start_memt_flash.sh) and the Flash client setup described in the [inference guide](inference/README.md#python-client).

部署要求

需要 Linux 服务器、NVIDIA CUDA 和 Python 3.11 或更高版本。

查看 README 依据

The examples below serve MemT-1.0-Nano with SGLang and use the `memt_sglang` Python client for conversations with native memory. For deployment details and MemT-1.0-Flash setup, see the inference guide in [English](inference/README.md) or [简体中文](inference/README_zh.md). ### Installation On a Linux server with NVIDIA CUDA and Python 3.11 or newer, install from the repository's `inference` directory: ```bash cd inference uv pip install '.[server]' ``` If the Python client runs in a separate environment, install the client extra from the same directory in that environment: ```bash

已知限制

仓库软件采用 PolyForm 非商业许可证，不允许商业使用。

查看 README 依据

- **Repository software:** Original source code in this repository, including the SGLang integration, is licensed under the [PolyForm Noncommercial License 1.0.0](LICENSE). Bundled third-party components retain their own licenses. The SGLang integration includes flash-linear-attention code under MIT; see the [component license table](inference/README.md#licensing). Commercial use of the repository software is not permitted under the PolyForm Noncommercial License. For commercial licensing inquiries, please contact the authors.