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How to compare replies with native memory on and off with MemT 1 0

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

  • Research
  • Intermediate
  • 1 hour 30 minutes
  • From the README
  • Any agent

Who it is forA researcher or evaluator testing a memory model who needs a plain baseline next to memory-enabled answers.

The problemWithout a baseline, it is hard to tell whether a good answer came from native memory or from the model's general knowledge.

The outcomeTwo answers to the same prompt, one with memory reading on and one with it off, that can be compared side by side.

What you need first

  • 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

Steps

  1. Start the Nano server with sh start_memt_nano.sh /models/MemT-1.0-Nano on the GPU host.
  2. Create a session with MemTSession(client, chat_format, parser) and store a fact using enable_memory=True.
  3. In a fresh session, call chat.set_memory_read(False) and ask the same fact-based question with enable_memory=False.
  4. Repeat the question in a session where memory reading is on, and record both answers.

Prompt for your agent

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.

Caveat. The README reports benchmark results for 11 memory benchmarks, but this repo's examples are small illustrations and do not reproduce those evaluations.

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