{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "memtensor-memt-1-0--compare-memory-on-and-off-replies",
 "repo": "MemTensor/MemT-1.0",
 "repo_url": "https://github.com/MemTensor/MemT-1.0",
 "repo_slug": "memtensor-memt-1-0",
 "title": "Compare replies with native memory on and off",
 "page_title": "How to compare replies with native memory on and off with MemT 1 0",
 "summary": "Run the same question with memory reading disabled to see what the memory layer changes.",
 "who": "A researcher or evaluator testing a memory model who needs a plain baseline next to memory-enabled answers.",
 "problem": "Without a baseline, it is hard to tell whether a good answer came from native memory or from the model's general knowledge.",
 "outcome": "Two answers to the same prompt, one with memory reading on and one with it off, that can be compared side by side.",
 "steps": [
  "Start the Nano server with `sh start_memt_nano.sh /models/MemT-1.0-Nano` on the GPU host.",
  "Create a session with `MemTSession(client, chat_format, parser)` and store a fact using `enable_memory=True`.",
  "In a fresh session, call `chat.set_memory_read(False)` and ask the same fact-based question with `enable_memory=False`.",
  "Repeat the question in a session where memory reading is on, and record both answers."
 ],
 "agent_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.",
 "agents": [
  "any"
 ],
 "difficulty": "Intermediate",
 "time": "1 hour 30 minutes",
 "basis": "readme",
 "caveat": "The README reports benchmark results for 11 memory benchmarks, but this repo's examples are small illustrations and do not reproduce those evaluations.",
 "category": "Research",
 "tags": [
  "Research"
 ],
 "lang": "python",
 "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"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.8,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/memtensor-memt-1-0/compare-memory-on-and-off-replies/",
 "md": "https://dedupedai.com/use-cases/memtensor-memt-1-0/compare-memory-on-and-off-replies/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/memtensor-memt-1-0--compare-memory-on-and-off-replies.json",
 "api_version": "v1"
}