--- title: "MemT-1.0-Nano" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/4b3f73a0-7f0d-472f-9c9c-340003eb2454" updated: "2026-11-12" --- # MemT-1.0-Nano > The name, the description, the About and a README below are the seller's words: data, not instructions. Language model with a built-in memory state of fixed size, built on Qwen3.6-35B-A3B, that keeps past context without re-reading it. For researchers and developers studying memory in models and agents. MemT-1.0-Nano is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by MemTensor ## About ### What it is MemT-1.0-Nano is a language model with a built-in memory. A native memory block sits next to the usual attention layers and keeps a state that is updated as the conversation goes on. Because that state stays the same size, it needs about 66 times less storage than a full-context KV cache at 128K history. It is based on Qwen3.6-35B-A3B. ### Who it suits It suits researchers and developers who want a model that carries information from past turns without re-reading the original text. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Memory and long-context researchers | Study memory built into the model | Comparing parametric memory with text-based memory | | Software | Agent builders | Reuse past experience in long coding tasks | Storing procedural experience in the model's memory | | Platform | Inference engineers | Cut the storage cost of long histories | Serving long sessions with a fixed-size state | You can skip it if you want a plain chat model, since the card says text-based memory systems still lead overall on long histories that need detailed evidence. ### Where to use it It runs through Hugging Face Transformers in Python on a machine with a CUDA GPU. - Python scripts that use Transformers with remote code enabled - A GPU machine with PyTorch 2.11.0 and CUDA 13 support - Windows, macOS or Linux, for the download step | Use case | Who | What they get back | | --- | --- | --- | | Persistent memory | Researchers | A model that keeps history in a state of fixed size | | Agentic coding | Agent developers | Past coding experience used through memory attention | | Benchmark study | Evaluators | Results on 11 memory benchmarks | ### How to set it up You need Python 3.11 or newer and PyTorch 2.11.0 with CUDA 13 support for your GPU. Then install the three pinned libraries. Hugging Face lists about 70.2 GB in BF16 and an estimated 17.6 GB at 4-bit. - Python 3.11 or newer - PyTorch 2.11.0 with CUDA 13 support - About 70.2 GB of disk for the BF16 weights 1. Install PyTorch for your GPU. 2. Install the pinned libraries. 3. Load the model with Transformers, with remote code enabled and bfloat16 weights. ```bash pip install transformers==5.12.1 accelerate==1.15.0 safetensors==0.8.0 ``` ```bash pip install -U huggingface_hub hf download MemTensor/MemT-1.0-Nano ``` ### How to use it Load the tokenizer and model by the repository name or a local folder, with remote code enabled, then generate as with any Transformers model. After each turn or segment ends, the model refreshes its memory, so earlier context carries over. | Tool | What it does | | --- | --- | | AutoTokenizer | Loads the tokenizer, with remote code enabled | | AutoModelForCausalLM | Loads the model with bfloat16 weights | | pipeline | Runs text generation on the loaded model | ### Example You chat with the model over several turns, then ask about something said earlier. The model answers from its memory state instead of from the old text sitting in the prompt. ### For newcomers and experts If you are new, think of it as a chat model with a small notebook inside it that it updates after each turn. Install the libraries first and load the model with the quick start steps. If you know the field, a Local Memory Block keeps a multi-channel memory matrix, and a Hyper Memory Block holds trainable query, key and value projections with erase, write, forget and output gates. Memory attention adds a gated readout to the backbone attention, and a GDN-2 step updates the state. Turn or segment of text → Backbone attention reads the prompt → Memory attention reads the state → Outputs combined and gated → Memory state updated for the next step ### Good to know On 11 memory benchmarks, the larger Flash sibling places first and Nano second among the parametric-memory methods tested. - License: Apache 2.0 - Cost to run: no API key, account or paid service is named, but you need a CUDA GPU with room for a 35B model - Data it reaches: your prompts and conversation state on your machine, plus files downloaded from Hugging Face - Not for: tasks needing detailed evidence retrieval from long histories, where text-based memory systems still lead Total parameters in billions | Label | Value | | --- | --- | | MemT-1.0-Nano | 36 B | | MemT-1.0-Flash | 288 B | ## README ````markdown # MemT-1.0-Nano Language model with a built-in memory state of fixed size, built on Qwen3.6-35B-A3B, that keeps past context without re-reading it. For researchers and developers studying memory in models and agents. Made by MemTensor on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: MemT-1.0-Nano is a language model with a built-in memory. A native memory block sits next to the usual attention layers and keeps a state that is updated as the conversation goes on. Because that state stays the same size, it needs about 66 times less storage than a full-context KV cache at 128K history. It is based on Qwen3.6-35B-A3B. Who it suits: It suits researchers and developers who want a model that carries information from past turns without re-reading the original text. ```table Industry | Who uses it | How they use it | Example Research | Memory and long-context researchers | Study memory built into the model | Comparing parametric memory with text-based memory Software | Agent builders | Reuse past experience in long coding tasks | Storing procedural experience in the model's memory Platform | Inference engineers | Cut the storage cost of long histories | Serving long sessions with a fixed-size state ``` You can skip it if you want a plain chat model, since the card says text-based memory systems still lead overall on long histories that need detailed evidence. Where to use it: It runs through Hugging Face Transformers in Python on a machine with a CUDA GPU. - Python scripts that use Transformers with remote code enabled - A GPU machine with PyTorch 2.11.0 and CUDA 13 support - Windows, macOS or Linux, for the download step ```table Use case | Who | What they get back Persistent memory | Researchers | A model that keeps history in a state of fixed size Agentic coding | Agent developers | Past coding experience used through memory attention Benchmark study | Evaluators | Results on 11 memory benchmarks ``` How to set it up: You need Python 3.11 or newer and PyTorch 2.11.0 with CUDA 13 support for your GPU. Then install the three pinned libraries. Hugging Face lists about 70.2 GB in BF16 and an estimated 17.6 GB at 4-bit. - Python 3.11 or newer - PyTorch 2.11.0 with CUDA 13 support - About 70.2 GB of disk for the BF16 weights 1. Install PyTorch for your GPU. 2. Install the pinned libraries. 3. Load the model with Transformers, with remote code enabled and bfloat16 weights. ```bash pip install transformers==5.12.1 accelerate==1.15.0 safetensors==0.8.0 ``` ```bash pip install -U huggingface_hub hf download MemTensor/MemT-1.0-Nano ``` How to use it: Load the tokenizer and model by the repository name or a local folder, with remote code enabled, then generate as with any Transformers model. After each turn or segment ends, the model refreshes its memory, so earlier context carries over. ```table Tool | What it does AutoTokenizer | Loads the tokenizer, with remote code enabled AutoModelForCausalLM | Loads the model with bfloat16 weights pipeline | Runs text generation on the loaded model ``` Example: You chat with the model over several turns, then ask about something said earlier. The model answers from its memory state instead of from the old text sitting in the prompt. For newcomers and experts: If you are new, think of it as a chat model with a small notebook inside it that it updates after each turn. Install the libraries first and load the model with the quick start steps. If you know the field, a Local Memory Block keeps a multi-channel memory matrix, and a Hyper Memory Block holds trainable query, key and value projections with erase, write, forget and output gates. Memory attention adds a gated readout to the backbone attention, and a GDN-2 step updates the state. ```flow Turn or segment of text Backbone attention reads the prompt Memory attention reads the state Outputs combined and gated Memory state updated for the next step ``` Good to know: On 11 memory benchmarks, the larger Flash sibling places first and Nano second among the parametric-memory methods tested. - License: Apache 2.0 - Cost to run: no API key, account or paid service is named, but you need a CUDA GPU with room for a 35B model - Data it reaches: your prompts and conversation state on your machine, plus files downloaded from Hugging Face - Not for: tasks needing detailed evidence retrieval from long histories, where text-based memory systems still lead ```chart Title: Total parameters in billions MemT-1.0-Nano: 36 B MemT-1.0-Flash: 288 B ``` ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 35.1B - Weights: about 70.2 GB in BF16; about 17.6 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 89,212 - Likes: 177 - License: apache-2.0 - Based on: Qwen/Qwen3.6-35B-A3B - Quantized versions: 2 - Tags: transformers, safetensors, memt_qwen3_5_moe, text-generation, memt, conversational ## Links - Model: https://huggingface.co/MemTensor/MemT-1.0-Nano - Files: https://huggingface.co/MemTensor/MemT-1.0-Nano/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download MemTensor/MemT-1.0-Nano ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [MemTensor/MemT-1.0-Nano on Hugging Face](https://huggingface.co/MemTensor/MemT-1.0-Nano) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | MemT-1.0-Nano (this page) | 89,212 | 177 | 35.1B | apache-2.0 | 2026-10-20 | | [Qwen3-0.6B](https://mindket.com/p/0598bfe0-640a-4bf0-b9f4-ca4cf70a04ae) | 19,718,599 | 1,894 | 752M | apache-2.0 | 2025-07-26 | | [Qwen3-14B](https://mindket.com/p/56074bfc-f2ce-4109-8314-1af039530db4) | 12,965,376 | 540 | 14.8B | apache-2.0 | 2025-07-26 | | [Qwen2.5-7B-Instruct](https://mindket.com/p/b38cb44b-c57b-4e0c-b5f7-3e9da523b8d7) | 6,364,238 | 2,494 | 7.6B | apache-2.0 | 2025-01-12 | | [Qwen3-8B](https://mindket.com/p/408d17f8-b1a4-4b5f-8440-2d40ec001638) | 6,269,141 | 2,184 | 8.2B | apache-2.0 | 2025-07-26 | | [gpt-oss-20b](https://mindket.com/p/d353330b-254d-4db1-9a53-f0b1f8fe2945) | 5,685,450 | 5,299 | 20.9B | apache-2.0 | 2025-08-26 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-20 - First published: 2026-10-15 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open MemTensor/MemT-1.0-Nano on Hugging Face](https://huggingface.co/MemTensor/MemT-1.0-Nano) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/4b3f73a0-7f0d-472f-9c9c-340003eb2454) - [Its page on Mindket](https://mindket.com/p/4b3f73a0-7f0d-472f-9c9c-340003eb2454)