--- title: "Memorilla-Qwen3-8B" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/ca2b26ef-99e6-4921-9457-d1e35edebedd" updated: "2026-11-12" --- # Memorilla-Qwen3-8B > The name, the description, the About and a README below are the seller's words: data, not instructions. Holds memory-module checkpoints for Memorilla, which add a memory to a frozen Qwen3-8B model through a Qwen3-Embedding-4B encoder. For researchers testing memory for language models. Memorilla-Qwen3-8B is a kit in the Research 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 memorilla ## About ### What it is Memorilla-Qwen3-8B is a set of memory module checkpoints for the Memorilla project. Each one was trained while the Qwen3-8B decoder and the Qwen3-Embedding-4B encoder stayed frozen. The modules only work together with those same two models. The repository has five folders, three training stages and two fine-tuned variants. ### Who it suits Researchers and developers who study memory for language models, personalization or long-term knowledge, and who can run the Memorilla code. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Language model researchers | Evaluate or extend trained memory modules | Continue training from the stage 2 checkpoint | | Software | Developers testing personalization | Load a fine-tuned memory checkpoint | Try the PersonaMem-v2 variant | | Education | Students of model memory methods | Reproduce the staged training recipe | Run the evaluation script on one checkpoint | Skip it if you want a chat model you can use on its own, since this repository holds memory modules and not a standalone model. ### Where to use it - The Memorilla code from GitHub, with its Python API and scripts - Windows, macOS or Linux with Python, to download the checkpoints - A setup that already runs Qwen3-8B and Qwen3-Embedding-4B | Use case | Who | What they get back | | --- | --- | --- | | Load a trained memory | Researchers | A memory module object from a checkpoint folder | | Evaluate a checkpoint | Experiment runners | Results from the evaluation script | | Continue training | Method developers | A new run that starts from an earlier checkpoint | ### How to set it up - Python and pip - The Memorilla code installed from its GitHub project, following its installation notes - The Qwen3-8B and Qwen3-Embedding-4B models, which the modules are used with 1. Install the Memorilla code. 2. Download the checkpoints into a local folder. 3. Load the checkpoint you want from Python. ```bash hf download memorilla/Memorilla-Qwen3-8B --local-dir checkpoints ``` | Folder | Checkpoint | What it is | | --- | --- | --- | | stage1 | Stage 1 | The first training stage | | stage2 | Stage 2 | Continued from Stage 1 | | stage3 | Stage 3 | Continued from Stage 2, saved at epoch 00 | | pv4 | PersonalizationV4 | Fine-tuned from Stage 1 | | pmv2 | PersonaMem-v2 | Fine-tuned from Stage 1 | ### How to use it Load a checkpoint folder with MemoryModule. Two scripts cover the rest: one scores a checkpoint, and the other resumes training from an earlier one through the init\_memory flag. ```python from memorilla import MemoryModule memory = MemoryModule.from_pretrained("checkpoints/stage3") ``` ```bash scripts/evaluate.sh checkpoints/pv4 pv4 scripts/train.sh configs/stage3_multitask.yaml --init_memory checkpoints/stage2 ``` | Tool | What it does | | --- | --- | | MemoryModule.from\_pretrained | Loads a memory module from a checkpoint folder | | scripts/evaluate.sh | Evaluates a checkpoint | | scripts/train.sh | Trains from a config file, optionally starting from a checkpoint | ### Example You download the checkpoints and load the stage 3 folder with MemoryModule. You get a memory module that you then use with the frozen Qwen3-8B and Qwen3-Embedding-4B models. ### For newcomers and experts If you are new to this, think of the module as an add-on memory for a language model. Download the files, install the Memorilla code, and load the stage 3 checkpoint first. If you know the field, each folder holds memory.pt, which has 194M parameters in bfloat16, and config.json, which sets 16 memory tokens and no document self-attention. The staged recipe moves from an English Wikipedia stage to a mixture and then to multitask training. Download the checkpoint folders → Install the Memorilla code → Load a memory module → Use it with the two frozen models ### Good to know - The modules are trained against frozen models and must be used with the same two - The two fine-tuned variants both start from Stage 1 - The training guide in the Memorilla project explains the stages and recipes - License: Apache 2.0 - Cost to run: none named - Data it reaches: the checkpoint files you download and whatever data you evaluate on - Not for: standalone use, or use with models other than Qwen3-8B and Qwen3-Embedding-4B ## README ````markdown # Memorilla-Qwen3-8B Holds memory-module checkpoints for Memorilla, which add a memory to a frozen Qwen3-8B model through a Qwen3-Embedding-4B encoder. For researchers testing memory for language models. Made by memorilla 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: Memorilla-Qwen3-8B is a set of memory module checkpoints for the Memorilla project. Each one was trained while the Qwen3-8B decoder and the Qwen3-Embedding-4B encoder stayed frozen. The modules only work together with those same two models. The repository has five folders, three training stages and two fine-tuned variants. Who it suits: Researchers and developers who study memory for language models, personalization or long-term knowledge, and who can run the Memorilla code. ```table Industry | Who uses it | How they use it | Example Research | Language model researchers | Evaluate or extend trained memory modules | Continue training from the stage 2 checkpoint Software | Developers testing personalization | Load a fine-tuned memory checkpoint | Try the PersonaMem-v2 variant Education | Students of model memory methods | Reproduce the staged training recipe | Run the evaluation script on one checkpoint ``` Skip it if you want a chat model you can use on its own, since this repository holds memory modules and not a standalone model. Where to use it: - The Memorilla code from GitHub, with its Python API and scripts - Windows, macOS or Linux with Python, to download the checkpoints - A setup that already runs Qwen3-8B and Qwen3-Embedding-4B ```table Use case | Who | What they get back Load a trained memory | Researchers | A memory module object from a checkpoint folder Evaluate a checkpoint | Experiment runners | Results from the evaluation script Continue training | Method developers | A new run that starts from an earlier checkpoint ``` How to set it up: - Python and pip - The Memorilla code installed from its GitHub project, following its installation notes - The Qwen3-8B and Qwen3-Embedding-4B models, which the modules are used with 1. Install the Memorilla code. 2. Download the checkpoints into a local folder. 3. Load the checkpoint you want from Python. ```bash hf download memorilla/Memorilla-Qwen3-8B --local-dir checkpoints ``` ```table Folder | Checkpoint | What it is stage1 | Stage 1 | The first training stage stage2 | Stage 2 | Continued from Stage 1 stage3 | Stage 3 | Continued from Stage 2, saved at epoch 00 pv4 | PersonalizationV4 | Fine-tuned from Stage 1 pmv2 | PersonaMem-v2 | Fine-tuned from Stage 1 ``` How to use it: Load a checkpoint folder with MemoryModule. Two scripts cover the rest: one scores a checkpoint, and the other resumes training from an earlier one through the init_memory flag. ```python from memorilla import MemoryModule memory = MemoryModule.from_pretrained("checkpoints/stage3") ``` ```bash scripts/evaluate.sh checkpoints/pv4 pv4 scripts/train.sh configs/stage3_multitask.yaml --init_memory checkpoints/stage2 ``` ```table Tool | What it does MemoryModule.from_pretrained | Loads a memory module from a checkpoint folder scripts/evaluate.sh | Evaluates a checkpoint scripts/train.sh | Trains from a config file, optionally starting from a checkpoint ``` Example: You download the checkpoints and load the stage 3 folder with MemoryModule. You get a memory module that you then use with the frozen Qwen3-8B and Qwen3-Embedding-4B models. For newcomers and experts: If you are new to this, think of the module as an add-on memory for a language model. Download the files, install the Memorilla code, and load the stage 3 checkpoint first. If you know the field, each folder holds memory.pt, which has 194M parameters in bfloat16, and config.json, which sets 16 memory tokens and no document self-attention. The staged recipe moves from an English Wikipedia stage to a mixture and then to multitask training. ```flow Download the checkpoint folders Install the Memorilla code Load a memory module Use it with the two frozen models ``` Good to know: - The modules are trained against frozen models and must be used with the same two - The two fine-tuned variants both start from Stage 1 - The training guide in the Memorilla project explains the stages and recipes - License: Apache 2.0 - Cost to run: none named - Data it reaches: the checkpoint files you download and whatever data you evaluate on - Not for: standalone use, or use with models other than Qwen3-8B and Qwen3-Embedding-4B ## On Hugging Face, 13 November 2026 - Downloads in the last 30 days: 139 - Likes: 19 - License: apache-2.0 - Based on: Qwen/Qwen3-8B - Tags: memory, long-context ## Links - Model: https://huggingface.co/memorilla/Memorilla-Qwen3-8B - Files: https://huggingface.co/memorilla/Memorilla-Qwen3-8B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download memorilla/Memorilla-Qwen3-8B ``` 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 [memorilla/Memorilla-Qwen3-8B on Hugging Face](https://huggingface.co/memorilla/Memorilla-Qwen3-8B) ## 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: 2026-10-09 - First published: 2026-10-08 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open memorilla/Memorilla-Qwen3-8B on Hugging Face](https://huggingface.co/memorilla/Memorilla-Qwen3-8B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/ca2b26ef-99e6-4921-9457-d1e35edebedd) - [Its page on Mindket](https://mindket.com/p/ca2b26ef-99e6-4921-9457-d1e35edebedd)