--- title: "GLM-5.3-SuperExpert-v2-9.2GB" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/f7f67939-8a45-485a-b197-422c0e68b65c" updated: "2026-11-13" --- # GLM-5.3-SuperExpert-v2-9.2GB > The name, the description, the About and a README below are the seller's words: data, not instructions. A text generation model compressed from GLM-5.3-Flash to 9.2 GB that its author says runs on a 16 GB laptop or a free Colab T4. Non-commercial license. GLM-5.3-SuperExpert-v2-9.2GB is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by shashanth-27xA9 ## About ### What it is GLM-5.3-SuperExpert-v2 is a text generation model published by an author named Shashanth. It starts from the large GLM-5.3-Flash model and shrinks it to a 9.2 GB runtime using a method the author calls AW-SECD. The author says it runs locally on laptops with 16 GB of RAM, on Apple Silicon Macs and on a free Colab T4. ### Who it suits People with modest hardware who want to try a compressed large-model derivative locally, mostly for chat and experiments. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Personal use | Laptop owners | Run a chat model on their own machine | Ask questions without sending them to a cloud service | | Learning | Students with a free Colab | Try a large-model derivative in a notebook | Test it on a Colab T4 | | Local AI hobbyists | Ollama users | Pull the Q4\_K\_M GGUF with one command | Start a chat in the terminal | You can skip it if you need commercial use, since the license is non-commercial, or if you want independently checked quality. ### Where to use it - Ollama, in a local terminal - A laptop with 16 GB of RAM, or an Apple Silicon Mac - A free Google Colab Tesla T4 - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Local chat | Hobbyists | Text replies generated on your own machine | | Notebook trials | Students | A quick way to test the model on a free GPU | | Studying compression | Researchers | A concrete example of a compressed model to inspect | ### How to set it up Install Ollama first. The card names a Q4\_K\_M GGUF file of about 9.63 GB, so keep that much disk space free. - Ollama installed on your machine - About 10 GB of free disk space - A computer that fits the 16 GB RAM guidance 1. Install Ollama. 2. Run the one-line command in a terminal. 3. Wait for the first download to finish. ```bash ollama run hf.co/shashanth-27xA9/GLM-5.3-SuperExpert-v2-9.2GB:q4_k_m ``` To fetch the repository files yourself, Hugging Face gives these steps: ```sh pip install -U huggingface_hub hf download shashanth-27xA9/GLM-5.3-SuperExpert-v2-9.2GB ``` ### How to use it Run the Ollama command above and type to the model in the same terminal. The command downloads the Q4\_K\_M GGUF on first use and then starts it. | Tool | What it does | | --- | --- | | ollama run | Downloads the Q4\_K\_M GGUF from the model repository and starts a session | ### Example After the command above starts, you type "Explain what a mutex is in plain words." The model answers with text in the terminal. The card gives no sample outputs in the part used here. ### For newcomers and experts If you are new, install Ollama and run the one-line command, then ask a short question to see how it answers. Do not expect results of a full-size model. For experts, the author describes AW-SECD as a consolidation that compresses a 320B-parameter architecture into the 9.2 GB model. The card claims it keeps 95.6 percent of baseline capability. The part of the card used here does not show how that was measured, so treat the figure as the author's claim. GLM-5.3-Flash base model → AW-SECD consolidation → 9.2 GB model → Local run in Ollama ### Good to know - Formats: Safetensors and GGUF - Languages: English and Chinese - Claims: the quality and speed figures come from the author, not from an independent test - License: CC BY-NC 4.0, so it is for non-commercial use - Cost to run: the card says no cloud inference cost, and it names no key or paid service - Data it reaches: downloads files from Hugging Face, and the run stays on your machine - Not for: commercial use, since the license is non-commercial ## README ````markdown # GLM-5.3-SuperExpert-v2-9.2GB A text generation model compressed from GLM-5.3-Flash to 9.2 GB that its author says runs on a 16 GB laptop or a free Colab T4. Non-commercial license. Made by shashanth-27xA9 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: GLM-5.3-SuperExpert-v2 is a text generation model published by an author named Shashanth. It starts from the large GLM-5.3-Flash model and shrinks it to a 9.2 GB runtime using a method the author calls AW-SECD. The author says it runs locally on laptops with 16 GB of RAM, on Apple Silicon Macs and on a free Colab T4. Who it suits: People with modest hardware who want to try a compressed large-model derivative locally, mostly for chat and experiments. ```table Industry | Who uses it | How they use it | Example Personal use | Laptop owners | Run a chat model on their own machine | Ask questions without sending them to a cloud service Learning | Students with a free Colab | Try a large-model derivative in a notebook | Test it on a Colab T4 Local AI hobbyists | Ollama users | Pull the Q4_K_M GGUF with one command | Start a chat in the terminal ``` You can skip it if you need commercial use, since the license is non-commercial, or if you want independently checked quality. Where to use it: - Ollama, in a local terminal - A laptop with 16 GB of RAM, or an Apple Silicon Mac - A free Google Colab Tesla T4 - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Local chat | Hobbyists | Text replies generated on your own machine Notebook trials | Students | A quick way to test the model on a free GPU Studying compression | Researchers | A concrete example of a compressed model to inspect ``` How to set it up: Install Ollama first. The card names a Q4_K_M GGUF file of about 9.63 GB, so keep that much disk space free. - Ollama installed on your machine - About 10 GB of free disk space - A computer that fits the 16 GB RAM guidance 1. Install Ollama. 2. Run the one-line command in a terminal. 3. Wait for the first download to finish. ```bash ollama run hf.co/shashanth-27xA9/GLM-5.3-SuperExpert-v2-9.2GB:q4_k_m ``` To fetch the repository files yourself, Hugging Face gives these steps: ```sh pip install -U huggingface_hub hf download shashanth-27xA9/GLM-5.3-SuperExpert-v2-9.2GB ``` How to use it: Run the Ollama command above and type to the model in the same terminal. The command downloads the Q4_K_M GGUF on first use and then starts it. ```table Tool | What it does ollama run | Downloads the Q4_K_M GGUF from the model repository and starts a session ``` Example: After the command above starts, you type "Explain what a mutex is in plain words." The model answers with text in the terminal. The card gives no sample outputs in the part used here. For newcomers and experts: If you are new, install Ollama and run the one-line command, then ask a short question to see how it answers. Do not expect results of a full-size model. For experts, the author describes AW-SECD as a consolidation that compresses a 320B-parameter architecture into the 9.2 GB model. The card claims it keeps 95.6 percent of baseline capability. The part of the card used here does not show how that was measured, so treat the figure as the author's claim. ```flow GLM-5.3-Flash base model AW-SECD consolidation 9.2 GB model Local run in Ollama ``` Good to know: - Formats: Safetensors and GGUF - Languages: English and Chinese - Claims: the quality and speed figures come from the author, not from an independent test - License: CC BY-NC 4.0, so it is for non-commercial use - Cost to run: the card says no cloud inference cost, and it names no key or paid service - Data it reaches: downloads files from Hugging Face, and the run stays on your machine - Not for: commercial use, since the license is non-commercial ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 9.2B - Weights: about 9.2 GB in U8 - Formats: Safetensors, GGUF - Downloads in the last 30 days: 10,445 - Likes: 10 - License: cc-by-nc-4.0 - Languages: en, zh - Based on: zai-org/GLM-5.3-Flash - Tags: safetensors, gguf, glm_superexpert, glm4, glm5_next, super-expert, aw-secd, reasoning, sub-10gb, laptop-runnable, ollama, llama.cpp ## Links - Model: https://huggingface.co/shashanth-27xA9/GLM-5.3-SuperExpert-v2-9.2GB - Files: https://huggingface.co/shashanth-27xA9/GLM-5.3-SuperExpert-v2-9.2GB/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download shashanth-27xA9/GLM-5.3-SuperExpert-v2-9.2GB ``` 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 [shashanth-27xA9/GLM-5.3-SuperExpert-v2-9.2GB on Hugging Face](https://huggingface.co/shashanth-27xA9/GLM-5.3-SuperExpert-v2-9.2GB) ## 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 | | --- | --- | --- | --- | --- | --- | | GLM-5.3-SuperExpert-v2-9.2GB (this page) | 10,445 | 10 | 9.2B | cc-by-nc-4.0 | 2026-11-10 | | [GLM-4-9B-0414-GGUF](https://mindket.com/p/9aba7016-1302-46e4-9a38-9ec85c1652cb) | 13,323 | 33 | 9.4B | mit | 2025-07-03 | | [theOG-50M](https://mindket.com/p/fbfa18d1-c133-4a6d-9b8b-949fac32b65e) | 1,312 | 3 | 50.1M | apache-2.0 | 2026-11-09 | | [Qwen3.8-35B-A3B-Distill-GGUF](https://mindket.com/p/d707b0c8-89b8-4c4f-8d49-2ff67de613c7) | 1,337,097 | 277 | 35.5B | apache-2.0 | 2026-09-16 | | [Tess-4.5-27B GGUF](https://mindket.com/p/2c9f4a01-ed9d-4dd7-94a5-94a5fe30c6a8) | 1,095,620 | 65 | 26.9B | apache-2.0 | 2026-11-08 | | [Qwen3.8-4B-Distill-GGUF](https://mindket.com/p/ef3614cf-441f-4388-b81f-7b72b3e7ed30) | 570,974 | 208 | 4.3B | apache-2.0 | 2026-08-16 | 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: 2026-11-10 - First published: 2026-10-16 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open shashanth-27xA9/GLM-5.3-SuperExpert-v2-9.2GB on Hugging Face](https://huggingface.co/shashanth-27xA9/GLM-5.3-SuperExpert-v2-9.2GB) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/f7f67939-8a45-485a-b197-422c0e68b65c) - [Its page on Mindket](https://mindket.com/p/f7f67939-8a45-485a-b197-422c0e68b65c)