--- title: "GLM-5.3-Flash-oQ4e-mtp" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/e0eac6dc-388f-4725-bec8-a491a559f867" updated: "2026-11-13" --- # GLM-5.3-Flash-oQ4e-mtp > The name, the description, the About and a README below are the seller's words: data, not instructions. A mixed-precision MLX copy of zai-org's GLM-5.3-Flash multimodal mixture-of-experts model, about 4.6 bits per weight, with the vision encoder and multi-token prediction head included. GLM-5.3-Flash-oQ4e-mtp 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 Jundot ## About ### What it is This is GLM-5.3-Flash converted to MLX and compressed. The original is a mixture-of-experts model that handles text and images, with around 320B parameters overall and 18B in use for each token. Three parts are bundled: the text model, an image encoder and a head that predicts several tokens at once. The files were compressed with oQ, a mixed-precision method in oMLX, to an average of 4.641 bits per weight. The author says an earlier copy was re-quantized with a better low-bit method, so re-download if yours is old. ### Who it suits People with a large-memory machine who want to run a huge multimodal model locally through MLX, and people who study mixed-precision quantization. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Quantization researchers | Study sensitivity-based bit allocation | Reading the card's bit distribution table | | Software | Local-model developers | Run a large multimodal model on their own hardware | A private assistant that reads images and text | | Hobbyists | MLX users | Try a frontier-scale open model locally | Testing it on a high-memory machine | You can skip it if your machine has far less than about 174 GiB of memory for the weights, or if you want a small model. ### Where to use it It is an MLX checkpoint, built for MLX tools and Apple's MLX framework on Apple silicon, and the card names oMLX as the quantization tool. - MLX-based tools on Apple silicon - oMLX, the tool that made the quantization - A machine with room for roughly 174 GiB of weights | Use case | Who | What they get back | | --- | --- | --- | | Local multimodal chat | Hobbyists | Replies to text and images from a huge model | | Quantization study | Researchers | A documented mixed-precision layout | | Speculative decoding experiments | Developers | A checkpoint that includes the MTP head | ### How to set it up You need Python for the download tool, an MLX-based runner, and a lot of memory. The card gives only the download step and no run command. - Python with pip - An MLX-compatible runner - About 174 GiB of free disk, and memory to match 1. Install the Hugging Face hub tools with pip. 2. Download the repository files with the hf command. 3. Open the checkpoint in your MLX runner. ```sh pip install -U huggingface_hub hf download Jundot/GLM-5.3-Flash-oQ4e-mtp ``` ### How to use it Load the folder in an MLX-based tool and chat as with any local model. The card includes no run command, so follow your tool's own steps. | Part | Storage format | | --- | --- | | Most weights | Affine 4-bit with group size 64 | | Sensitive layers | Affine 8-bit with group size 64 | | Some components | BF16 | | A few parameters | FP32 | ### Example You load the checkpoint in an MLX runner and ask it a question about a photo. The model answers from its language backbone and vision encoder, running only on your machine. ### For newcomers and experts If you are new, know that this is a huge model that needs a lot of memory. Start with a smaller checkpoint if your computer is modest. If you know the field, oQ4e spends more bits on layers it measures as sensitive. Each group is then rounded using an importance matrix drawn from 128 samples of 512 tokens each, followed by a refit of the group's scale and bias. Measure layer sensitivity → Assign bits per group → Round with an importance matrix → Refit scale and bias → Save the MLX checkpoint ### Good to know Nearly all weights are stored as 4-bit, and the language backbone dominates the files. The sizes come from the card. Storage by component, gigabytes | Label | Value | | --- | --- | | Language backbone | 180.966 GB | | MTP head | 4.309 GB | | Vision encoder | 1.127 GB | - License: MIT - Cost to run: none named, but the weights need a large amount of memory - Data it reaches: the files you download and the prompts and images you give it locally - Not for: small machines, since the whole checkpoint is about 186 GB ## README ````markdown # GLM-5.3-Flash-oQ4e-mtp A mixed-precision MLX copy of zai-org's GLM-5.3-Flash multimodal mixture-of-experts model, about 4.6 bits per weight, with the vision encoder and multi-token prediction head included. Made by Jundot 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: This is GLM-5.3-Flash converted to MLX and compressed. The original is a mixture-of-experts model that handles text and images, with around 320B parameters overall and 18B in use for each token. Three parts are bundled: the text model, an image encoder and a head that predicts several tokens at once. The files were compressed with oQ, a mixed-precision method in oMLX, to an average of 4.641 bits per weight. The author says an earlier copy was re-quantized with a better low-bit method, so re-download if yours is old. Who it suits: People with a large-memory machine who want to run a huge multimodal model locally through MLX, and people who study mixed-precision quantization. ```table Industry | Who uses it | How they use it | Example Research | Quantization researchers | Study sensitivity-based bit allocation | Reading the card's bit distribution table Software | Local-model developers | Run a large multimodal model on their own hardware | A private assistant that reads images and text Hobbyists | MLX users | Try a frontier-scale open model locally | Testing it on a high-memory machine ``` You can skip it if your machine has far less than about 174 GiB of memory for the weights, or if you want a small model. Where to use it: It is an MLX checkpoint, built for MLX tools and Apple's MLX framework on Apple silicon, and the card names oMLX as the quantization tool. - MLX-based tools on Apple silicon - oMLX, the tool that made the quantization - A machine with room for roughly 174 GiB of weights ```table Use case | Who | What they get back Local multimodal chat | Hobbyists | Replies to text and images from a huge model Quantization study | Researchers | A documented mixed-precision layout Speculative decoding experiments | Developers | A checkpoint that includes the MTP head ``` How to set it up: You need Python for the download tool, an MLX-based runner, and a lot of memory. The card gives only the download step and no run command. - Python with pip - An MLX-compatible runner - About 174 GiB of free disk, and memory to match 1. Install the Hugging Face hub tools with pip. 2. Download the repository files with the hf command. 3. Open the checkpoint in your MLX runner. ```sh pip install -U huggingface_hub hf download Jundot/GLM-5.3-Flash-oQ4e-mtp ``` How to use it: Load the folder in an MLX-based tool and chat as with any local model. The card includes no run command, so follow your tool's own steps. ```table Part | Storage format Most weights | Affine 4-bit with group size 64 Sensitive layers | Affine 8-bit with group size 64 Some components | BF16 A few parameters | FP32 ``` Example: You load the checkpoint in an MLX runner and ask it a question about a photo. The model answers from its language backbone and vision encoder, running only on your machine. For newcomers and experts: If you are new, know that this is a huge model that needs a lot of memory. Start with a smaller checkpoint if your computer is modest. If you know the field, oQ4e spends more bits on layers it measures as sensitive. Each group is then rounded using an importance matrix drawn from 128 samples of 512 tokens each, followed by a refit of the group's scale and bias. ```flow Measure layer sensitivity Assign bits per group Round with an importance matrix Refit scale and bias Save the MLX checkpoint ``` Good to know: Nearly all weights are stored as 4-bit, and the language backbone dominates the files. The sizes come from the card. ```chart Title: Storage by component, gigabytes Language backbone: 180.966 GB MTP head: 4.309 GB Vision encoder: 1.127 GB ``` - License: MIT - Cost to run: none named, but the weights need a large amount of memory - Data it reaches: the files you download and the prompts and images you give it locally - Not for: small machines, since the whole checkpoint is about 186 GB ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: mlx - Parameters: 321B - Weights: about 1,285 GB in U32 - Formats: Safetensors - Downloads in the last 30 days: 26,944 - Likes: 7 - License: mit - Based on: zai-org/GLM-5.3-Flash - Tags: safetensors, glm5_next, quantized, image-text-to-text, conversational, 4-bit ## Links - Model: https://huggingface.co/Jundot/GLM-5.3-Flash-oQ4e-mtp - Files: https://huggingface.co/Jundot/GLM-5.3-Flash-oQ4e-mtp/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Jundot/GLM-5.3-Flash-oQ4e-mtp ``` 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 [Jundot/GLM-5.3-Flash-oQ4e-mtp on Hugging Face](https://huggingface.co/Jundot/GLM-5.3-Flash-oQ4e-mtp) ## 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-Flash-oQ4e-mtp (this page) | 26,944 | 7 | 321B | mit | 2026-10-25 | | [GLM-5.3-Flash EXL3 4bpw for TensorFold](https://mindket.com/p/dc92b67d-d498-4170-8472-86e468d8b950) | 4,746 | 73 | 87.8B | apache-2.0 | 2026-10-03 | | [GLM-5.3-Flash-NVFP4](https://mindket.com/p/679b8989-3e0b-422a-89df-8e0aed6fb131) | 79,326 | 186 | 321B | mit | 2026-10-28 | | [GLM-5.3-Flash EXL3 Spark](https://mindket.com/p/ec88db01-e5e6-420e-9171-26462734e98f) | 399 | 11 | 48B | mit | 2026-09-18 | | [GLM-5.3-Flash EXL3 3.5bpw](https://mindket.com/p/aa1e775c-766f-4b32-94d9-711360cd2ea8) | 140 | 4 | 78.1B | apache-2.0 | 2026-10-30 | | [Qwen3.8-27B-oQ4e-mtp](https://mindket.com/p/d7bc66cd-7f50-482e-9362-f0e9236d1f32) | 37,954 | 86 | 27.8B | apache-2.0 | 2026-10-29 | 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: 2026-10-25 - First published: 2026-10-11 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Jundot/GLM-5.3-Flash-oQ4e-mtp on Hugging Face](https://huggingface.co/Jundot/GLM-5.3-Flash-oQ4e-mtp) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/e0eac6dc-388f-4725-bec8-a491a559f867) - [Its page on Mindket](https://mindket.com/p/e0eac6dc-388f-4725-bec8-a491a559f867)