--- title: "GLM-5.3-Flash-Coder-RCO-152E-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/3d3f2b43-1835-4865-a6e4-82126e543835" updated: "2026-11-12" --- # GLM-5.3-Flash-Coder-RCO-152E-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs a pruned GLM-5.3-Flash coding and agent model on a home PC, as GGUF files in five quant sizes. It keeps 152 of 288 experts per layer, cutting a 321B model to about 170B. GLM-5.3-Flash-Coder-RCO-152E-GGUF 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 lapadatbogdan ## About ### What it is This repository holds GGUF files of GLM-5.3-Flash-Coder-RCO-152E, a pruned version of zai-org's GLM-5.3-Flash. It keeps 152 of the 288 experts in each layer, which shrinks the 321B mixture-of-experts model to about 170B with 17B active. The aim is coding and agent work on a desktop at home. ### Who it suits Developers with a strong desktop who want a local coding and agent model, and tinkerers who like to compare quantization levels. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run a large local coding model on a home PC | A coding assistant in a terminal | | Agents | Agent builders | Back a tool-using agent with a local model | An agent that edits a project | | Research | ML researchers | Study expert pruning and quantization trade-offs | A comparison of Q4\_K\_M and Q3\_K\_M | | Hobby | Enthusiasts | Fit a large model onto a GPU plus system memory | A 24 GB card with 64 GB of RAM | If you have no large GPU or lots of memory, you can skip it, since even the smallest file is 47.4 GB. ### Where to use it - llama.cpp and other programs that load GGUF files, on Windows, macOS or Linux - Home PCs that pair a GPU with plenty of system RAM, or a Mac with large unified memory - Multi-GPU machines, for the largest Q8\_0 files | Use case | Who | What they get back | | --- | --- | --- | | Coding help | Developers | Code written across many programming languages | | Agentic coding | Agent builders | A model tuned on agentic sessions for tool use | | Size comparison | Researchers | Five quant files from 47.4 GB to 180.4 GB | ### How to set it up - Python, to run the Hugging Face download tool - Disk space for the quant you choose, from 47.4 GB to 180.4 GB - Memory to match, such as 24 GB of VRAM plus 96 GB of RAM for Q4\_K\_M 1. Install the Hugging Face hub tools with pip. 2. Download the repository with the hf command, or pick one quant. 3. Check the SHA256 sums the card lists, since an earlier upload was truncated. 4. Load the first part of a split file in llama.cpp. ```bash pip install -U huggingface_hub hf download lapadatbogdan/GLM-5.3-Flash-Coder-RCO-152E-GGUF ``` ### How to use it Load the quant that fits your memory in llama.cpp and use it for coding and agent tasks. The card says the Q3\_K\_M and Q4\_K\_M files include a merged fine-tune that removes reasoning loops, so the old logit-bias workaround is not needed. | Tool | What it does | | --- | --- | | Q8\_0 | 180.4 GB in 4 parts, the reference quant | | Q4\_K\_M | 102.6 GB in 3 parts, fits 24 GB VRAM plus 96 GB RAM | | Q3\_K\_M | 75.1 GB in 2 parts, fits 24 GB VRAM plus 64 GB RAM | | IQ2\_XXS | 47.4 GB in 1 file, fits 24 GB VRAM plus 32 GB RAM | | IQ2\_XXS-downIQ2\_S | 50.8 GB in 2 parts, fits 24 GB VRAM plus 32 GB RAM | ### Example You load the Q4\_K\_M file and ask "Write a Python function that merges two sorted lists and add a short test". The model returns the function and a test, and the merged fine-tune keeps it from looping in its reasoning. ### For newcomers and experts Start with the Q4\_K\_M or Q3\_K\_M file if your memory allows it. Check the SHA256 sums before your first run, because a damaged download produces garbage output. The pruning decided which experts stay by testing on agent sessions, code, math and everyday text, and a repair pass followed. The card measures drift against Q8\_0 with KL divergence, where lower is closer. Q4\_K\_M picks the same top token as Q8\_0 94.5% of the time, and Q3\_K\_M 92.5%. Download a quant, checking SHA256 → Load the first part in llama.cpp → Prompt for code or an agent task → Model answers without reasoning loops ### Good to know - License: MIT - Cost to run: none named, but the files are large and need a strong PC - Data it reaches: downloads files from Hugging Face, then runs on your own machine - Not for: small machines, since the smallest quant is 47.4 GB - Limits: the card says an earlier upload was truncated and gave garbage output, so verify the sums Q3\_K\_M on 80 held-out coding runs, before and after the fine-tune | Label | Value | | --- | --- | | Looping runs before | 11 runs | | Looping runs after | 0 runs | | Solved before | 46 runs | | Solved after | 51 runs | ## README ````markdown # GLM-5.3-Flash-Coder-RCO-152E-GGUF Runs a pruned GLM-5.3-Flash coding and agent model on a home PC, as GGUF files in five quant sizes. It keeps 152 of 288 experts per layer, cutting a 321B model to about 170B. Made by lapadatbogdan 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 repository holds GGUF files of GLM-5.3-Flash-Coder-RCO-152E, a pruned version of zai-org's GLM-5.3-Flash. It keeps 152 of the 288 experts in each layer, which shrinks the 321B mixture-of-experts model to about 170B with 17B active. The aim is coding and agent work on a desktop at home. Who it suits: Developers with a strong desktop who want a local coding and agent model, and tinkerers who like to compare quantization levels. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run a large local coding model on a home PC | A coding assistant in a terminal Agents | Agent builders | Back a tool-using agent with a local model | An agent that edits a project Research | ML researchers | Study expert pruning and quantization trade-offs | A comparison of Q4_K_M and Q3_K_M Hobby | Enthusiasts | Fit a large model onto a GPU plus system memory | A 24 GB card with 64 GB of RAM ``` If you have no large GPU or lots of memory, you can skip it, since even the smallest file is 47.4 GB. Where to use it: - llama.cpp and other programs that load GGUF files, on Windows, macOS or Linux - Home PCs that pair a GPU with plenty of system RAM, or a Mac with large unified memory - Multi-GPU machines, for the largest Q8_0 files ```table Use case | Who | What they get back Coding help | Developers | Code written across many programming languages Agentic coding | Agent builders | A model tuned on agentic sessions for tool use Size comparison | Researchers | Five quant files from 47.4 GB to 180.4 GB ``` How to set it up: - Python, to run the Hugging Face download tool - Disk space for the quant you choose, from 47.4 GB to 180.4 GB - Memory to match, such as 24 GB of VRAM plus 96 GB of RAM for Q4_K_M 1. Install the Hugging Face hub tools with pip. 2. Download the repository with the hf command, or pick one quant. 3. Check the SHA256 sums the card lists, since an earlier upload was truncated. 4. Load the first part of a split file in llama.cpp. ```bash pip install -U huggingface_hub hf download lapadatbogdan/GLM-5.3-Flash-Coder-RCO-152E-GGUF ``` How to use it: Load the quant that fits your memory in llama.cpp and use it for coding and agent tasks. The card says the Q3_K_M and Q4_K_M files include a merged fine-tune that removes reasoning loops, so the old logit-bias workaround is not needed. ```table Tool | What it does Q8_0 | 180.4 GB in 4 parts, the reference quant Q4_K_M | 102.6 GB in 3 parts, fits 24 GB VRAM plus 96 GB RAM Q3_K_M | 75.1 GB in 2 parts, fits 24 GB VRAM plus 64 GB RAM IQ2_XXS | 47.4 GB in 1 file, fits 24 GB VRAM plus 32 GB RAM IQ2_XXS-downIQ2_S | 50.8 GB in 2 parts, fits 24 GB VRAM plus 32 GB RAM ``` Example: You load the Q4_K_M file and ask "Write a Python function that merges two sorted lists and add a short test". The model returns the function and a test, and the merged fine-tune keeps it from looping in its reasoning. For newcomers and experts: Start with the Q4_K_M or Q3_K_M file if your memory allows it. Check the SHA256 sums before your first run, because a damaged download produces garbage output. The pruning decided which experts stay by testing on agent sessions, code, math and everyday text, and a repair pass followed. The card measures drift against Q8_0 with KL divergence, where lower is closer. Q4_K_M picks the same top token as Q8_0 94.5% of the time, and Q3_K_M 92.5%. ```flow Download a quant, checking SHA256 Load the first part in llama.cpp Prompt for code or an agent task Model answers without reasoning loops ``` Good to know: - License: MIT - Cost to run: none named, but the files are large and need a strong PC - Data it reaches: downloads files from Hugging Face, then runs on your own machine - Not for: small machines, since the smallest quant is 47.4 GB - Limits: the card says an earlier upload was truncated and gave garbage output, so verify the sums ```chart Title: Q3_K_M on 80 held-out coding runs, before and after the fine-tune Looping runs before: 11 runs Looping runs after: 0 runs Solved before: 46 runs Solved after: 51 runs ``` ## On Hugging Face, 13 November 2026 - Task: text generation - Library: gguf - Parameters: 170B - Weights: about 84.8 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 34,478 - Likes: 36 - License: mit - Based on: zai-org/GLM-5.3-Flash - Tags: gguf, expert-pruning, code, agentic, llama.cpp, text-generation, conversational ## Links - Model: https://huggingface.co/lapadatbogdan/GLM-5.3-Flash-Coder-RCO-152E-GGUF - Files: https://huggingface.co/lapadatbogdan/GLM-5.3-Flash-Coder-RCO-152E-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download lapadatbogdan/GLM-5.3-Flash-Coder-RCO-152E-GGUF ``` 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 [lapadatbogdan/GLM-5.3-Flash-Coder-RCO-152E-GGUF on Hugging Face](https://huggingface.co/lapadatbogdan/GLM-5.3-Flash-Coder-RCO-152E-GGUF) ## 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-Coder-RCO-152E-GGUF (this page) | 34,478 | 36 | 170B | mit | 2026-10-31 | | [DeepSeek-V4.1-Flash-1017-GSQ-RCO-Coder-GGUF](https://mindket.com/p/1350113a-de6f-4d62-96d6-599c30da33a2) | 342,280 | 225 | 197B | mit | 2026-11-02 | | [Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF](https://mindket.com/p/06acb609-98ee-4422-8521-7943aeb0bcaa) | 781,625 | 610 | 117B | apache-2.0 | 2026-10-19 | | [officialmobai\_Rostam-S1-4B-GGUF](https://mindket.com/p/713c7480-92f9-49e8-8921-587c7837e002) | 1,091 | 2 | 4.1B | apache-2.0 | 2026-11-02 | | [Gemma4-12B v2 Coding + Agentic Edition](https://mindket.com/p/97d1d60d-f19f-421c-a409-1c64ebd17541) | 92,024 | 1,773 | 11.9B | apache-2.0 | 2026-06-19 | | [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 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-14. ## 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: 2026-10-31 - First published: 2026-10-13 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open lapadatbogdan/GLM-5.3-Flash-Coder-RCO-152E-GGUF on Hugging Face](https://huggingface.co/lapadatbogdan/GLM-5.3-Flash-Coder-RCO-152E-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/3d3f2b43-1835-4865-a6e4-82126e543835) - [Its page on Mindket](https://mindket.com/p/3d3f2b43-1835-4865-a6e4-82126e543835)