--- title: "MiMo-V2.6-Distill-Qwen-9B-GSQ-RCO-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/81b8b624-a1fa-4440-b7f9-b44fdafef92b" updated: "2026-11-13" --- # MiMo-V2.6-Distill-Qwen-9B-GSQ-RCO-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Gives you a 9B coding and tool-calling model as one 3.64 GiB GGUF file, quantized at about 3.5 bits per weight for running on your own machine. MiMo-V2.6-Distill-Qwen-9B-GSQ-RCO-GGUF 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 sainez ## About ### What it is The author compressed a 9B Xiaomi MiMo model into one GGUF file that mixes bit widths, taking it from 16.68 GiB in BF16 to 3.64 GiB. Two methods named GSQ and RCO did the work. The aim is to keep agentic coding, short reasoning and accurate tool calls for long-context local use. ### Who it suits It suits people who run coding models on their own hardware, especially devices with unified memory, and researchers who study quantization. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software development | Developers | Load the GGUF file in a local runtime for coding help | Fixing a bug with a model that stays on your machine | | Local AI | Owners of unified-memory devices | Run a 9B model in about 3.64 GiB | Trying a coding agent without a hosted service | | AI research | Quantization researchers | Study how mixed precision was assigned per tensor | Comparing this recipe with plain quantization | You can skip it if you need the full BF16 quality or a hosted service, since this is a compressed copy. ### Where to use it - Local runtimes that read GGUF files - Windows, macOS or Linux, with Python for the download step - Devices with unified memory, which the card names as its target | Use case | Who | What they get back | | --- | --- | --- | | Local coding help | Developers | Code answers with short reasoning from a model on your machine | | Tool calling | Agent builders | Multi-turn tool calls, which the card says it aims to keep accurate | | Quantization study | Researchers | A worked example of GSQ plus RCO on a hybrid model | ### How to set it up - Python, to run the Hugging Face download tool - The huggingface\_hub package - A runtime that can load GGUF files - Room for about 3.64 GiB of model file 1. Install the Hugging Face hub package. 2. Download the repository files to your computer. 3. Open the gguf file in your GGUF runtime. ```sh pip install -U huggingface_hub hf download sainez/MiMo-V2.6-Distill-Qwen-9B-GSQ-RCO-GGUF ``` ### How to use it The download holds the gguf model file and a small json receipt of each tensor's format. The card text shown here gives no run command, so load the gguf file the way your runtime documents. | Tool | What it does | | --- | --- | | hf download | Fetches the repository files to your computer | | The gguf file | The quantized model itself | | The json file | A receipt of each tensor's format and byte size | ### Example You load the gguf file and paste a short function with a bug, asking for a fix. The card says the model aims to keep its thinking short inside think tags and to call tools accurately across turns. ### For newcomers and experts A quantized model is a shrunk copy that stores each number with fewer bits, so it fits on smaller machines. To start, run the two download commands and check that the gguf file is about 3.64 GiB. If you know the field, the base model is a 32-layer hybrid with 24 Gated DeltaNet blocks and 8 Gated Attention blocks. GSQ refines grid codes and scales for the low-bit formats. RCO then assigns a format to each tensor under a fixed byte ceiling. The BF16 checkpoint is the starting point → Each block matrix gets candidate formats → GSQ refines the low-bit candidates → RCO picks one format per tensor → The result is one GGUF file ### Good to know File size in GiB | Label | Value | | --- | --- | | BF16 original | 16.68 GiB | | This quantization | 3.64 GiB | - Method: the two token embedding matrices stay at Q4\_K, which the card says cuts vocabulary KL divergence by 17.0% - Calibration: 2,048-token windows over coding, code reasoning, tools, reasoning and general text - License: apache-2.0 - Cost to run: none named, it runs on your own hardware - Data it reaches: the prompts you give it, and the files you download from Hugging Face - Not for: matching the BF16 original exactly, since it is a quantized copy ## README ````markdown # MiMo-V2.6-Distill-Qwen-9B-GSQ-RCO-GGUF Gives you a 9B coding and tool-calling model as one 3.64 GiB GGUF file, quantized at about 3.5 bits per weight for running on your own machine. Made by sainez 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: The author compressed a 9B Xiaomi MiMo model into one GGUF file that mixes bit widths, taking it from 16.68 GiB in BF16 to 3.64 GiB. Two methods named GSQ and RCO did the work. The aim is to keep agentic coding, short reasoning and accurate tool calls for long-context local use. Who it suits: It suits people who run coding models on their own hardware, especially devices with unified memory, and researchers who study quantization. ```table Industry | Who uses it | How they use it | Example Software development | Developers | Load the GGUF file in a local runtime for coding help | Fixing a bug with a model that stays on your machine Local AI | Owners of unified-memory devices | Run a 9B model in about 3.64 GiB | Trying a coding agent without a hosted service AI research | Quantization researchers | Study how mixed precision was assigned per tensor | Comparing this recipe with plain quantization ``` You can skip it if you need the full BF16 quality or a hosted service, since this is a compressed copy. Where to use it: - Local runtimes that read GGUF files - Windows, macOS or Linux, with Python for the download step - Devices with unified memory, which the card names as its target ```table Use case | Who | What they get back Local coding help | Developers | Code answers with short reasoning from a model on your machine Tool calling | Agent builders | Multi-turn tool calls, which the card says it aims to keep accurate Quantization study | Researchers | A worked example of GSQ plus RCO on a hybrid model ``` How to set it up: - Python, to run the Hugging Face download tool - The huggingface_hub package - A runtime that can load GGUF files - Room for about 3.64 GiB of model file 1. Install the Hugging Face hub package. 2. Download the repository files to your computer. 3. Open the gguf file in your GGUF runtime. ```sh pip install -U huggingface_hub hf download sainez/MiMo-V2.6-Distill-Qwen-9B-GSQ-RCO-GGUF ``` How to use it: The download holds the gguf model file and a small json receipt of each tensor's format. The card text shown here gives no run command, so load the gguf file the way your runtime documents. ```table Tool | What it does hf download | Fetches the repository files to your computer The gguf file | The quantized model itself The json file | A receipt of each tensor's format and byte size ``` Example: You load the gguf file and paste a short function with a bug, asking for a fix. The card says the model aims to keep its thinking short inside think tags and to call tools accurately across turns. For newcomers and experts: A quantized model is a shrunk copy that stores each number with fewer bits, so it fits on smaller machines. To start, run the two download commands and check that the gguf file is about 3.64 GiB. If you know the field, the base model is a 32-layer hybrid with 24 Gated DeltaNet blocks and 8 Gated Attention blocks. GSQ refines grid codes and scales for the low-bit formats. RCO then assigns a format to each tensor under a fixed byte ceiling. ```flow The BF16 checkpoint is the starting point Each block matrix gets candidate formats GSQ refines the low-bit candidates RCO picks one format per tensor The result is one GGUF file ``` Good to know: ```chart Title: File size in GiB BF16 original: 16.68 GiB This quantization: 3.64 GiB ``` - Method: the two token embedding matrices stay at Q4_K, which the card says cuts vocabulary KL divergence by 17.0% - Calibration: 2,048-token windows over coding, code reasoning, tools, reasoning and general text - License: apache-2.0 - Cost to run: none named, it runs on your own hardware - Data it reaches: the prompts you give it, and the files you download from Hugging Face - Not for: matching the BF16 original exactly, since it is a quantized copy ## On Hugging Face, 13 November 2026 - Task: text generation - Library: gguf - Parameters: 9B - Weights: about 4.5 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 1,548 - Likes: 2 - License: apache-2.0 - Based on: XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B - Tags: gguf, llama.cpp, quantization, qwen3_5, mimo, agentic-coding, text-generation, imatrix, conversational ## Links - Model: https://huggingface.co/sainez/MiMo-V2.6-Distill-Qwen-9B-GSQ-RCO-GGUF - Files: https://huggingface.co/sainez/MiMo-V2.6-Distill-Qwen-9B-GSQ-RCO-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download sainez/MiMo-V2.6-Distill-Qwen-9B-GSQ-RCO-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 [sainez/MiMo-V2.6-Distill-Qwen-9B-GSQ-RCO-GGUF on Hugging Face](https://huggingface.co/sainez/MiMo-V2.6-Distill-Qwen-9B-GSQ-RCO-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 | | --- | --- | --- | --- | --- | --- | | MiMo-V2.6-Distill-Qwen-9B-GSQ-RCO-GGUF (this page) | 1,548 | 2 | 9B | apache-2.0 | 2026-10-15 | | [Ripple-65B-Preview-GGUF](https://mindket.com/p/00143f02-5553-4372-8620-836dbb7dfec1) | 100,818 | 53 | 64.8B | apache-2.0 | 2026-11-06 | | [Ornith-1.5-9B-GGUF by AtomicChat](https://mindket.com/p/e8c0edff-085e-4d2a-98f1-7bfdabe54557) | 41,746 | 83 | 9B | mit | 2026-10-28 | | [Qwen3.8-27B-SLQ-GGUF](https://mindket.com/p/c549f05b-1d25-49d4-868a-58981cf20769) | 13,478 | 60 | 27.3B | apache-2.0 | 2026-10-27 | | [KAT-Coder-V2.5-Dev GGUF](https://mindket.com/p/e8ca97ed-d610-4d96-bb2f-d351f4b671c4) | 234,191 | 148 | 34.7B | apache-2.0 | 2026-07-24 | | [Qwen3.8-Flash-Next-GSQ-RCO-GGUF](https://mindket.com/p/ee9ed4a6-39c8-4eee-8958-cce1cd508fa6) | 6,963,596 | 1,229 | 177B | apache-2.0 | 2026-09-29 | 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: 2026-10-15 - First published: 2026-10-14 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open sainez/MiMo-V2.6-Distill-Qwen-9B-GSQ-RCO-GGUF on Hugging Face](https://huggingface.co/sainez/MiMo-V2.6-Distill-Qwen-9B-GSQ-RCO-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/81b8b624-a1fa-4440-b7f9-b44fdafef92b) - [Its page on Mindket](https://mindket.com/p/81b8b624-a1fa-4440-b7f9-b44fdafef92b)