--- title: "Qwen3.8-27B-SLQ-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c549f05b-1d25-49d4-868a-58981cf20769" updated: "2026-11-13" --- # Qwen3.8-27B-SLQ-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Compresses Qwen3.8-27B into three GGUF files with different size and fidelity levels, using a custom statistically lossless quantization. For people who run the model locally in llama.cpp. Qwen3.8-27B-SLQ-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 kaitchup ## About ### What it is This repository holds Qwen3.8-27B compressed with a custom version of SLQ, short for Statistically-Lossless Quantization, and packed as GGUF. Instead of one uniform precision, the search picks a source for each of 498 whole matrices, including the embeddings and the output head. You choose one of three files. ### Who it suits It suits people who run large models locally and want to pick a precise point between file size and closeness to the original. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run a 27B model locally in llama.cpp | A local coding or chat assistant | | Research | Quantization researchers | Study per-matrix mixed precision and EAR scores | Compare the three files on your own prompts | | AI hobbyists | Local model users | Choose a file that fits their memory | Pick the smallest file when memory is tight | | Operations | Platform teams | Serve a faithful compressed copy of the base model | An internal text endpoint | If you need image or video input, you can skip it, because no vision tensors or projector are included. ### Where to use it - llama.cpp builds with Qwen3.8 architecture support - Windows, macOS or Linux, where you download the files with Python - Other programs that read GGUF files | Use case | Who | What they get back | | --- | --- | --- | | Text chat | Local model users | Replies from a close copy of Qwen3.8-27B | | Size choice | Developers | Three files, from 18.876 GB to 23.697 GB | | Fidelity study | Researchers | Measured EAR@10 values near 98, 98.5 and 99 percent | ### How to set it up You need Python with pip and a llama.cpp build that supports Qwen3.8. - Python and pip - A llama.cpp build with Qwen3.8 support - Disk space for the file you choose, from 18.876 GB to 23.697 GB 1. Install the Hugging Face client with pip. 2. Download one file from the repository, not all three. 3. Load that file in llama.cpp. ```bash pip install -U huggingface_hub hf download kaitchup/Qwen3.8-27B-SLQ-GGUF \ Qwen3.8-27B-SLQ-EAR98-Q5_K_S.gguf --local-dir . ``` ### How to use it Load the file you downloaded in a llama.cpp build and chat in text. The card validated text scoring and standalone GGUF parity, but not image or video input and not speculative decoding. | Model | Quant label | Complete file in GB | EAR@10 target | | --- | --- | --- | --- | | EAR98 | Q5\_K\_S | 18.876 | 98% | | EAR98.5 | Q5\_K\_M | 20.703 | 98.5% | | EAR99 | Q6\_K | 23.697 | 99% | ### Example You download the EAR98 file, load it in llama.cpp and ask a text question. You get an ordinary text reply. The card says the output stays close to the BF16 model's next-token probabilities, though it does not promise identical answers. ### For newcomers and experts Pick the file by how much memory you have. A bigger file stays closer to the original model. The first small thing to try is the smallest file, with the download command above. EAR means Expected Acceptance Rate, the overlap between the BF16 model's next-token probabilities and the compared model's, over the top 10 likely tokens. An EAR of 0.99 is a 99% average overlap in probabilities, which is not the same as 99% accuracy on benchmarks. The filename suffixes are only Hub compatibility labels for the custom allocations. The search drew from several donor files, including unsloth GGUF types and a GSQ file. Qwen3.8-27B BF16 reference → Candidate donor files from several quant types → Search picks a source per matrix → Custom SLQ allocation reaches an EAR target → Packed as GGUF ### Good to know Sizes: file sizes are in decimal GB. Each file keeps about 351 million bytes of MTP tensors, which were disabled during evaluation. Complete file size in GB | Label | Value | | --- | --- | | EAR98 | 18.876 GB | | EAR98.5 | 20.703 GB | | EAR99 | 23.697 GB | - Fidelity: the values are calibration fidelity, since the same corpus guided the choice, and the card says downstream task equivalence has not been established. - Missing parts: the files leave out vision tensors and a multimodal projector. - Credit: the card thanks the Qwen team, Unsloth, ISTA-DASLab and the SLQ authors. - License: Apache 2.0, as the License line says. - Cost to run: no API key, account or paid service is named. The author invites optional support. - Data it reaches: the prompts you give your runner, and Hugging Face for the download. - Not for: image or video input, and speculative decoding, which the card says it did not validate. ## README ````markdown # Qwen3.8-27B-SLQ-GGUF Compresses Qwen3.8-27B into three GGUF files with different size and fidelity levels, using a custom statistically lossless quantization. For people who run the model locally in llama.cpp. Made by kaitchup 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 Qwen3.8-27B compressed with a custom version of SLQ, short for Statistically-Lossless Quantization, and packed as GGUF. Instead of one uniform precision, the search picks a source for each of 498 whole matrices, including the embeddings and the output head. You choose one of three files. Who it suits: It suits people who run large models locally and want to pick a precise point between file size and closeness to the original. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run a 27B model locally in llama.cpp | A local coding or chat assistant Research | Quantization researchers | Study per-matrix mixed precision and EAR scores | Compare the three files on your own prompts AI hobbyists | Local model users | Choose a file that fits their memory | Pick the smallest file when memory is tight Operations | Platform teams | Serve a faithful compressed copy of the base model | An internal text endpoint ``` If you need image or video input, you can skip it, because no vision tensors or projector are included. Where to use it: - llama.cpp builds with Qwen3.8 architecture support - Windows, macOS or Linux, where you download the files with Python - Other programs that read GGUF files ```table Use case | Who | What they get back Text chat | Local model users | Replies from a close copy of Qwen3.8-27B Size choice | Developers | Three files, from 18.876 GB to 23.697 GB Fidelity study | Researchers | Measured EAR@10 values near 98, 98.5 and 99 percent ``` How to set it up: You need Python with pip and a llama.cpp build that supports Qwen3.8. - Python and pip - A llama.cpp build with Qwen3.8 support - Disk space for the file you choose, from 18.876 GB to 23.697 GB 1. Install the Hugging Face client with pip. 2. Download one file from the repository, not all three. 3. Load that file in llama.cpp. ```bash pip install -U huggingface_hub hf download kaitchup/Qwen3.8-27B-SLQ-GGUF \ Qwen3.8-27B-SLQ-EAR98-Q5_K_S.gguf --local-dir . ``` How to use it: Load the file you downloaded in a llama.cpp build and chat in text. The card validated text scoring and standalone GGUF parity, but not image or video input and not speculative decoding. ```table Model | Quant label | Complete file in GB | EAR@10 target EAR98 | Q5_K_S | 18.876 | 98% EAR98.5 | Q5_K_M | 20.703 | 98.5% EAR99 | Q6_K | 23.697 | 99% ``` Example: You download the EAR98 file, load it in llama.cpp and ask a text question. You get an ordinary text reply. The card says the output stays close to the BF16 model's next-token probabilities, though it does not promise identical answers. For newcomers and experts: Pick the file by how much memory you have. A bigger file stays closer to the original model. The first small thing to try is the smallest file, with the download command above. EAR means Expected Acceptance Rate, the overlap between the BF16 model's next-token probabilities and the compared model's, over the top 10 likely tokens. An EAR of 0.99 is a 99% average overlap in probabilities, which is not the same as 99% accuracy on benchmarks. The filename suffixes are only Hub compatibility labels for the custom allocations. The search drew from several donor files, including unsloth GGUF types and a GSQ file. ```flow Qwen3.8-27B BF16 reference Candidate donor files from several quant types Search picks a source per matrix Custom SLQ allocation reaches an EAR target Packed as GGUF ``` Good to know: Sizes: file sizes are in decimal GB. Each file keeps about 351 million bytes of MTP tensors, which were disabled during evaluation. ```chart Title: Complete file size in GB EAR98: 18.876 GB EAR98.5: 20.703 GB EAR99: 23.697 GB ``` - Fidelity: the values are calibration fidelity, since the same corpus guided the choice, and the card says downstream task equivalence has not been established. - Missing parts: the files leave out vision tensors and a multimodal projector. - Credit: the card thanks the Qwen team, Unsloth, ISTA-DASLab and the SLQ authors. - License: Apache 2.0, as the License line says. - Cost to run: no API key, account or paid service is named. The author invites optional support. - Data it reaches: the prompts you give your runner, and Hugging Face for the download. - Not for: image or video input, and speculative decoding, which the card says it did not validate. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: gguf - Parameters: 27.3B - Weights: about 13.7 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 13,478 - Likes: 60 - License: apache-2.0 - Based on: Qwen/Qwen3.8-27B - Tags: gguf, qwen3_5, mixed-precision, llama.cpp, quantization, text-generation, conversational ## Links - Model: https://huggingface.co/kaitchup/Qwen3.8-27B-SLQ-GGUF - Files: https://huggingface.co/kaitchup/Qwen3.8-27B-SLQ-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download kaitchup/Qwen3.8-27B-SLQ-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 [kaitchup/Qwen3.8-27B-SLQ-GGUF on Hugging Face](https://huggingface.co/kaitchup/Qwen3.8-27B-SLQ-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 | | --- | --- | --- | --- | --- | --- | | Qwen3.8-27B-SLQ-GGUF (this page) | 13,478 | 60 | 27.3B | apache-2.0 | 2026-10-27 | | [MiMo-V2.6-Distill-Qwen-9B-GSQ-RCO-GGUF](https://mindket.com/p/81b8b624-a1fa-4440-b7f9-b44fdafef92b) | 1,548 | 2 | 9B | apache-2.0 | 2026-10-15 | | [DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF](https://mindket.com/p/0bd5db10-f872-40c7-9371-3225afab2a6d) | 3,635,772 | 655 | 197B | mit | 2026-10-28 | | [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 | | [Ornith-1.5-35B-A3B-GSQ-RCO-GGUF](https://mindket.com/p/1ce4ee0b-4bd4-410d-a55a-e0e0f7c496c4) | 11,634 | 30 | 34.7B | mit | 2026-09-24 | | [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: 2026-10-27 - First published: 2026-10-27 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open kaitchup/Qwen3.8-27B-SLQ-GGUF on Hugging Face](https://huggingface.co/kaitchup/Qwen3.8-27B-SLQ-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c549f05b-1d25-49d4-868a-58981cf20769) - [Its page on Mindket](https://mindket.com/p/c549f05b-1d25-49d4-868a-58981cf20769)