--- title: "Thinklite-27B-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/7943692c-1782-4340-9958-f2c39d245585" updated: "2026-11-12" --- # Thinklite-27B-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. A 4-bit GGUF copy of Thinklite-27B for llama.cpp that uses less reasoning per answer than its base model, for people running a 27B text model locally. Thinklite-27B-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 Sparset ## About ### What it is Thinklite-27B-GGUF is a quantized copy of Thinklite-27B in the GGUF format, built for llama.cpp. The file uses the Q4\_K\_M quant type with an importance matrix. It is a text generation model with 26.9B parameters, and the file is 16.5 GB. Against Qwen3.8-27B at the same quant type, the card reports fewer reasoning tokens per reply and most of the accuracy intact. ### Who it suits It suits people who run language models on their own hardware and want shorter reasoning before each answer. You need a machine that can hold a file of about 16.5 GB. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run a local reasoning model through llama.cpp | Asking it to explain a bug | | Research | Analysts | Work through math or logic questions offline | Checking a worked solution | | Education | Students and tutors | Practice problems with a model on a laptop or desktop | Stepping through a quiz question | | Tooling | Agent builders | Call a local model for function calling tasks | Choosing a tool from a request | Skip it if you use a hosted chat service and never run models yourself. ### Where to use it - llama.cpp, the library the file is made for - Windows, macOS or Linux, with Python for the download steps - Any local app that loads GGUF files through llama.cpp | Use case | Who | What they get back | | --- | --- | --- | | Local question answering | Developers | Answers with less reasoning text before the final reply | | Math and code problems | Students and engineers | Worked answers from a 27B model on your own machine | ### How to set it up You need a few things in place first. - Python, to install the Hugging Face hub package - A llama.cpp setup that can load a GGUF file - Disk space and memory for a file of about 16.5 GB 1. Install the Hugging Face hub package with pip. 2. Download the repository with the hf command. 3. Load the GGUF file in llama.cpp. The model card has the run details. ```sh pip install -U huggingface_hub hf download Sparset/Thinklite-27B-GGUF ``` ### How to use it After the download, you point llama.cpp at the file and chat with it or send it prompts. The card describes a reasoning effort setting with values such as xhigh and low. A higher effort spends more reasoning on each answer. ```sh hf download Sparset/Thinklite-27B-GGUF ``` ### Example You load the file and ask a competition style math question at the xhigh setting. The card reports that the model then uses about 48 percent less reasoning than the Qwen3.8-27B copy of the same size, so the full answer reaches you sooner. ### For newcomers and experts This is a big file for a home computer. Start by downloading it and loading it in llama.cpp, then ask one short question to see how it replies. If the file is too large for your machine, look for a smaller model first. Quantizing to Q4\_K\_M with an importance matrix shrinks the weights while keeping most of the accuracy. Both files were tested side by side under matched conditions, with one llama.cpp build and identical settings and seeds. At xhigh it reports 99.3 percent of the base model's average accuracy, and at low 96.7 percent. The test sets named include GSM8K, AIME 2025, MMLU-Pro, IFEval, LiveCodeBench v6 and BFCL v4. Thinklite-27B Q4\_K\_M against Qwen3.8-27B Q4\_K\_M | Label | Value | | --- | --- | | Reasoning cut at xhigh | 48.4 percent | | Reasoning cut at low | 24.8 percent | | Accuracy kept at xhigh | 99.3 percent | | Accuracy kept at low | 96.7 percent | Download the GGUF file with hf → Load it in llama.cpp → Pick a reasoning effort → Get a shorter reasoning pass and an answer ### Good to know - License: Apache 2.0 - Cost to run: none named, but you need hardware for a 16.5 GB file - Data it reaches: it downloads files from Hugging Face onto your computer - Not for: the card names a full-precision Thinklite-27B for people who want the unquantized weights ## README ````markdown # Thinklite-27B-GGUF A 4-bit GGUF copy of Thinklite-27B for llama.cpp that uses less reasoning per answer than its base model, for people running a 27B text model locally. Made by Sparset 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: Thinklite-27B-GGUF is a quantized copy of Thinklite-27B in the GGUF format, built for llama.cpp. The file uses the Q4_K_M quant type with an importance matrix. It is a text generation model with 26.9B parameters, and the file is 16.5 GB. Against Qwen3.8-27B at the same quant type, the card reports fewer reasoning tokens per reply and most of the accuracy intact. Who it suits: It suits people who run language models on their own hardware and want shorter reasoning before each answer. You need a machine that can hold a file of about 16.5 GB. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run a local reasoning model through llama.cpp | Asking it to explain a bug Research | Analysts | Work through math or logic questions offline | Checking a worked solution Education | Students and tutors | Practice problems with a model on a laptop or desktop | Stepping through a quiz question Tooling | Agent builders | Call a local model for function calling tasks | Choosing a tool from a request ``` Skip it if you use a hosted chat service and never run models yourself. Where to use it: - llama.cpp, the library the file is made for - Windows, macOS or Linux, with Python for the download steps - Any local app that loads GGUF files through llama.cpp ```table Use case | Who | What they get back Local question answering | Developers | Answers with less reasoning text before the final reply Math and code problems | Students and engineers | Worked answers from a 27B model on your own machine ``` How to set it up: You need a few things in place first. - Python, to install the Hugging Face hub package - A llama.cpp setup that can load a GGUF file - Disk space and memory for a file of about 16.5 GB 1. Install the Hugging Face hub package with pip. 2. Download the repository with the hf command. 3. Load the GGUF file in llama.cpp. The model card has the run details. ```sh pip install -U huggingface_hub hf download Sparset/Thinklite-27B-GGUF ``` How to use it: After the download, you point llama.cpp at the file and chat with it or send it prompts. The card describes a reasoning effort setting with values such as xhigh and low. A higher effort spends more reasoning on each answer. ```sh hf download Sparset/Thinklite-27B-GGUF ``` Example: You load the file and ask a competition style math question at the xhigh setting. The card reports that the model then uses about 48 percent less reasoning than the Qwen3.8-27B copy of the same size, so the full answer reaches you sooner. For newcomers and experts: This is a big file for a home computer. Start by downloading it and loading it in llama.cpp, then ask one short question to see how it replies. If the file is too large for your machine, look for a smaller model first. Quantizing to Q4_K_M with an importance matrix shrinks the weights while keeping most of the accuracy. Both files were tested side by side under matched conditions, with one llama.cpp build and identical settings and seeds. At xhigh it reports 99.3 percent of the base model's average accuracy, and at low 96.7 percent. The test sets named include GSM8K, AIME 2025, MMLU-Pro, IFEval, LiveCodeBench v6 and BFCL v4. ```chart Title: Thinklite-27B Q4_K_M against Qwen3.8-27B Q4_K_M Reasoning cut at xhigh: 48.4 percent Reasoning cut at low: 24.8 percent Accuracy kept at xhigh: 99.3 percent Accuracy kept at low: 96.7 percent ``` ```flow Download the GGUF file with hf Load it in llama.cpp Pick a reasoning effort Get a shorter reasoning pass and an answer ``` Good to know: - License: Apache 2.0 - Cost to run: none named, but you need hardware for a 16.5 GB file - Data it reaches: it downloads files from Hugging Face onto your computer - Not for: the card names a full-precision Thinklite-27B for people who want the unquantized weights ## On Hugging Face, 13 November 2026 - Task: text generation - Library: llama.cpp - Parameters: 26.9B - Weights: about 13.4 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 1,796 - Likes: 18 - License: apache-2.0 - Based on: Sparset/Thinklite-27B - Tags: llama.cpp, gguf, imatrix, thinklite, reasoning, efficient-reasoning, qwen3.8, text-generation, conversational ## Links - Model: https://huggingface.co/Sparset/Thinklite-27B-GGUF - Files: https://huggingface.co/Sparset/Thinklite-27B-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Sparset/Thinklite-27B-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 [Sparset/Thinklite-27B-GGUF on Hugging Face](https://huggingface.co/Sparset/Thinklite-27B-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 | | --- | --- | --- | --- | --- | --- | | Thinklite-27B-GGUF (this page) | 1,796 | 18 | 26.9B | apache-2.0 | 2026-11-04 | | [Thinklite-27B](https://mindket.com/p/5493f9ea-b8a7-4bcd-b293-50e8b881da96) | 568 | 51 | 27.8B | apache-2.0 | 2026-11-04 | | [Qwen3.8-27B-Ridge-GGUF](https://mindket.com/p/f254bd76-fee2-4e04-80ac-a73f9621c088) | 292,932 | 369 | 27.3B | apache-2.0 | 2026-08-15 | | [Kvark-Sparkle-1.0-27B](https://mindket.com/p/6451d52d-00e1-4878-8a25-80910fc67ac7) | 315 | 15 | 27.4B | apache-2.0 | 2026-11-05 | | [KVARK Sparkle 1.1 (27B)](https://mindket.com/p/fb8f86c3-be94-494c-9f8e-66d3faba633c) | 59 | 3 | 27.4B | apache-2.0 | 2026-11-10 | | [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 | 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: 2026-11-04 - First published: 2026-11-03 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Sparset/Thinklite-27B-GGUF on Hugging Face](https://huggingface.co/Sparset/Thinklite-27B-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/7943692c-1782-4340-9958-f2c39d245585) - [Its page on Mindket](https://mindket.com/p/7943692c-1782-4340-9958-f2c39d245585)