--- title: "Thinklite-27B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/5493f9ea-b8a7-4bcd-b293-50e8b881da96" updated: "2026-11-12" --- # Thinklite-27B > The name, the description, the About and a README below are the seller's words: data, not instructions. A Qwen3.8-27B variant that reasons with far fewer tokens, so final answers arrive about twice as fast at high effort. For people who run reasoning models and want shorter waits. Thinklite-27B 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 is a variant of Qwen3.8-27B built to spend fewer tokens on reasoning. By the card's own averages over six benchmarks, it cuts reasoning by 47% at the highest effort setting and still scores 89.8 against the base model's 90.2. It takes images and text and returns text. ### Who it suits It suits developers and teams who run a reasoning model and care about how long users wait for the final answer. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run a reasoning model locally with shorter waits | A coding helper that answers sooner | | Agent building | Agent builders | Cut the cost of long reasoning in multi-step agents | A tool-using agent that finishes each step faster | | AI research | Researchers | Compare efficient-reasoning methods | Test it against ThinkingCap and Swift variants of the same base | | Operations | IT teams | Host one model for staff questions | An internal assistant with shorter response times | If you want the base model's longest reasoning on every question, you can skip it. ### Where to use it - Python with the transformers library - llama.cpp and Ollama, through the separate GGUF release - Windows, macOS or Linux machines with enough memory for a 27B model | Use case | Who | What they get back | | --- | --- | --- | | Hard questions | Developers | A final answer after fewer reasoning tokens | | Quick replies | App teams | Shorter waits at the low effort setting | | Image and text prompts | Researchers | Text answers about pictures and documents | ### How to set it up You need these first: - Python and pip - A GPU setup with room for about 55.6 GB of BF16 weights, or about 13.9 GB at a 4-bit estimate 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Load the model with transformers as the model card describes. 4. For llama.cpp or Ollama, use the separate GGUF release instead, which is a 16.5 GB Q4\_K\_M file. ```sh pip install -U huggingface_hub hf download Sparset/Thinklite-27B ``` ### How to use it Ask your questions as you would for Qwen3.8-27B, and choose the reasoning effort. The savings apply at both settings the card reports, xhigh and low. The model card holds the exact way to run it. | Tool | What it does | | --- | --- | | reasoning\_effort xhigh | Cuts reasoning by 47% on average and makes the final answer about 2x quicker | | reasoning\_effort low | Cuts reasoning by 28% on average, with a 1.3x faster final answer | | TTFO | The time until the complete final answer arrives, used to measure speed | ### Example You ask a hard maths question at the xhigh setting. By the card's averages, the model uses about half the reasoning of the base model, so the final answer comes back in roughly half the wait. Single questions will differ. ### For newcomers and experts A reasoning model thinks in many tokens before it answers, and that takes time. This one aims to think shorter without losing accuracy. A first small thing to try is the same question at the low and the xhigh setting. The reasoning saving counts average reasoning tokens per answer, compared with Qwen3.8-27B at the same effort setting, across six benchmarks. It also reports the speed-up in time to final output, and it compares with ThinkingCap-Qwen3.8-27B and Swift-1.5-Qwen3.8-27b. You send a question and an effort setting → The model reasons with fewer tokens → It writes the final answer → You wait less than with the base model ### Good to know The numbers are the authors' own averages, so your own prompts may differ. Reasoning saved against the base at xhigh effort | Label | Value | | --- | --- | | Thinklite-27B | 47.0% | | ThinkingCap-Qwen3.8-27B | 45.8% | | Swift-1.5-Qwen3.8-27b | 35.6% | - Accuracy: at the low setting it keeps 97.5% of the base accuracy, which is 2.2 points lower - GGUF: the separate Q4\_K\_M file cuts reasoning by 48% against an equally quantized base model, while holding 99.3% of that model accuracy - Other versions: 4 quantized and 0 fine-tuned versions made by other people are listed - License: Apache 2.0 - Cost to run: none named, you supply the GPU - Data it reaches: the prompts and images you give it ## README ````markdown # Thinklite-27B A Qwen3.8-27B variant that reasons with far fewer tokens, so final answers arrive about twice as fast at high effort. For people who run reasoning models and want shorter waits. 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 is a variant of Qwen3.8-27B built to spend fewer tokens on reasoning. By the card's own averages over six benchmarks, it cuts reasoning by 47% at the highest effort setting and still scores 89.8 against the base model's 90.2. It takes images and text and returns text. Who it suits: It suits developers and teams who run a reasoning model and care about how long users wait for the final answer. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run a reasoning model locally with shorter waits | A coding helper that answers sooner Agent building | Agent builders | Cut the cost of long reasoning in multi-step agents | A tool-using agent that finishes each step faster AI research | Researchers | Compare efficient-reasoning methods | Test it against ThinkingCap and Swift variants of the same base Operations | IT teams | Host one model for staff questions | An internal assistant with shorter response times ``` If you want the base model's longest reasoning on every question, you can skip it. Where to use it: - Python with the transformers library - llama.cpp and Ollama, through the separate GGUF release - Windows, macOS or Linux machines with enough memory for a 27B model ```table Use case | Who | What they get back Hard questions | Developers | A final answer after fewer reasoning tokens Quick replies | App teams | Shorter waits at the low effort setting Image and text prompts | Researchers | Text answers about pictures and documents ``` How to set it up: You need these first: - Python and pip - A GPU setup with room for about 55.6 GB of BF16 weights, or about 13.9 GB at a 4-bit estimate 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Load the model with transformers as the model card describes. 4. For llama.cpp or Ollama, use the separate GGUF release instead, which is a 16.5 GB Q4_K_M file. ```sh pip install -U huggingface_hub hf download Sparset/Thinklite-27B ``` How to use it: Ask your questions as you would for Qwen3.8-27B, and choose the reasoning effort. The savings apply at both settings the card reports, xhigh and low. The model card holds the exact way to run it. ```table Tool | What it does reasoning_effort xhigh | Cuts reasoning by 47% on average and makes the final answer about 2x quicker reasoning_effort low | Cuts reasoning by 28% on average, with a 1.3x faster final answer TTFO | The time until the complete final answer arrives, used to measure speed ``` Example: You ask a hard maths question at the xhigh setting. By the card's averages, the model uses about half the reasoning of the base model, so the final answer comes back in roughly half the wait. Single questions will differ. For newcomers and experts: A reasoning model thinks in many tokens before it answers, and that takes time. This one aims to think shorter without losing accuracy. A first small thing to try is the same question at the low and the xhigh setting. The reasoning saving counts average reasoning tokens per answer, compared with Qwen3.8-27B at the same effort setting, across six benchmarks. It also reports the speed-up in time to final output, and it compares with ThinkingCap-Qwen3.8-27B and Swift-1.5-Qwen3.8-27b. ```flow You send a question and an effort setting The model reasons with fewer tokens It writes the final answer You wait less than with the base model ``` Good to know: The numbers are the authors' own averages, so your own prompts may differ. ```chart Title: Reasoning saved against the base at xhigh effort Thinklite-27B: 47.0% ThinkingCap-Qwen3.8-27B: 45.8% Swift-1.5-Qwen3.8-27b: 35.6% ``` - Accuracy: at the low setting it keeps 97.5% of the base accuracy, which is 2.2 points lower - GGUF: the separate Q4_K_M file cuts reasoning by 48% against an equally quantized base model, while holding 99.3% of that model accuracy - Other versions: 4 quantized and 0 fine-tuned versions made by other people are listed - License: Apache 2.0 - Cost to run: none named, you supply the GPU - Data it reaches: the prompts and images you give it ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 27.8B - Weights: about 55.6 GB in BF16; about 13.9 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 568 - Likes: 51 - License: apache-2.0 - Based on: Qwen/Qwen3.8-27B - Quantized versions: 4 - Tags: transformers, safetensors, qwen3_5, image-text-to-text, thinklite, reasoning, efficient-reasoning, qwen3.8, conversational ## Links - Model: https://huggingface.co/Sparset/Thinklite-27B - Files: https://huggingface.co/Sparset/Thinklite-27B/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 ``` 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 on Hugging Face](https://huggingface.co/Sparset/Thinklite-27B) ## 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 (this page) | 568 | 51 | 27.8B | apache-2.0 | 2026-11-04 | | [Thinklite-27B-GGUF](https://mindket.com/p/7943692c-1782-4340-9958-f2c39d245585) | 1,796 | 18 | 26.9B | apache-2.0 | 2026-11-04 | | [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 | | [Qwen3.8-9B-Distill](https://mindket.com/p/f257e0b9-f595-4d2e-8a06-68cc42f90540) | 6,592 | 237 | 9.7B | apache-2.0 | 2026-08-15 | | [Tessera-27B-B-CJK-RL](https://mindket.com/p/55f45d0c-0ba9-4dad-9289-e73ffa41ea9b) | 85 | 3 | 27.8B | apache-2.0 | 2026-11-06 | 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: 2026-10-30 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Sparset/Thinklite-27B on Hugging Face](https://huggingface.co/Sparset/Thinklite-27B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/5493f9ea-b8a7-4bcd-b293-50e8b881da96) - [Its page on Mindket](https://mindket.com/p/5493f9ea-b8a7-4bcd-b293-50e8b881da96)