--- title: "Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/e69239ed-602f-46a8-8c61-e1bb0dd73291" updated: "2026-11-13" --- # Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Thinks through problems step by step inside think tags, as a 4B Qwen3.5 model fine-tuned on reasoning chains and packaged as GGUF. For people who run a small reasoning model on their own computer. Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-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 Jackrong ## About ### What it is This is a GGUF version of a small reasoning model built on Qwen3.5-4B. The author fine-tuned it on reasoning chains that came mainly from Claude-4.6 Opus interactions, so it plans in steps inside think tags before it answers. It handles text only. ### Who it suits People who want a small model that shows its reasoning and runs on their own computer, and learners who want to see how such fine-tuning is done. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Education | Students | Ask for step-by-step working on math or science questions | A physics problem solved with the steps laid out | | Software | Developers | Run a small local model for planning and analysis | A task broken into parts before any code is written | | Research | Hobbyists | Study how reasoning distillation changes a small model | Compare its thinking with the base model | | Writing | Analysts | Think through an argument before drafting | A list of constraints and edge cases for a plan | Skip it if you need image input, because the final model is text only. ### Where to use it - Your own computer, on Windows, macOS or Linux, once Python has fetched the files - Local runners that open GGUF files | Use case | Who | What they get back | | --- | --- | --- | | Step-by-step problem solving | Students | A reasoning trace in think tags, then an answer | | Task planning | Developers | A plan that lists the goal, parts, constraints and checks | | Training study | Learners | A model whose fine-tuning the author documented | ### How to set it up You need Python with pip, a local runner for GGUF files and about 2.1 GB of disk at 4-bit by the record's estimate. - Python and pip - A runner that opens GGUF files - Around 2.1 GB of disk space for a 4-bit file 1. Install the Hugging Face hub tool. 2. Download the repository's files. 3. Open a GGUF file in your runner. ```bash pip install -U huggingface_hub hf download Jackrong/Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF ``` ### How to use it Chat with it in your runner and give it problems that benefit from steps. The model writes its thinking first, inside think tags, then gives the final solution. ```bash hf download Jackrong/Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF ``` | Tool | What it does | | --- | --- | | hf download | Copies this repository's files to your computer | | think tags | Hold the model's step-by-step reasoning before the answer | ### Example You ask it to plan how to solve a word problem. It first writes a short analysis, naming the goal, splitting the task, checking constraints and edge cases, then it gives a solution. ### For newcomers and experts If you are new to this, load the file and ask one math question. Watch the thinking section before the answer. If you know the field, the author trained with supervised fine-tuning and LoRA, masking the loss to the assistant's thinking and reply. The card says later data from a larger Qwen3.5 model added science, instruction-following and math reasoning. It aims to cut repetitive loops on simple questions. Start from Qwen3.5-4B → Supervised fine-tuning with LoRA → Train on structured reasoning chains → Package the result as GGUF ### Good to know The card makes claims about efficiency that this page cannot confirm. Test the model on your own questions before you rely on it. - Languages: English, Chinese and Korean are listed - Training material: the author published a notebook, code and a guide for the fine-tuning process - License: Apache 2.0 - Cost to run: none named - Data it reaches: the files you download from Hugging Face, plus the text you give your local runner - Not for: image input, since the final model is text only ## README ````markdown # Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF Thinks through problems step by step inside think tags, as a 4B Qwen3.5 model fine-tuned on reasoning chains and packaged as GGUF. For people who run a small reasoning model on their own computer. Made by Jackrong 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 is a GGUF version of a small reasoning model built on Qwen3.5-4B. The author fine-tuned it on reasoning chains that came mainly from Claude-4.6 Opus interactions, so it plans in steps inside think tags before it answers. It handles text only. Who it suits: People who want a small model that shows its reasoning and runs on their own computer, and learners who want to see how such fine-tuning is done. ```table Industry | Who uses it | How they use it | Example Education | Students | Ask for step-by-step working on math or science questions | A physics problem solved with the steps laid out Software | Developers | Run a small local model for planning and analysis | A task broken into parts before any code is written Research | Hobbyists | Study how reasoning distillation changes a small model | Compare its thinking with the base model Writing | Analysts | Think through an argument before drafting | A list of constraints and edge cases for a plan ``` Skip it if you need image input, because the final model is text only. Where to use it: - Your own computer, on Windows, macOS or Linux, once Python has fetched the files - Local runners that open GGUF files ```table Use case | Who | What they get back Step-by-step problem solving | Students | A reasoning trace in think tags, then an answer Task planning | Developers | A plan that lists the goal, parts, constraints and checks Training study | Learners | A model whose fine-tuning the author documented ``` How to set it up: You need Python with pip, a local runner for GGUF files and about 2.1 GB of disk at 4-bit by the record's estimate. - Python and pip - A runner that opens GGUF files - Around 2.1 GB of disk space for a 4-bit file 1. Install the Hugging Face hub tool. 2. Download the repository's files. 3. Open a GGUF file in your runner. ```bash pip install -U huggingface_hub hf download Jackrong/Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF ``` How to use it: Chat with it in your runner and give it problems that benefit from steps. The model writes its thinking first, inside think tags, then gives the final solution. ```bash hf download Jackrong/Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF ``` ```table Tool | What it does hf download | Copies this repository's files to your computer think tags | Hold the model's step-by-step reasoning before the answer ``` Example: You ask it to plan how to solve a word problem. It first writes a short analysis, naming the goal, splitting the task, checking constraints and edge cases, then it gives a solution. For newcomers and experts: If you are new to this, load the file and ask one math question. Watch the thinking section before the answer. If you know the field, the author trained with supervised fine-tuning and LoRA, masking the loss to the assistant's thinking and reply. The card says later data from a larger Qwen3.5 model added science, instruction-following and math reasoning. It aims to cut repetitive loops on simple questions. ```flow Start from Qwen3.5-4B Supervised fine-tuning with LoRA Train on structured reasoning chains Package the result as GGUF ``` Good to know: The card makes claims about efficiency that this page cannot confirm. Test the model on your own questions before you rely on it. - Languages: English, Chinese and Korean are listed - Training material: the author published a notebook, code and a guide for the fine-tuning process - License: Apache 2.0 - Cost to run: none named - Data it reaches: the files you download from Hugging Face, plus the text you give your local runner - Not for: image input, since the final model is text only ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 4.2B - Weights: about 2.1 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 39,987 - Likes: 188 - License: apache-2.0 - Languages: en, zh, ko - Based on: Qwen/Qwen3.5-4B - Tags: gguf, qwen3_5, unsloth, qwen, qwen3.5, reasoning, chain-of-thought, lora, text-generation, conversational ## Links - Model: https://huggingface.co/Jackrong/Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF - Files: https://huggingface.co/Jackrong/Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Jackrong/Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-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 [Jackrong/Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF on Hugging Face](https://huggingface.co/Jackrong/Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-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 | | --- | --- | --- | --- | --- | --- | | Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF (this page) | 39,987 | 188 | 4.2B | apache-2.0 | 2026-04-06 | | [Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF](https://mindket.com/p/2b5092f0-78de-42d0-ae72-417592fa56e9) | 17,179 | 296 | 34.7B | apache-2.0 | 2026-04-19 | | [Qwopus3.8-27B-Flash-V2-GGUF](https://mindket.com/p/f4542d06-f1ba-46a9-8148-58a34e8911b6) | 897,675 | 75 | 461M | apache-2.0 | 2026-09-21 | | [Qwen3.8-9B-Distill](https://mindket.com/p/f257e0b9-f595-4d2e-8a06-68cc42f90540) | 6,592 | 237 | 9.7B | apache-2.0 | 2026-08-15 | | [Qwen3.6-27B-GGUF](https://mindket.com/p/91bfefb7-49da-47e4-a52a-f80091e27806) | 227,770 | 978 | 26.9B | apache-2.0 | 2026-04-22 | | [Qwen3.5-4B-MTP-GGUF](https://mindket.com/p/dd8b24c6-73ed-4159-b540-b7370a9077f5) | 109,500 | 112 | 4.3B | apache-2.0 | 2026-05-16 | Numbers as Mindket last read them, on 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: 2026-04-06 - First published: 2026-03-03 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Jackrong/Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF on Hugging Face](https://huggingface.co/Jackrong/Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/e69239ed-602f-46a8-8c61-e1bb0dd73291) - [Its page on Mindket](https://mindket.com/p/e69239ed-602f-46a8-8c61-e1bb0dd73291)