--- title: "Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/2b5092f0-78de-42d0-ae72-417592fa56e9" updated: "2026-11-13" --- # Qwen3.6-35B-A3B-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. Runs a reasoning-tuned Qwen3.6-35B-A3B on your own machine as GGUF files in four quant sizes, trained on Claude Opus 4.6 style reasoning data. For people who want step-by-step answers locally. Qwen3.6-35B-A3B-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 hesamation ## About ### What it is These are GGUF quantizations of a Qwen3.6-35B-A3B model that was fine-tuned for reasoning. The training data was chain-of-thought material in the style of Claude Opus 4.6. The files cover text only, so image and video input is not part of what was trained. ### Who it suits It suits people who want to run a reasoning-focused model on their own computer and compare it with its base model. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Local AI | Hobbyists with strong hardware | Run a 35B reasoning model offline | Working through a logic puzzle step by step | | Research | Fine-tuning researchers | Study the effect of reasoning distillation on a base model | Comparing answers with the original Qwen model | | Software | Developers | Try a reasoning model in a local llama.cpp setup | Asking a technical question and reading the working | | Education | Students and tutors | Read worked reasoning on problems | Walking through a maths question | Skip it if you need image input or have little memory, since the 4-bit weights alone are about 17.3 GB by estimate. ### Where to use it - llama.cpp and other runtimes that read GGUF files - Windows, macOS or Linux with Python, for downloading the files - Local chat or scripting setups where you pick the quant that fits your memory | Use case | Who | What they get back | | --- | --- | --- | | Reasoning questions | Local AI users | Answers that show their working | | Model comparison | Researchers | A tuned model to test against the base Qwen model | | Quant choice | Anyone tuning memory use | Four file sizes to trade quality against memory | ### How to set it up You need these first. - Python with pip - A runtime that loads GGUF files, such as llama.cpp - Enough memory for your chosen quant, starting near 17.3 GB for 4-bit 1. Install the Hugging Face hub library with pip. 2. Download the model files with the hf command. 3. Pick one quant file, since the card says files arrive as each quantization finishes. 4. Load that file in your GGUF runtime, following its own instructions. ```sh pip install -U huggingface_hub hf download hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF ``` ### How to use it Load one quant file in your runtime and chat with it as a reasoning model. The training used the qwen3-thinking chat template. The card gives no run command of its own, so the exact steps depend on your runtime. | Tool | What it does | | --- | --- | | Q4\_K\_M | The smallest everyday choice for local use | | Q5\_K\_M | A little larger, with better quality than Q4 | | Q6\_K | Higher quality when your memory allows | | Q8\_0 | The largest file, closest to the source model | ### Example Load the Q4\_K\_M file and ask it to solve a word problem and show its steps. You get a written line of reasoning followed by an answer. The card gives no sample output, so expect the style to vary with your settings. ### For newcomers and experts If you are new, a quant is a compressed copy of a large model. Start with Q4\_K\_M because it is the smallest, and try one reasoning question before changing anything else. If you know the field, this is a LoRA fine-tune on attention modules only, rank 32 and alpha 32, run for 2 epochs and 762 steps, then merged and quantized with llama.cpp. Three reasoning datasets fed the training. The one benchmark is a small MMLU-Pro smoke test on the merged source model, not on each GGUF, and the author calls it a comparative check, not a full benchmark. Qwen3.6-35B-A3B base model → Supervised fine-tune with LoRA → Merge into one full model → Quantize to GGUF with llama.cpp → Four quant files to choose from ### Good to know - Text only: the base design has a vision encoder, but this run did not train on images or video - Benchmarks: the score was measured on the merged source model, and quantizing can change scores - Other builds: other people have made 2 fine-tuned versions of it MMLU-Pro exact match on 70 questions | Label | Value | | --- | --- | | Base model | 42.86% | | Merged source model | 75.71% | - License: Apache 2.0 - Cost to run: none named, the source lists no API key, account or paid service - Data it reaches: the files you download and the prompts you give your own runtime, and the source names no web or account access - Not for: image or video input, and not backed by a full-scale benchmark, since the one test is a 70-question check ## README ````markdown # Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF Runs a reasoning-tuned Qwen3.6-35B-A3B on your own machine as GGUF files in four quant sizes, trained on Claude Opus 4.6 style reasoning data. For people who want step-by-step answers locally. Made by hesamation 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: These are GGUF quantizations of a Qwen3.6-35B-A3B model that was fine-tuned for reasoning. The training data was chain-of-thought material in the style of Claude Opus 4.6. The files cover text only, so image and video input is not part of what was trained. Who it suits: It suits people who want to run a reasoning-focused model on their own computer and compare it with its base model. ```table Industry | Who uses it | How they use it | Example Local AI | Hobbyists with strong hardware | Run a 35B reasoning model offline | Working through a logic puzzle step by step Research | Fine-tuning researchers | Study the effect of reasoning distillation on a base model | Comparing answers with the original Qwen model Software | Developers | Try a reasoning model in a local llama.cpp setup | Asking a technical question and reading the working Education | Students and tutors | Read worked reasoning on problems | Walking through a maths question ``` Skip it if you need image input or have little memory, since the 4-bit weights alone are about 17.3 GB by estimate. Where to use it: - llama.cpp and other runtimes that read GGUF files - Windows, macOS or Linux with Python, for downloading the files - Local chat or scripting setups where you pick the quant that fits your memory ```table Use case | Who | What they get back Reasoning questions | Local AI users | Answers that show their working Model comparison | Researchers | A tuned model to test against the base Qwen model Quant choice | Anyone tuning memory use | Four file sizes to trade quality against memory ``` How to set it up: You need these first. - Python with pip - A runtime that loads GGUF files, such as llama.cpp - Enough memory for your chosen quant, starting near 17.3 GB for 4-bit 1. Install the Hugging Face hub library with pip. 2. Download the model files with the hf command. 3. Pick one quant file, since the card says files arrive as each quantization finishes. 4. Load that file in your GGUF runtime, following its own instructions. ```sh pip install -U huggingface_hub hf download hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF ``` How to use it: Load one quant file in your runtime and chat with it as a reasoning model. The training used the qwen3-thinking chat template. The card gives no run command of its own, so the exact steps depend on your runtime. ```table Tool | What it does Q4_K_M | The smallest everyday choice for local use Q5_K_M | A little larger, with better quality than Q4 Q6_K | Higher quality when your memory allows Q8_0 | The largest file, closest to the source model ``` Example: Load the Q4_K_M file and ask it to solve a word problem and show its steps. You get a written line of reasoning followed by an answer. The card gives no sample output, so expect the style to vary with your settings. For newcomers and experts: If you are new, a quant is a compressed copy of a large model. Start with Q4_K_M because it is the smallest, and try one reasoning question before changing anything else. If you know the field, this is a LoRA fine-tune on attention modules only, rank 32 and alpha 32, run for 2 epochs and 762 steps, then merged and quantized with llama.cpp. Three reasoning datasets fed the training. The one benchmark is a small MMLU-Pro smoke test on the merged source model, not on each GGUF, and the author calls it a comparative check, not a full benchmark. ```flow Qwen3.6-35B-A3B base model Supervised fine-tune with LoRA Merge into one full model Quantize to GGUF with llama.cpp Four quant files to choose from ``` Good to know: - Text only: the base design has a vision encoder, but this run did not train on images or video - Benchmarks: the score was measured on the merged source model, and quantizing can change scores - Other builds: other people have made 2 fine-tuned versions of it ```chart Title: MMLU-Pro exact match on 70 questions Base model: 42.86% Merged source model: 75.71% ``` - License: Apache 2.0 - Cost to run: none named, the source lists no API key, account or paid service - Data it reaches: the files you download and the prompts you give your own runtime, and the source names no web or account access - Not for: image or video input, and not backed by a full-scale benchmark, since the one test is a 70-question check ## On Hugging Face, 13 November 2026 - Task: text generation - Library: gguf - Parameters: 34.7B - Weights: about 17.3 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 17,179 - Likes: 296 - License: apache-2.0 - Languages: en - Based on: hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled - Fine-tunes: 2 - Tags: gguf, llama.cpp, qwen, qwen3.6, qwen3_5_moe, reasoning, chain-of-thought, conversational, quantized, unsloth, text-generation, model-index ## Links - Model: https://huggingface.co/hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF - Files: https://huggingface.co/hesamation/Qwen3.6-35B-A3B-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 hesamation/Qwen3.6-35B-A3B-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, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF on Hugging Face](https://huggingface.co/hesamation/Qwen3.6-35B-A3B-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 | | --- | --- | --- | --- | --- | --- | | Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF (this page) | 17,179 | 296 | 34.7B | apache-2.0 | 2026-04-19 | | [Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-GGUF](https://mindket.com/p/e69239ed-602f-46a8-8c61-e1bb0dd73291) | 39,987 | 188 | 4.2B | apache-2.0 | 2026-04-06 | | [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 | | [Qwen3.8-Whittle-MoE-27B-MLX-4bit](https://mindket.com/p/62c224d0-5cf9-4116-b0e4-4faeb9832a1a) | 1,611 | 11 | 26.9B | apache-2.0 | 2026-08-24 | | [Qwen3.8-35B-A3B-Distill](https://mindket.com/p/db646c99-e7db-4a3b-9573-75557227bfa0) | 4,390 | 103 | 35.1B | apache-2.0 | 2026-09-16 | | [Qwen3.6-35B-A3B-NVFP4](https://mindket.com/p/eb2cda8c-206e-4479-89fa-20515e3a0444) | 4,104,345 | 678 | 18.7B | apache-2.0 | 2026-08-29 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · abtinf · 2026-05-19](https://news.ycombinator.com/item?id=48197486): Gemini 3.5 Flash: hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF @ Q6\_K 8112 tokens @ 52.97 TPS, 0.85s TTFT https://gistpreview.github.io/?7bdefff99aca89d1bc12405323bd4... Full session: https://gist.github.com/abtinf/7bdefff99aca89d1bc12405323bd4... Generated with LM Studio on a Macbook Pro M2… ## 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: 2026-04-19 - First published: 2026-04-18 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF on Hugging Face](https://huggingface.co/hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/2b5092f0-78de-42d0-ae72-417592fa56e9) - [Its page on Mindket](https://mindket.com/p/2b5092f0-78de-42d0-ae72-417592fa56e9)