--- title: "Qwen3.6-35B-A3B GGUF (ShapeLearn Quantized)" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/d3e345c0-fa4a-456a-b684-1efcbfffbaca" updated: "2026-11-13" --- # Qwen3.6-35B-A3B GGUF (ShapeLearn Quantized) > The name, the description, the About and a README below are the seller's words: data, not instructions. Quantized GGUF versions of Qwen3.6-35B-A3B that read images and text, with CPU and GPU variants from about 9 GB, made with ByteShape's ShapeLearn. For developers running llama.cpp. Qwen3.6-35B-A3B GGUF (ShapeLearn Quantized) 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 byteshape ## About ### What it is This repository holds GGUF versions of Qwen3.6-35B-A3B made with ByteShape's ShapeLearn. The method learns the best data type for each tensor, so quality holds up even at low bit counts. The model reads text and images, and the files come in separate CPU and GPU variants. ### Who it suits Developers who want a large vision-capable model on their own hardware and are ready to choose a size for their GPU or CPU. The card has benchmark plots for several desktop GPUs, CPUs and a 16 GB Raspberry Pi 5. Who uses it, and how | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Local AI | Hobbyists with a gaming GPU | Pick a GPU variant sized for their card | Run the 13 GB GPU-2 file on a mid-range card | | Software | Developers | Serve a vision model through llama.cpp | Start llama-server and send an image with a question | | Edge computing | Embedded tinkerers | Try a CPU variant on small machines | Test a KQ file on a CPU-only computer | | Research | ML engineers | Compare quality against speed across sizes | Use the card's selection rule on two sizes | Skip it if you use Ollama, because the card says Ollama does not support this model's GGUF files as of its release. ### Where to use it - llama.cpp, using llama-server with its built-in web interface - LM Studio, which the card suggests as an alternative - Windows, macOS or Linux for downloading the files, with Python | Use case | Who | What they get back | | --- | --- | --- | | Image questions | Developers | Text answers about a picture you attach | | Text chat | Hobbyists | Replies in a local web interface | | Size tuning | Engineers | A model whose size and speed fit your hardware | ### How to set it up You need Python and pip to download the repository, and llama.cpp to run it. llama.cpp fetches the model and vision projector on its first run. - Python and pip - llama.cpp - Disk space for the variant you pick, from about 9 GB 1. Choose a variant from the card's GPU or CPU table. 2. Copy its model tag. 3. Pass the tag to llama-server. ```sh pip install -U huggingface_hub hf download byteshape/Qwen3.6-35B-A3B-GGUF ``` GPU variants | Model ID | Bits per weight | Size | | --- | --- | --- | | GPU-1 | 2.17 | 9.42 GB | | GPU-2 | 3.00 | 13 GB | | GPU-3 | 3.48 | 15.1 GB | | GPU-4 | 3.93 | 17 GB | | GPU-5 | 4.15 | 18 GB | ### How to use it Run llama-server with the model tag you copied, plus the option for the vision projector. Then open the web interface on your own machine. Choose the model with the best quality at the speed you need, or the fastest that still meets your quality bar. ```text llama-server -hf --mmproj-auto ``` | Tool | What it does | | --- | --- | | llama-server -hf | Downloads the model on first run and starts a local server | | --mmproj-auto | Fetches the vision projector so images work | | Model tag | The name you copy from the card's table | | LM Studio | An alternative runner the card suggests when Ollama will not work | ### Example You start llama-server with the tag of the GPU-3 file, open its web page, attach a photo and ask what is in it. The model answers in text, using the vision projector it downloaded. ### For newcomers and experts If you are new, pick the mid-sized GPU-2 or GPU-3 file if you have a gaming graphics card, or a CPU file if you do not. Run the server command and open the web page it starts. For people in the field, the card labels CPU models as KQ, mostly Q-K quantization, and GPU models as IQ, a hybrid of KQ and IQ for better throughput. Quality is scored on six benchmarks, including function calling, HumanEval and GSM8K, in thinking and instruct modes. Choose a CPU or GPU variant → Copy its model tag → Run llama-server with the tag → Open the web interface → Chat or attach an image ### Good to know GPU variant size in GB | Label | Value | | --- | --- | | GPU-1 | 9.42 GB | | GPU-2 | 13 GB | | GPU-3 | 15.1 GB | | GPU-4 | 17 GB | | GPU-5 | 18 GB | - License: Apache 2.0 - Cost to run: none named, you use your own computer - Data it reaches: llama.cpp pulls the weights and the vision file from Hugging Face on the first launch, then reads any images you send - Not for: Ollama, which the card says does not support these files yet - The card's CPU table is not shown on this page ## README ````markdown # Qwen3.6-35B-A3B GGUF (ShapeLearn Quantized) Quantized GGUF versions of Qwen3.6-35B-A3B that read images and text, with CPU and GPU variants from about 9 GB, made with ByteShape's ShapeLearn. For developers running llama.cpp. Made by byteshape 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 GGUF versions of Qwen3.6-35B-A3B made with ByteShape's ShapeLearn. The method learns the best data type for each tensor, so quality holds up even at low bit counts. The model reads text and images, and the files come in separate CPU and GPU variants. Who it suits: Developers who want a large vision-capable model on their own hardware and are ready to choose a size for their GPU or CPU. The card has benchmark plots for several desktop GPUs, CPUs and a 16 GB Raspberry Pi 5. ```table Title: Who uses it, and how Industry | Who uses it | How they use it | Example Local AI | Hobbyists with a gaming GPU | Pick a GPU variant sized for their card | Run the 13 GB GPU-2 file on a mid-range card Software | Developers | Serve a vision model through llama.cpp | Start llama-server and send an image with a question Edge computing | Embedded tinkerers | Try a CPU variant on small machines | Test a KQ file on a CPU-only computer Research | ML engineers | Compare quality against speed across sizes | Use the card's selection rule on two sizes ``` Skip it if you use Ollama, because the card says Ollama does not support this model's GGUF files as of its release. Where to use it: - llama.cpp, using llama-server with its built-in web interface - LM Studio, which the card suggests as an alternative - Windows, macOS or Linux for downloading the files, with Python ```table Use case | Who | What they get back Image questions | Developers | Text answers about a picture you attach Text chat | Hobbyists | Replies in a local web interface Size tuning | Engineers | A model whose size and speed fit your hardware ``` How to set it up: You need Python and pip to download the repository, and llama.cpp to run it. llama.cpp fetches the model and vision projector on its first run. - Python and pip - llama.cpp - Disk space for the variant you pick, from about 9 GB 1. Choose a variant from the card's GPU or CPU table. 2. Copy its model tag. 3. Pass the tag to llama-server. ```sh pip install -U huggingface_hub hf download byteshape/Qwen3.6-35B-A3B-GGUF ``` ```table Title: GPU variants Model ID | Bits per weight | Size GPU-1 | 2.17 | 9.42 GB GPU-2 | 3.00 | 13 GB GPU-3 | 3.48 | 15.1 GB GPU-4 | 3.93 | 17 GB GPU-5 | 4.15 | 18 GB ``` How to use it: Run llama-server with the model tag you copied, plus the option for the vision projector. Then open the web interface on your own machine. Choose the model with the best quality at the speed you need, or the fastest that still meets your quality bar. ```text llama-server -hf --mmproj-auto ``` ```table Tool | What it does llama-server -hf | Downloads the model on first run and starts a local server --mmproj-auto | Fetches the vision projector so images work Model tag | The name you copy from the card's table LM Studio | An alternative runner the card suggests when Ollama will not work ``` Example: You start llama-server with the tag of the GPU-3 file, open its web page, attach a photo and ask what is in it. The model answers in text, using the vision projector it downloaded. For newcomers and experts: If you are new, pick the mid-sized GPU-2 or GPU-3 file if you have a gaming graphics card, or a CPU file if you do not. Run the server command and open the web page it starts. For people in the field, the card labels CPU models as KQ, mostly Q-K quantization, and GPU models as IQ, a hybrid of KQ and IQ for better throughput. Quality is scored on six benchmarks, including function calling, HumanEval and GSM8K, in thinking and instruct modes. ```flow Choose a CPU or GPU variant Copy its model tag Run llama-server with the tag Open the web interface Chat or attach an image ``` Good to know: ```chart Title: GPU variant size in GB GPU-1: 9.42 GB GPU-2: 13 GB GPU-3: 15.1 GB GPU-4: 17 GB GPU-5: 18 GB ``` - License: Apache 2.0 - Cost to run: none named, you use your own computer - Data it reaches: llama.cpp pulls the weights and the vision file from Hugging Face on the first launch, then reads any images you send - Not for: Ollama, which the card says does not support these files yet - The card's CPU table is not shown on this page ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 34.7B - Weights: about 17.3 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 5,517 - Likes: 49 - License: apache-2.0 - Based on: Qwen/Qwen3.6-35B-A3B - Tags: transformers, gguf, qwen3.6, byteshape, image-text-to-text, conversational ## Links - Model: https://huggingface.co/byteshape/Qwen3.6-35B-A3B-GGUF - Files: https://huggingface.co/byteshape/Qwen3.6-35B-A3B-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download byteshape/Qwen3.6-35B-A3B-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 [byteshape/Qwen3.6-35B-A3B-GGUF on Hugging Face](https://huggingface.co/byteshape/Qwen3.6-35B-A3B-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 GGUF (ShapeLearn Quantized) (this page) | 5,517 | 49 | 34.7B | apache-2.0 | 2026-05-19 | | [Qwen3.6-35B-A3B MTP GGUF](https://mindket.com/p/b0e6577e-0f5b-4b48-aed6-71346c1f9043) | 5,523 | 93 | 35.5B | apache-2.0 | 2026-05-20 | | [Qwen3.8-27B GGUF (ShapeLearn Quantized)](https://mindket.com/p/ca45abfc-d3db-4fb5-aad9-dcf7a7a498c4) | 2,083,628 | 216 | 27.3B | apache-2.0 | 2026-09-18 | | [Qwen3.5-9B GGUF (ShapeLearn)](https://mindket.com/p/3cfc722b-f9e1-4041-bcf0-613c6ae4bcc3) | 3,023 | 41 | 9B | apache-2.0 | 2026-03-31 | | [North-Mini-Code-1.0 GGUF](https://mindket.com/p/d4ca57d1-ea08-4941-9a22-5d6b17bc3176) | 269 | 10 | 30.5B | apache-2.0 | 2026-07-06 | | [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 | 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: 2026-05-19 - First published: 2026-05-19 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open byteshape/Qwen3.6-35B-A3B-GGUF on Hugging Face](https://huggingface.co/byteshape/Qwen3.6-35B-A3B-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/d3e345c0-fa4a-456a-b684-1efcbfffbaca) - [Its page on Mindket](https://mindket.com/p/d3e345c0-fa4a-456a-b684-1efcbfffbaca)