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sharpcaterpillar/Qwen3.6-35B-A3B-GGUF
Qwen3.6-35B-A3B-GGUF is a machine learning model from sharpcaterpillar. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
This repository contains GGUF format model files for Qwen/Qwen3.6-35B-A3B.
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Updated Apr 16, 2026
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From the Hugging Face model README
This repository contains GGUF format model files for Qwen/Qwen3.6-35B-A3B.
| File Name | Format | Size | Description |
|---|---|---|---|
Qwen3.6-35B-A3B-Q4_K_M.gguf | Q4_K_M | ~20.5 GB | Recommended. 4-bit quantization using the K-quants method. Offers an excellent balance between inference speed, memory consumption, and model quality. |
Qwen3.6-35B-A3B-F16.gguf | FP16 | ~70.0 GB | Unquantized 16-bit precision. Extremely large file size. Mainly provided for researchers or users who wish to apply their own custom quantization methods. |
Note: Please check the exact file sizes in the "Files and versions" tab.
Unlike most conversions performed on Nvidia hardware, this GGUF conversion was executed on an AMD APU platform leveraging a massive Unified Memory Architecture (UMA):
llama.cpp compiled specifically for Windows ROCm (gfx1151 architecture).The massive UMA pool allowed for the efficient handling and conversion of the 70GB+ unquantized FP16 tensors directly in system RAM before being quantized via the hardware's compute units.
llama.cppYou can run this model locally using the llama.cpp CLI. Download the Q4_K_M version and run the following command in your terminal:
bash
llama-cli.exe -m Qwen3.6-35B-A3B-Q4_K_M.gguf -p "You are a helpful assistant. Please explain the theory of relativity." -n 1024 -c 4096 -ngl 99
Parameters explanation:
-m: Path to the downloaded GGUF model.-p: Your prompt.-n: Maximum number of tokens to predict/generate.-c: Context window size (adjust based on your available memory).-ngl: Number of layers to offload to the GPU. For 35B models, set this to a high number (like 99) if you have 24GB+ of VRAM to fully offload the model for maximum speed.All credit for the original model architecture and weights goes to the Qwen Team.