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URL: https://huggingface.co/bartowski/Qwen_Qwen3.5-27B-GGUF

⇱ bartowski/Qwen_Qwen3.5-27B-GGUF · Hugging Face


Llamacpp imatrix Quantizations of Qwen3.5-27B by Qwen

Using llama.cpp release b9222 for quantization.

Original model: https://huggingface.co/Qwen/Qwen3.5-27B

All quants made using imatrix option with dataset from here

Run them in your choice of tools:

Note: if it's a newly supported model, you may need to wait for an update from the developers.

Prompt format

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
<think>

What's new:

Adding MTP layers now that support has been added with release b9180

In order to use it, run:

--spec-type draft-mtp

For more details, check out the PR: #22673

Download a file (not the whole branch) from below:

Filename Quant type File Size Split Description
Qwen3.5-27B-bf16.gguf bf16 54.66GB true Full BF16 weights.
Qwen3.5-27B-Q8_0.gguf Q8_0 29.12GB false Extremely high quality, generally unneeded but max available quant.
Qwen3.5-27B-Q6_K_L.gguf Q6_K_L 24.29GB false Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended.
Qwen3.5-27B-Q6_K.gguf Q6_K 23.68GB false Very high quality, near perfect, recommended.
Qwen3.5-27B-Q5_K_L.gguf Q5_K_L 21.75GB false Uses Q8_0 for embed and output weights. High quality, recommended.
Qwen3.5-27B-Q5_K_M.gguf Q5_K_M 20.97GB false High quality, recommended.
Qwen3.5-27B-Q5_K_S.gguf Q5_K_S 19.89GB false High quality, recommended.
Qwen3.5-27B-Q4_K_L.gguf Q4_K_L 18.93GB false Uses Q8_0 for embed and output weights. Good quality, recommended.
Qwen3.5-27B-Q4_1.gguf Q4_1 18.04GB false Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon.
Qwen3.5-27B-Q4_K_M.gguf Q4_K_M 17.98GB false Good quality, default size for most use cases, recommended.
Qwen3.5-27B-Q4_K_S.gguf Q4_K_S 16.93GB false Slightly lower quality with more space savings, recommended.
Qwen3.5-27B-Q3_K_XL.gguf Q3_K_XL 16.60GB false Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability.
Qwen3.5-27B-Q4_0.gguf Q4_0 16.56GB false Legacy format, offers online repacking for ARM and AVX CPU inference.
Qwen3.5-27B-IQ4_NL.gguf IQ4_NL 16.54GB false Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference.
Qwen3.5-27B-IQ4_XS.gguf IQ4_XS 15.78GB false Decent quality, smaller than Q4_K_S with similar performance, recommended.
Qwen3.5-27B-Q3_K_L.gguf Q3_K_L 15.49GB false Lower quality but usable, good for low RAM availability.
Qwen3.5-27B-Q3_K_M.gguf Q3_K_M 14.82GB false Low quality.
Qwen3.5-27B-IQ3_M.gguf IQ3_M 14.12GB false Medium-low quality, new method with decent performance comparable to Q3_K_M.
Qwen3.5-27B-Q3_K_S.gguf Q3_K_S 13.93GB false Low quality, not recommended.
Qwen3.5-27B-IQ3_XS.gguf IQ3_XS 13.54GB false Lower quality, new method with decent performance, slightly better than Q3_K_S.
Qwen3.5-27B-Q2_K_L.gguf Q2_K_L 13.29GB false Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable.
Qwen3.5-27B-IQ3_XXS.gguf IQ3_XXS 12.84GB false Lower quality, new method with decent performance, comparable to Q3 quants.
Qwen3.5-27B-Q2_K.gguf Q2_K 12.05GB false Very low quality but surprisingly usable.
Qwen3.5-27B-IQ2_M.gguf IQ2_M 11.09GB false Relatively low quality, uses SOTA techniques to be surprisingly usable.
Qwen3.5-27B-IQ2_S.gguf IQ2_S 10.51GB false Low quality, uses SOTA techniques to be usable.
Qwen3.5-27B-IQ2_XS.gguf IQ2_XS 10.20GB false Low quality, uses SOTA techniques to be usable.
Qwen3.5-27B-IQ2_XXS.gguf IQ2_XXS 9.61GB false Very low quality, uses SOTA techniques to be usable.

Embed/output weights

Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

Downloading using huggingface-cli

ARM/AVX information

Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.

Now, however, there is something called "online repacking" for weights. details in this PR. If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.

As of llama.cpp build b4282 you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.

Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to this PR which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.

Which file should I choose?

Credits

Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.

Thank you ZeroWw for the inspiration to experiment with embed/output.

Thank you to LM Studio for sponsoring my work.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

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