VOOZH about

URL: https://huggingface.co/bartowski/Qwen_Qwen3.5-397B-A17B-GGUF

⇱ bartowski/Qwen_Qwen3.5-397B-A17B-GGUF · Hugging Face


Llamacpp imatrix Quantizations of Qwen3.5-397B-A17B by Qwen

Using llama.cpp release b9222 for quantization.

Original model: https://huggingface.co/Qwen/Qwen3.5-397B-A17B

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
Qwen3.5-397B-A17B-Q8_0.gguf Q8_0 428.59GB true Extremely high quality, generally unneeded but max available quant.
Qwen3.5-397B-A17B-Q6_K_L.gguf Q6_K_L 349.37GB true Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended.
Qwen3.5-397B-A17B-Q6_K.gguf Q6_K 348.88GB true Very high quality, near perfect, recommended.
Qwen3.5-397B-A17B-Q5_K_L.gguf Q5_K_L 290.61GB true Uses Q8_0 for embed and output weights. High quality, recommended.
Qwen3.5-397B-A17B-Q5_K_M.gguf Q5_K_M 289.98GB true High quality, recommended.
Qwen3.5-397B-A17B-Q5_K_S.gguf Q5_K_S 281.00GB true High quality, recommended.
Qwen3.5-397B-A17B-Q4_1.gguf Q4_1 256.10GB true Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon.
Qwen3.5-397B-A17B-Q4_K_L.gguf Q4_K_L 249.01GB true Uses Q8_0 for embed and output weights. Good quality, recommended.
Qwen3.5-397B-A17B-Q4_K_M.gguf Q4_K_M 248.26GB true Good quality, default size for most use cases, recommended.
Qwen3.5-397B-A17B-Q4_K_S.gguf Q4_K_S 239.57GB true Slightly lower quality with more space savings, recommended.
Qwen3.5-397B-A17B-Q4_0.gguf Q4_0 232.44GB true Legacy format, offers online repacking for ARM and AVX CPU inference.
Qwen3.5-397B-A17B-IQ4_NL.gguf IQ4_NL 231.52GB true Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference.
Qwen3.5-397B-A17B-IQ4_XS.gguf IQ4_XS 219.24GB true Decent quality, smaller than Q4_K_S with similar performance, recommended.
Qwen3.5-397B-A17B-Q3_K_XL.gguf Q3_K_XL 196.82GB true Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability.
Qwen3.5-397B-A17B-IQ3_M.gguf IQ3_M 196.01GB true Medium-low quality, new method with decent performance comparable to Q3_K_M.
Qwen3.5-397B-A17B-Q3_K_L.gguf Q3_K_L 195.93GB true Lower quality but usable, good for low RAM availability.
Qwen3.5-397B-A17B-Q3_K_M.gguf Q3_K_M 188.21GB true Low quality.
Qwen3.5-397B-A17B-IQ3_XS.gguf IQ3_XS 188.16GB true Lower quality, new method with decent performance, slightly better than Q3_K_S.
Qwen3.5-397B-A17B-Q3_K_S.gguf Q3_K_S 179.94GB true Low quality, not recommended.
Qwen3.5-397B-A17B-IQ3_XXS.gguf IQ3_XXS 172.70GB true Lower quality, new method with decent performance, comparable to Q3 quants.
Qwen3.5-397B-A17B-Q2_K_L.gguf Q2_K_L 147.28GB true Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable.
Qwen3.5-397B-A17B-Q2_K.gguf Q2_K 146.28GB true Very low quality but surprisingly usable.
Qwen3.5-397B-A17B-IQ2_M.gguf IQ2_M 139.90GB true Relatively low quality, uses SOTA techniques to be surprisingly usable.
Qwen3.5-397B-A17B-IQ2_S.gguf IQ2_S 127.31GB true Low quality, uses SOTA techniques to be usable.
Qwen3.5-397B-A17B-IQ2_XS.gguf IQ2_XS 125.40GB true Low quality, uses SOTA techniques to be usable.
Qwen3.5-397B-A17B-IQ2_XXS.gguf IQ2_XXS 113.25GB true Very low quality, uses SOTA techniques to be usable.
Qwen3.5-397B-A17B-IQ1_M.gguf IQ1_M 98.28GB true Extremely low quality, not recommended.
Qwen3.5-397B-A17B-IQ1_S.gguf IQ1_S 88.73GB true Extremely low quality, not recommended.

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

Downloads last month
66,490
GGUF
Model size
403B params
Architecture
qwen35moe
Hardware compatibility
Log In to add your hardware

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for bartowski/Qwen_Qwen3.5-397B-A17B-GGUF

Quantized
(66)
this model