ubergarm/Qwen3.5-397B-A17B-GGUF

Name: ubergarm/Qwen3.5-397B-A17B-GGUF
Author: ubergarm

High-quality GGUF model

8.4K 📥 Downloads

25 ❤️ Likes

1 📁 GGUF Files

1.06 GB 💾 Total Size

1 day ago 🔄 Last Updated

📋 Model Description

quantized_by: ubergarm pipeline_tag: text-generation base_model: Qwen/Qwen3.5-397B-A17B basemodelrelation: quantized license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3.5-397B-A17B/blob/main/LICENSE tags:

imatrix
conversational
qwen35moe
ik_llama.cpp

`ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3.5-397B-A17B

NOTE ikllama.cpp can also run your existing GGUFs from bartowski, unsloth, mradermacher, etc if you want to try it out before downloading my quants. Only a couple quants in this collection are compatible with mainline llamma.cpp/LMStudio/KoboldCPP/etc as mentioned in the specific description, all others require ikllama.cpp.

Some of ik's new quants are supported with Nexesenex/croco.cpp fork of KoboldCPP with Windows builds. Also check for ikllama.cpp windows builds by Thireus here..

These quants provide best in class perplexity for the given memory footprint.

Big Thanks

Shout out to Wendell and the Level1Techs crew, the community Forums, YouTube Channel! BIG thanks for providing BIG hardware expertise and access to run these experiments and make these great quants available to the community!!!

Also thanks to all the folks in the quanting and inferencing community on BeaverAI Club Discord and on r/LocalLLaMA for tips and tricks helping each other run, test, and benchmark all the fun new models! Thanks to huggingface for hosting all these big quants!

Finally, I really appreciate the support from aifoundry.org so check out their open source RISC-V based solutions!

Quant Collection

Perplexity computed against wiki.test.raw. (lower is "better")

!Perplexity Chart

These two are just test quants for baseline perplexity comparison and not available for download here:

BF16 738.493 GiB (16.005 BPW)

- PPL over 580 chunks for n_ctx=512 = 3.4852 +/- 0.01883

Q8_0 392.549 GiB (8.508 BPW)

- PPL over 580 chunks for n_ctx=512 = 3.4862 +/- 0.01883

NOTE: The first split file is much smaller on purpose to only contain metadata, its fine!

IQ4_KSS 194.058 GiB (4.206 BPW)

PPL over 580 chunks for n_ctx=512 = 3.5102 +/- 0.01896

👈 Secret Recipe

#!/usr/bin/env bash

custom="
60 Repeating Layers [0-59]


Gated Attention/Delta Net [Blended 0-59]
blk\..*\.attngate\.weight=q80
blk\..*\.attnqkv\.weight=q80
blk\..*\.attnoutput\.weight=q80
blk\..*\.attnq\.weight=q80
blk\..*\.attnk\.weight=q80
blk\..*\.attnv\.weight=q80
blk\..*\.ssmalpha\.weight=q80
blk\..*\.ssmbeta\.weight=q80
blk\..*\.ssmout\.weight=q80

Shared Expert Layers [0-59]
blk\..*\.ffndownshexp\.weight=q8_0
blk\..*\.ffn(gate|up)shexp\.weight=q8_0

Routed Experts Layers [0-59]
blk\..*\.ffndownexps\.weight=iq4_ks
blk\..*\.ffn(gate|up)exps\.weight=iq4_kss

Non-Repeating Layers
tokenembd\.weight=iq6k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Qwen3.5-397B-A17B-GGUF/imatrix-Qwen3.5-397B-A17B-BF16.dat \
    /mnt/data/models/ubergarm/Qwen3.5-397B-A17B-GGUF/Qwen3.5-397B-A17B-BF16-00001-of-00017.gguf \
    /mnt/data/models/ubergarm/Qwen3.5-397B-A17B-GGUF/Qwen3.5-397B-A17B-IQ4_KSS.gguf \
    IQ4_KSS \
    128

Q3_K 179.97 GiB (3.90 BPW)

PPL over 580 chunks for n_ctx=512 = 3.5409 +/- 0.01924

This is a custom mainline llama.cpp compatible MoE optimized mix similar to AesSedai/ddh0's mixes and likely better than vanilla Q3K mixes. Check the recipe for details.

👈 Secret Recipe

#!/usr/bin/env bash

./build/bin/llama-quantize \ --tensor-type ffndownexps=q4_K \ --tensor-type ffngateexps=q3_K \ --tensor-type ffnupexps=q3_K \ --token-embedding-type q4_K \ --output-tensor-type q6_K \ --imatrix /mnt/data/models/ubergarm/Qwen3.5-397B-A17B-GGUF/imatrix-Qwen3.5-397B-A17B-BF16-mainline.gguf \ /mnt/data/models/ubergarm/Qwen3.5-397B-A17B-GGUF/Qwen3.5-397B-A17B-BF16-00001-of-00017.gguf \ /mnt/data/models/ubergarm/Qwen3.5-397B-A17B-GGUF/Qwen3.5-397B-A17B-Q3_K.gguf \ Q8_0 \ 128

IQ2_KL 138.142 GiB (2.994 BPW)

PPL over 580 chunks for n_ctx=512 = 3.6536 +/- 0.02000

👈 Secret Recipe

#!/usr/bin/env bash

custom="
60 Repeating Layers [0-59]


Gated Attention/Delta Net [Blended 0-59]
blk\..*\.attngate\.weight=q80
blk\..*\.attnqkv\.weight=q80
blk\..*\.attnoutput\.weight=q80
blk\..*\.attnq\.weight=q80
blk\..*\.attnk\.weight=q80
blk\..*\.attnv\.weight=q80
blk\..*\.ssmalpha\.weight=q80
blk\..*\.ssmbeta\.weight=q80
blk\..*\.ssmout\.weight=q80

Shared Expert Layers [0-59]
blk\..*\.ffndownshexp\.weight=q8_0
blk\..*\.ffn(gate|up)shexp\.weight=q8_0

Routed Experts Layers [0-59]
blk\..*\.ffndownexps\.weight=iq3_ks
blk\..*\.ffn(gate|up)exps\.weight=iq2_kl

Non-Repeating Layers
tokenembd\.weight=iq4k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Qwen3.5-397B-A17B-GGUF/imatrix-Qwen3.5-397B-A17B-BF16.dat \
    /mnt/data/models/ubergarm/Qwen3.5-397B-A17B-GGUF/Qwen3.5-397B-A17B-BF16-00001-of-00017.gguf \
    /mnt/data/models/ubergarm/Qwen3.5-397B-A17B-GGUF/Qwen3.5-397B-A17B-IQ2_KL.gguf \
    IQ2_KL \
    128

smol-IQ2_XS 113.41 GiB (2.46 BPW)

PPL over 580 chunks for n_ctx=512 = 3.8717 +/- 0.02131

This is a custom mainline compatible MoE optimized mix similar to AesSedai/ddh0's mixes and likely better than vanilla mixes especially lacking imatrix. Check the recipe for details.

👈 Secret Recipe

#!/usr/bin/env bash

./build/bin/llama-quantize \ --tensor-type ffndownexps=iq2_xs \ --tensor-type ffngateexps=iq2_xs \ --tensor-type ffnupexps=iq2_xs \ --token-embedding-type q4_K \ --output-tensor-type q6_K \ --imatrix /mnt/data/models/ubergarm/Qwen3.5-397B-A17B-GGUF/imatrix-Qwen3.5-397B-A17B-BF16-mainline.gguf \ /mnt/data/models/ubergarm/Qwen3.5-397B-A17B-GGUF/Qwen3.5-397B-A17B-BF16-00001-of-00017.gguf \ /mnt/data/models/ubergarm/Qwen3.5-397B-A17B-GGUF/Qwen3.5-397B-A17B-smol-IQ2_XS.gguf \ Q8_0 \ 128

smol-IQ2_KS 108.142 GiB (2.344 BPW)

PPL over 580 chunks for n_ctx=512 = 3.9153 +/- 0.02172

👈 Secret Recipe

#!/usr/bin/env bash

custom="
60 Repeating Layers [0-59]


Gated Attention/Delta Net [Blended 0-59]
blk\..*\.attngate\.weight=q80
blk\..*\.attnqkv\.weight=q80
blk\..*\.attnoutput\.weight=q80
blk\..*\.attnq\.weight=q80
blk\..*\.attnk\.weight=q80
blk\..*\.attnv\.weight=q80
blk\..*\.ssmalpha\.weight=q80
blk\..*\.ssmbeta\.weight=q80
blk\..*\.ssmout\.weight=q80

Shared Expert Layers [0-59]
blk\..*\.ffndownshexp\.weight=q8_0
blk\..*\.ffn(gate|up)shexp\.weight=q8_0

Routed Experts Layers [0-59]
blk\..*\.ffndownexps\.weight=iq2_ks
blk\..*\.ffn(gate|up)exps\.weight=iq2_ks

Non-Repeating Layers
tokenembd\.weight=iq4k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Qwen3.5-397B-A17B-GGUF/imatrix-Qwen3.5-397B-A17B-BF16.dat \
    /mnt/data/models/ubergarm/Qwen3.5-397B-A17B-GGUF/Qwen3.5-397B-A17B-BF16-00001-of-00017.gguf \
    /mnt/data/models/ubergarm/Qwen3.5-397B-A17B-GGUF/Qwen3.5-397B-A17B-smol-IQ2_KS.gguf \
    IQ2_KS \
    128

smol-IQ1_KT 88.807 GiB (1.925 BPW)

PPL over 580 chunks for n_ctx=512 = 4.2523 +/- 0.02412

👈 Secret Recipe

#!/usr/bin/env bash

custom="
60 Repeating Layers [0-59]


Gated Attention/Delta Net [Blended 0-59]
blk\..*\.attngate\.weight=q80
blk\..*\.attnqkv\.weight=q80
blk\..*\.attnoutput\.weight=q80
blk\..*\.attnq\.weight=q80
blk\..*\.attnk\.weight=q80
blk\..*\.attnv\.weight=q80
blk\..*\.ssmalpha\.weight=q80
blk\..*\.ssmbeta\.weight=q80
blk\..*\.ssmout\.weight=q80

Shared Expert Layers [0-59]
blk\..*\.ffndownshexp\.weight=q8_0
blk\..*\.ffn(gate|up)shexp\.weight=q8_0

Routed Experts Layers [0-59]
blk\..*\.ffndownexps\.weight=iq1_kt
blk\..*\.ffn(gate|up)exps\.weight=iq1_kt

Non-Repeating Layers
tokenembd\.weight=iq4k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Qwen3.5-397B-A17B-GGUF/imatrix-Qwen3.5-397B-A17B-BF16.dat \
    /mnt/data/models/ubergarm/Qwen3.5-397B-A17B-GGUF/Qwen3.5-397B-A17B-BF16-00001-of-00017.gguf \
    /mnt/data/models/ubergarm/Qwen3.5-397B-A17B-GGUF/Qwen3.5-397B-A17B-smol-IQ1_KT.gguf \
    IQ1_KT \
    128

Quick Start

Example command for mainline llama.cpp for now including mmproj from another repo. Just remove the --mmproj if you don't want image capabilities.

# Clone and checkout
$ git clone https://github.com/ikawrakow/ik_llama.cpp
$ cd ik_llama.cpp

Build for hybrid CPU+CUDA
$ cmake -B build -DCMAKEBUILDTYPE=Release -DGGML_CUDA=ON
$ cmake --build build --config Release -j $(nproc)

Download Desired Quants
$ pip install huggingface_hub
$ hf download --local-dir ./ --include=smol-IQ2_XS/*.gguf ubergarm/Qwen3.5-397B-A17B-GGUF

Hybrid CPU+GPU
model=/mnt/raid/models/ubergarm/Qwen3.5-397B-A17B-GGUF/Qwen3.5-397B-A17B-Q3_K-00001-of-00005.gguf

./build/bin/llama-server \
    --model "$model"\
    --mmproj /mnt/raid/models/ubergarm/Qwen3.5-397B-A17B-GGUF/mmproj-BF16.gguf \
    --alias ubergarm/Qwen3.5-392B-A17B \
    -fa on \
    --ctx-size 135168 \
    -ctk q80 -ctv q80 \
    -ub 2048 -b 2048 \
    -fit off \
    -ngl 999 \
    -ot "blk\.(0|1|2|3|4|5|6|7|8|9|10|11|12)\.ffn(gate|up|down)exps.=CUDA0,blk\.(47|47|48|49|50|51|52|53|54|55|56|57|58|59|60)\.ffn(gate|up|down)exps.=CUDA1" \
    --cpu-moe \
    --threads 24 \
    --host 127.0.0.1 \
    --port 8080 \
    --parallel 1 \
    --no-mmap \
    --jinja

This setup was used to generate these results:
AMD Thread Ripper Pro (Zen 4) 7965WX 24x Core
8x32GiB DDR5@4800 (221.41 GB/s via mlc)
Dual RTX A6000 (48GB VRAM each)
Driver: 580.105.08 CUDA: 13.0 P2P: OK NCCL found!

prompt eval time =   20177.37 ms /  4036 tokens (    5.00 ms per token,   200.03 tokens per second)
       eval time =  118034.13 ms /  2525 tokens (   46.75 ms per token,    21.39 tokens per second)
      total time =  138211.50 ms /  6561 tokens

prompt eval time =   53071.66 ms / 11154 tokens (    4.76 ms per token,   210.17 tokens per second)
       eval time =    5012.90 ms /   104 tokens (   48.20 ms per token,    20.75 tokens per second)
      total time =   58084.56 ms / 11258 tokens

CPU-Only
It works but seems to inference very slowly on CPU-only and probably requires at least one GPU to handle attention/kv-cache/delta-net stuff as it is much faster even hybrid CPU+GPU.

If you're using chat completions endpoint, you can disable thinking with --chat-template-kwargs '{"enable_thinking": false }'.

Vibe Coding

Seems like the autoparser branch is working fortunately!

https://github.com/ggml-org/llama.cpp/pull/18675

You can get the freshest version like so:

git remote add pwilkin [email protected]:pwilkin/llama.cpp.git
git fetch pwilkin
git checkout pwilkin/autoparser

compile as normal

Otherwise I noticed when trying opencode without autoparser branch it is spitting this error, still got the error using a chat template with --chat-template-file myTemplate.jinja from another repo.

Template supports tool calls but does not natively describe tools. The fallback behaviour used may produce bad results, inspect prompt w/ --verbose & consider overriding the template.
srv    operator(): got exception: {"error":{"code":500,"message":"\n------------\nWhile executing FilterExpression at line 120, column 73 in source:\n...name, argsvalue in toolcall.arguments|items %}↵                        {{- '<...\n                                           ^\nError: Unknown (built-in) filter 'items' for type String","type":"servererror"}}

References

📂 GGUF File List

📁 Filename 📦 Size ⚡ Download

imatrix-Qwen3.5-397B-A17B-BF16-mainline.gguf

Recommended LFS FP16
1.06 GB Download

📊 Model Information

🆔 Model ID: ubergarm/Qwen3.5-397B-A17B-GGUF

📅 Created: 2 weeks ago

🔄 Last Updated: 1 day ago

📥 Downloads: 8.4K

❤️ Likes: 25

🎯 Difficulty: Beginner

⚙️ Quantization: FP16

🏷️ Tags

ggufimatrixconversationalqwen3_5_moeik_llama.cpptext-generationbase_model:Qwen/Qwen3.5-397B-A17Bbase_model:quantized:Qwen/Qwen3.5-397B-A17Blicense:apache-2.0endpoints_compatibleregion:us

🔗 Related Links

🤗 Visit HuggingFace ⚡ Quick Download

ubergarm/Qwen3.5-397B-A17B-GGUF

📋 Model Description

ik_llama.cpp imatrix Quantizations of Qwen/Qwen3.5-397B-A17B

Big Thanks

Quant Collection

IQ4_KSS 194.058 GiB (4.206 BPW)

60 Repeating Layers [0-59]

Gated Attention/Delta Net [Blended 0-59]

Shared Expert Layers [0-59]

Routed Experts Layers [0-59]

Non-Repeating Layers

Q3_K 179.97 GiB (3.90 BPW)

IQ2_KL 138.142 GiB (2.994 BPW)

60 Repeating Layers [0-59]

Gated Attention/Delta Net [Blended 0-59]

Shared Expert Layers [0-59]

Routed Experts Layers [0-59]

Non-Repeating Layers

smol-IQ2_XS 113.41 GiB (2.46 BPW)

smol-IQ2_KS 108.142 GiB (2.344 BPW)

60 Repeating Layers [0-59]

Gated Attention/Delta Net [Blended 0-59]

Shared Expert Layers [0-59]

Routed Experts Layers [0-59]

Non-Repeating Layers

smol-IQ1_KT 88.807 GiB (1.925 BPW)

60 Repeating Layers [0-59]

Gated Attention/Delta Net [Blended 0-59]

Shared Expert Layers [0-59]

Routed Experts Layers [0-59]

Non-Repeating Layers

Quick Start

Build for hybrid CPU+CUDA

Download Desired Quants

Hybrid CPU+GPU

This setup was used to generate these results:

AMD Thread Ripper Pro (Zen 4) 7965WX 24x Core

8x32GiB DDR5@4800 (221.41 GB/s via mlc)

Dual RTX A6000 (48GB VRAM each)

Driver: 580.105.08 CUDA: 13.0 P2P: OK NCCL found!

CPU-Only

It works but seems to inference very slowly on CPU-only and probably requires at least one GPU to handle attention/kv-cache/delta-net stuff as it is much faster even hybrid CPU+GPU.

Vibe Coding

compile as normal

References

📂 GGUF File List

📊 Model Information

🏷️ Tags

🔗 Related Links

`ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3.5-397B-A17B

`It works but seems to inference very slowly on CPU-only and probably requires at least one GPU to handle attention/kv-cache/delta-net stuff as it is much faster even hybrid CPU+GPU.`

`compile as normal`