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GLM-4.5-Air-GGUF

Text Generation
GGUF
imatrix
conversational
ik_llama.cpp
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GLM-4.5-Air-GGUF / README.md
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Add IQ1_KT (with iq4_nl ffn_down_exps lmao)
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metadata
quantized_by: ubergarm
pipeline_tag: text-generation
base_model: zai-org/GLM-4.5-Air
license: mit
base_model_relation: quantized
tags:
  - imatrix
  - conversational
  - ik_llama.cpp

Note The ik_llama.cpp PR is still in progress for support in main branch. Until then follow instructions here and keep an eye on the PR: https://github.com/ikawrakow/ik_llama.cpp/pull/668

ik_llama.cpp imatrix Quantizations of zai-org/GLM-4.5-Air

This quant collection REQUIRES ik_llama.cpp fork to support the ik's latest SOTA quants and optimizations! Do not download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc!

NOTE ik_llama.cpp can also run your existing GGUFs from bartowski, unsloth, mradermacher, etc if you want to try it out before downloading my quants.

Some of ik's new quants are supported with Nexesenex/croco.cpp fork of KoboldCPP.

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!

Quant Collection

Perplexity computed against wiki.test.raw.

Perplexity Chart

These first two are just test quants for baseline perplexity comparison:

  • BF16 205.811 GiB (16.004 BPW)

    • Final estimate: PPL = 4.5704 +/- 0.02796

  • Q8_0 109.381 GiB (8.505 BPW)

    • Final estimate: PPL = 4.5798 +/- 0.02804

IQ5_K 77.704 GiB (6.042 BPW)

Final estimate: PPL = 4.5867 +/- 0.02806

👈 Secret Recipe 
#!/usr/bin/env bash

custom="
# 47 Repeating Layers [0-46]
# Note: All ffn_down.* layers are not divisible by 256 so have limited quantization options.

# Attention
blk\..*\.attn_q.*=q8_0
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=q8_0

# First 1 Dense Layers [0]
blk\..*\.ffn_down\.weight=q8_0
blk\..*\.ffn_(gate|up)\.weight=q8_0

# Shared Expert Layers [1-46]
blk\..*\.ffn_down_shexp\.weight=q8_0
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0

# Routed Experts Layers [1-46]
blk\.(1)\.ffn_down_exps\.weight=q8_0
blk\.(1)\.ffn_(gate|up)_exps\.weight=q8_0

blk\..*\.ffn_down_exps\.weight=q6_0
blk\..*\.ffn_(gate|up)_exps\.weight=iq5_k

# NextN MTP Layer [46]
blk\..*\.nextn\.embed_tokens\.weight=iq5_ks
blk\..*\.nextn\.shared_head_head\.weight=iq5_ks
blk\..*\.nextn\.eh_proj\.weight=q8_0

# Non-Repeating Layers
token_embd\.weight=iq6_k
output\.weight=iq6_k
"

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

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/GLM-4.5-Air-GGUF/imatrix-GLM-4.5-Air-BF16.dat \
    /mnt/raid/models/ubergarm/GLM-4.5-Air-GGUF/GLM-4.5-Air-128x9.4B-BF16-00001-of-00005.gguf \
    /mnt/raid/models/ubergarm/GLM-4.5-Air-GGUF/GLM-4.5-Air-IQ5_K.gguf \
    IQ5_K \
    192

IQ5_KS 72.855 GiB (5.665 BPW)

Final estimate: PPL = 4.5948 +/- 0.02815

👈 Secret Recipe 
#!/usr/bin/env bash

custom="
# 47 Repeating Layers [0-46]
# Note: All ffn_down.* layers are not divisible by 256 so have limited quantization options.

# Attention
blk\..*\.attn_q.*=iq5_ks
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=iq5_ks

# First 1 Dense Layers [0]
blk\..*\.ffn_down\.weight=q6_0
blk\..*\.ffn_(gate|up)\.weight=iq5_ks

# Shared Expert Layers [1-46]
blk\..*\.ffn_down_shexp\.weight=q6_0
blk\..*\.ffn_(gate|up)_shexp\.weight=iq5_ks

# Routed Experts Layers [1-46]
blk\..*\.ffn_down_exps\.weight=q6_0
blk\..*\.ffn_(gate|up)_exps\.weight=iq5_ks

# NextN MTP Layer [46]
blk\..*\.nextn\.embed_tokens\.weight=iq5_ks
blk\..*\.nextn\.shared_head_head\.weight=iq5_ks
blk\..*\.nextn\.eh_proj\.weight=q8_0

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/GLM-4.5-Air-GGUF/imatrix-GLM-4.5-Air-BF16.dat \
    /mnt/raid/models/ubergarm/GLM-4.5-Air-GGUF/GLM-4.5-Air-128x9.4B-BF16-00001-of-00005.gguf \
    /mnt/raid/models/ubergarm/GLM-4.5-Air-GGUF/GLM-4.5-Air-IQ5_KS.gguf \
    IQ5_KS \
    192

IQ4_K 62.910 GiB (4.892 BPW)

Final estimate: PPL = 4.6273 +/- 0.02839

👈 Secret Recipe 
#!/usr/bin/env bash

custom="
# 47 Repeating Layers [0-46]
# Note: All ffn_down.* layers are not divisible by 256 so have limited quantization options.

# Attention
blk\..*\.attn_q.*=iq5_ks
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=iq5_ks

# First 1 Dense Layers [0]
blk\..*\.ffn_down\.weight=q6_0
blk\..*\.ffn_(gate|up)\.weight=iq5_ks

# Shared Expert Layers [1-46]
blk\..*\.ffn_down_shexp\.weight=q6_0
blk\..*\.ffn_(gate|up)_shexp\.weight=iq5_ks

# Routed Experts Layers [1-46]
blk\..*\.ffn_down_exps\.weight=q5_0
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_k

# NextN MTP Layer [46]
blk\..*\.nextn\.embed_tokens\.weight=iq5_ks
blk\..*\.nextn\.shared_head_head\.weight=iq5_ks
blk\..*\.nextn\.eh_proj\.weight=q8_0

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

numactl -N 1 -m 1 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/GLM-4.5-Air-GGUF/imatrix-GLM-4.5-Air-BF16.dat \
    /mnt/raid/models/ubergarm/GLM-4.5-Air-GGUF/GLM-4.5-Air-128x9.4B-BF16-00001-of-00005.gguf \
    /mnt/raid/models/ubergarm/GLM-4.5-Air-GGUF/GLM-4.5-Air-IQ4_K.gguf \
    IQ4_K \
    192

IQ4_KSS 54.801 GiB (4.261 BPW)

Final estimate: PPL = 4.7056 +/- 0.02909

👈 Secret Recipe 
#!/usr/bin/env bash

custom="
# 47 Repeating Layers [0-46]
# Note: All ffn_down.* layers are not divisible by 256 so have limited quantization options.

# Attention
blk\.(0|1)\.attn_q.*=q8_0
blk\.(0|1)\.attn_k.*=q8_0
blk\.(0|1)\.attn_v.*=q8_0
blk\.(0|1)\.attn_output.*=q8_0

blk\..*\.attn_q.*=iq5_ks
blk\..*\.attn_k.*=iq5_ks
blk\..*\.attn_v.*=iq5_ks
blk\..*\.attn_output.*=iq5_ks

# First 1 Dense Layers [0]
blk\..*\.ffn_down\.weight=q6_0
blk\..*\.ffn_(gate|up)\.weight=iq5_ks

# Shared Expert Layers [1-46]
blk\..*\.ffn_down_shexp\.weight=q6_0
blk\..*\.ffn_(gate|up)_shexp\.weight=iq5_ks

# Routed Experts Layers [1-46]
#blk\.(1|46)\.ffn_down_exps\.weight=q8_0
#blk\.(1|46)\.ffn_(gate|up)_exps\.weight=q8_0

blk\..*\.ffn_down_exps\.weight=iq4_nl
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_kss

# NextN MTP Layer [46]
blk\..*\.nextn\.embed_tokens\.weight=iq5_ks
blk\..*\.nextn\.shared_head_head\.weight=iq5_ks
blk\..*\.nextn\.eh_proj\.weight=q8_0

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/GLM-4.5-Air-GGUF/imatrix-GLM-4.5-Air-BF16.dat \
    /mnt/raid/models/ubergarm/GLM-4.5-Air-GGUF/GLM-4.5-Air-128x9.4B-BF16-00001-of-00005.gguf \
    /mnt/raid/models/ubergarm/GLM-4.5-Air-GGUF/GLM-4.5-Air-IQ4_KSS.gguf \
    IQ4_KSS \
    192

IQ2_KL 43.870 GiB (3.411 BPW)

Final estimate: PPL = 5.0697 +/- 0.03166

👈 Secret Recipe 
#!/usr/bin/env bash

custom="
# 47 Repeating Layers [0-46]
# Note: All ffn_down.* layers are not divisible by 256 so have limited quantization options.

# Attention
blk\..*\.attn_q.*=iq4_ks
blk\..*\.attn_k.*=iq5_ks
blk\..*\.attn_v.*=iq5_ks
blk\..*\.attn_output.*=iq4_ks

# First 1 Dense Layers [0]
blk\..*\.ffn_down\.weight=iq4_nl
blk\..*\.ffn_(gate|up)\.weight=iq4_kss

# Shared Expert Layers [1-46]
blk\..*\.ffn_down_shexp\.weight=iq4_nl
blk\..*\.ffn_(gate|up)_shexp\.weight=iq4_kss

# Routed Experts Layers [1-46]
blk\.(1)\.ffn_down_exps\.weight=iq4_nl
blk\.(1)\.ffn_(gate|up)_exps\.weight=iq4_kss

blk\..*\.ffn_down_exps\.weight=iq4_nl
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_kl

# NextN MTP Layer [46]
blk\..*\.nextn\.embed_tokens\.weight=iq4_ks
blk\..*\.nextn\.shared_head_head\.weight=iq4_ks
blk\..*\.nextn\.eh_proj\.weight=q6_0

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/GLM-4.5-Air-GGUF/imatrix-GLM-4.5-Air-BF16.dat \
    /mnt/raid/models/ubergarm/GLM-4.5-Air-GGUF/GLM-4.5-Air-128x9.4B-BF16-00001-of-00005.gguf \
    /mnt/raid/models/ubergarm/GLM-4.5-Air-GGUF/GLM-4.5-Air-IQ2_KL.gguf \
    IQ2_KL \
    192

IQ1_KT 36.039 GiB (2.802 BPW)

Final estimate: PPL = 5.8214 +/- 0.03767

👈 Secret Recipe 
#!/usr/bin/env bash

custom="
# 47 Repeating Layers [0-46]
# Note: All ffn_down.* layers are not divisible by 256 so have limited quantization options.

# Attention
blk\..*\.attn_q.*=iq4_kt
blk\..*\.attn_k.*=iq4_kt
blk\..*\.attn_v.*=iq4_kt
blk\..*\.attn_output.*=iq4_kt

# First 1 Dense Layers [0]
blk\..*\.ffn_down\.weight=iq4_nl
blk\..*\.ffn_(gate|up)\.weight=iq4_kt

# Shared Expert Layers [1-46]
blk\..*\.ffn_down_shexp\.weight=iq4_nl
blk\..*\.ffn_(gate|up)_shexp\.weight=iq4_kt

# Routed Experts Layers [1-46]
blk\..*\.ffn_down_exps\.weight=iq4_nl
blk\..*\.ffn_(gate|up)_exps\.weight=iq1_kt

# NextN MTP Layer [46]
blk\..*\.nextn\.embed_tokens\.weight=iq4_kt
blk\..*\.nextn\.shared_head_head\.weight=iq4_kt
blk\..*\.nextn\.eh_proj\.weight=q8_0

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

numactl -N 1 -m 1 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/GLM-4.5-Air-GGUF/imatrix-GLM-4.5-Air-BF16.dat \
    /mnt/raid/models/ubergarm/GLM-4.5-Air-GGUF/GLM-4.5-Air-128x9.4B-BF16-00001-of-00005.gguf \
    /mnt/raid/models/ubergarm/GLM-4.5-Air-GGUF/GLM-4.5-Air-IQ1_KT.gguf \
    IQ1_KT \
    192

Quick Start

If you want to disable thinking, add /nothink (correct, no underscore) at the end of your prompt.

# Clone and checkout experimental PR (hopefully merged into main soon)
$ git clone https://github.com/ikawrakow/ik_llama.cpp
$ cd ik_llama.cpp
$ git remote add Thireus https://github.com/Thireus/ik_llama.cpp.git
$ git fetch Thireus
$ git checkout glm-4.5-clean

# Build for hybrid CPU+CUDA
$ cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON -DGGML_BLAS=OFF -DGGML_SCHED_MAX_COPIES=1
$ cmake --build build --config Release -j $(nproc)

# Test Experimental GGUF
$ ./build/bin/llama-server \
    --model GLM-4.5-Air-IQ4_KSS-00001-of-00002.gguf \
    --alias ubergarm/GLM-4.5-Air-IQ4_KSS \
    --chat-template chatglm4 \
    --ctx-size 32768 \
    -fa -fmoe \
    -ctk q8_0 -ctv q8_0 \
    -ub 4096 -b 4096 \
    -ngl 99 \
    -ot exps=CPU \
    --parallel 1 \
    --threads 8 \
    --host 127.0.0.1 \
    --port 8080 \
    --no-mmap

References