Hugging Face's logo

ubergarm
/
GLM-4.5-Air-GGUF

Text Generation
GGUF
imatrix
conversational
ik_llama.cpp
Model card Files Community
4

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 with Windows builds for CUDA 12.9. Also check for Windows builds by Thireus here. which have been CUDA 12.8.

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
$ git clone https://github.com/ikawrakow/ik_llama.cpp
$ cd ik_llama.cpp

# 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)

# Run API server
$ ./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

Downloads last month
5,259
GGUF
Model size
110B params
Architecture
glm4moe
Hardware compatibility
Log In to view the estimation

2-bit

View +1 variant
Inference Providers NEW
Text Generation
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for ubergarm/GLM-4.5-Air-GGUF

Base model

zai-org/GLM-4.5-Air
Quantized
(29)
this model