ubergarm/GLM-4.5-Air-GGUF

This is an experimental place-holder with an imatrix not for general purpose use just yet. I'm not releasing any quants for this just yet until the various PRs are in place and tested better.

Check the References below for the the github discussion as folks are working on adding support for this model.

Keep an eye out for new PR and follow along, once this thing is tested and considered working correctly I hope to release some quants for both this smaller Air model and the larger one too..

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.

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

• BF16 203.436 GiB (16.004 BPW)
  • Final estimate: PPL = TODO
• Q8_0 108.119 GiB (8.505 BPW)
  • Final estimate: PPL = TODO

IQ4_KSS 54.124 GiB (4.258 BPW)

👈 Secret Recipe

#!/usr/bin/env bash

# 620756992 |  4096, 151552,     1,     1 | Q8_0    | token_embd.weight
#
#  44826624 | 10944,   4096,     1,     1 | Q8_0    | blk.0.ffn_down.weight
#  44826624 |  4096,  10944,     1,     1 | Q8_0    | blk.0.ffn_gate.weight
#  44826624 |  4096,  10944,     1,     1 | Q8_0    | blk.0.ffn_up.weight
#      4096 |  4096,      1,     1,     1 | F32     | blk.0.attn_norm.weight
#      4096 |  4096,      1,     1,     1 | F32     | blk.0.ffn_norm.weight
#      1024 |  1024,      1,     1,     1 | F32     | blk.0.attn_k.bias
#   4194304 |  4096,   1024,     1,     1 | Q8_0    | blk.0.attn_k.weight
#  50331648 | 12288,   4096,     1,     1 | Q8_0    | blk.0.attn_output.weight
#   4194304 |  4096,   1024,     1,     1 | Q8_0    | blk.0.attn_v.weight
#  50331648 |  4096,  12288,     1,     1 | Q8_0    | blk.0.attn_q.weight
#     12288 | 12288,      1,     1,     1 | F32     | blk.0.attn_q.bias
#      1024 |  1024,      1,     1,     1 | F32     | blk.0.attn_v.bias
#
# 738197504 |  1408,   4096,   128,     1 | Q8_0    | blk.1.ffn_down_exps.weight
# 738197504 |  4096,   1408,   128,     1 | Q8_0    | blk.1.ffn_gate_exps.weight
# 738197504 |  4096,   1408,   128,     1 | Q8_0    | blk.1.ffn_up_exps.weight
#      4096 |  4096,      1,     1,     1 | F32     | blk.1.attn_norm.weight
#       128 |   128,      1,     1,     1 | F32     | blk.1.ffn_gate_inp.bias
#    524288 |  4096,    128,     1,     1 | F32     | blk.1.ffn_gate_inp.weight
#   5767168 |  1408,   4096,     1,     1 | Q8_0    | blk.1.ffn_down_shexp.weight
#   5767168 |  4096,   1408,     1,     1 | Q8_0    | blk.1.ffn_gate_shexp.weight
#   5767168 |  4096,   1408,     1,     1 | Q8_0    | blk.1.ffn_up_shexp.weight
#   4194304 |  4096,   1024,     1,     1 | Q8_0    | blk.1.attn_k.weight
#  50331648 | 12288,   4096,     1,     1 | Q8_0    | blk.1.attn_output.weight
#  50331648 |  4096,  12288,     1,     1 | Q8_0    | blk.1.attn_q.weight
#   4194304 |  4096,   1024,     1,     1 | Q8_0    | blk.1.attn_v.weight
#      4096 |  4096,      1,     1,     1 | F32     | blk.1.ffn_norm.weight
#      1024 |  1024,      1,     1,     1 | F32     | blk.1.attn_k.bias
#     12288 | 12288,      1,     1,     1 | F32     | blk.1.attn_q.bias
#      1024 |  1024,      1,     1,     1 | F32     | blk.1.attn_v.bias

# 620756992 |  4096, 151552,     1,     1 | Q8_0    | output.weight

custom="
# 47 Repeating Layers [0-46]

# Attention
#blk\.(0)\.attn_q.*=q8_0
#blk\.(0)\.attn_k.*=q8_0
#blk\.(0)\.attn_v.*=q8_0
#blk\.(0)\.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 [2-46]
blk\..*\.ffn_down_shexp\.weight=q6_0
blk\..*\.ffn_(gate|up)_shexp\.weight=iq5_ks

# Routed Experts Layers [2-46]
#blk\.(3|92)\.ffn_down_exps\.weight=q8_0
#blk\.(3|92)\.ffn_(gate|up)_exps\.weight=q8_0

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

# 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-128x8.1B-BF16-00001-of-00005.gguf \
    /mnt/raid/models/ubergarm/GLM-4.5-Air-GGUF/GLM-4.5-Air-IQ4_KSS.gguf \
    IQ4_KSS \
    192

Quick Start

# Clone and checkout experimental PR
$ 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 WARNING-EXPERIMENTAL-IKLLAMACPP-ONLY-GLM-4.5-Air-IQ4_KSS-00001-of-00002.gguf \
    --alias ubergarm/GLM-4.5-Air-IQ4_KSS \
    --ctx-size 32768 \
    -fa -fmoe \
    -ctk q8_0 -ctv q8_0 \
    --chat-template chatglm4 \
    --override-kv tokenizer.ggml.eot_token_id=int:151336 \
    -ub 4096 -b 4096 \
    -ngl 99 \
    -ot exps=CPU \
    --parallel 1 \
    --threads 8 \
    --host 127.0.0.1 \
    --port 8080 \
    --no-mmap

References

• ik_llama.cpp
• Getting Started Guide (already out of date lol)
• ubergarm-imatrix-calibration-corpus-v02.txt
• Mainline llama.cpp Draft PR14939
• ik_llama.cpp GLM-4.5 MoE PR668