GLM-4.7-GGUF

ik_llama.cpp imatrix Quantizations of zai-org/GLM-4.7

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! 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.

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

• BF16 667.598 GiB (16.003 BPW)
  • Final estimate: PPL over 565 chunks for n_ctx=512 = 3.9267 +/- 0.02423
• Q8_0 354.794 GiB (8.505 BPW)
  • Final estimate: PPL over 565 chunks for n_ctx=512 = 3.9320 +/- 0.02428

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

IQ5_K 250.635 GiB (6.008 BPW)

Final estimate: PPL over 565 chunks for n_ctx=512 = 3.9445 +/- 0.02439

<details> <summary>👈 Secret Recipe</summary>

#!/usr/bin/env bash

custom="
# 93 Repeating Layers [0-92]

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

# First 3 Dense Layers [0-2]
blk\..*\.ffn_down\.weight=q8_0
blk\..*\.ffn_(gate|up)\.weight=q8_0

# Shared Expert Layers [3-92]
blk\..*\.ffn_down_shexp\.weight=q8_0
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0

# Routed Experts Layers [3-92]
blk\..*\.ffn_down_exps\.weight=iq6_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq5_k

# NextN MTP Layer [92]
# Leave full q8_0 as supposedly better for MTP
# (doesn't use RAM or VRAM otherwise so its fine)
blk\..*\.nextn\.embed_tokens\.weight=q8_0
blk\..*\.nextn\.shared_head_head\.weight=q8_0
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 ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/GLM-4.7-GGUF/imatrix-GLM-4.7-BF16.dat \
    /mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-160x21B-4.7-BF16-00001-of-00015.gguf \
    /mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-4.7-IQ5_K.gguf \
    IQ5_K \
    128

</details>

IQ4_K 209.436 GiB (5.021 BPW)

Final estimate: PPL over 565 chunks for n_ctx=512 = 3.9879 +/- 0.02478

<details> <summary>👈 Secret Recipe</summary>

#!/usr/bin/env bash

custom="
# 93 Repeating Layers [0-92]

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

# First 3 Dense Layers [0-2]
blk\..*\.ffn_down\.weight=q8_0
blk\..*\.ffn_(gate|up)\.weight=q8_0

# Shared Expert Layers [3-92]
blk\..*\.ffn_down_shexp\.weight=q8_0
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0

# Routed Experts Layers [3-92]
blk\..*\.ffn_down_exps\.weight=iq5_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_k

# NextN MTP Layer [92]
# Leave full q8_0 as supposedly better for MTP
# (doesn't use RAM or VRAM otherwise so its fine)
blk\..*\.nextn\.embed_tokens\.weight=q8_0
blk\..*\.nextn\.shared_head_head\.weight=q8_0
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 ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/GLM-4.7-GGUF/imatrix-GLM-4.7-BF16.dat \
    /mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-160x21B-4.7-BF16-00001-of-00015.gguf \
    /mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-4.7-IQ4_K.gguf \
    IQ4_K \
    128

</details>

big-IQ3_KS 171.890 GiB (4.120 BPW)

Final estimate: PPL over 565 chunks for n_ctx=512 = 4.0410 +/- 0.02501

<details> <summary>👈 Secret Recipe</summary>

#!/usr/bin/env bash

custom="
# 93 Repeating Layers [0-92]

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

# First 3 Dense Layers [0-2]
blk\..*\.ffn_down\.weight=q8_0
blk\..*\.ffn_(gate|up)\.weight=q8_0

# Shared Expert Layers [3-92]
blk\..*\.ffn_down_shexp\.weight=q8_0
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0

# Routed Experts Layers [3-92]
blk\..*\.ffn_down_exps\.weight=iq5_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq3_ks

# NextN MTP Layer [92]
blk\..*\.nextn\.embed_tokens\.weight=q8_0
blk\..*\.nextn\.shared_head_head\.weight=q8_0
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 ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/GLM-4.7-GGUF/imatrix-GLM-4.7-BF16.dat \
    /mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-160x21B-4.7-BF16-00001-of-00015.gguf \
    /mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-4.7-big-IQ3_KS.gguf \
    IQ3_KS \
    128

</details>

IQ3_KS 155.219 GiB (3.721 BPW)

Final estimate: PPL over 565 chunks for n_ctx=512 = 4.1330 +/- 0.02573

<details> <summary>👈 Secret Recipe</summary>

#!/usr/bin/env bash

custom="
# 93 Repeating Layers [0-92]

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

# First 3 Dense Layers [0-2]
blk\..*\.ffn_down\.weight=q8_0
blk\..*\.ffn_(gate|up)\.weight=q8_0

# Shared Expert Layers [3-92]
blk\..*\.ffn_down_shexp\.weight=q8_0
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0

# Routed Experts Layers [3-92]
blk\..*\.ffn_down_exps\.weight=iq4_kss
blk\..*\.ffn_(gate|up)_exps\.weight=iq3_ks

# NextN MTP Layer [92]
blk\..*\.nextn\.embed_tokens\.weight=q8_0
blk\..*\.nextn\.shared_head_head\.weight=q8_0
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 ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/GLM-4.7-GGUF/imatrix-GLM-4.7-BF16.dat \
    /mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-160x21B-4.7-BF16-00001-of-00015.gguf \
    /mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-4.7-IQ3_KS.gguf \
    IQ3_KS \
    128

</details>

IQ2_KL 129.279 GiB (3.099 BPW)

Final estimate: PPL over 565 chunks for n_ctx=512 = 4.5644 +/- 0.02929

<details> <summary>👈 Secret Recipe</summary>

#!/usr/bin/env bash

custom="
# 93 Repeating Layers [0-92]

# Attention
blk\.(0|1|2)\.attn_q.*=iq6_k
blk\.(0|1|2)\.attn_k.*=q8_0
blk\.(0|1|2)\.attn_v.*=q8_0
blk\.(0|1|2)\.attn_output.*=iq6_k

blk\..*\.attn_q.*=iq5_k
blk\..*\.attn_k.*=iq6_k
blk\..*\.attn_v.*=iq6_k
blk\..*\.attn_output.*=iq5_k

# First 3 Dense Layers [0-2]
blk\..*\.ffn_down\.weight=iq6_k
blk\..*\.ffn_(gate|up)\.weight=iq5_k

# Shared Expert Layers [3-92]
blk\..*\.ffn_down_shexp\.weight=iq6_k
blk\..*\.ffn_(gate|up)_shexp\.weight=iq5_k

# Routed Experts Layers [3-92]
blk\..*\.ffn_down_exps\.weight=iq3_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_kl

# NextN MTP Layer [92]
blk\..*\.nextn\.embed_tokens\.weight=q8_0
blk\..*\.nextn\.shared_head_head\.weight=q8_0
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 ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/GLM-4.7-GGUF/imatrix-GLM-4.7-BF16.dat \
    /mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-160x21B-4.7-BF16-00001-of-00015.gguf \
    /mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-4.7-v14-IQ2_KL.gguf \
    IQ2_KL \
    128

</details>

smol-IQ2_KS 99.237 GiB (2.379 BPW)

Final estimate: PPL over 565 chunks for n_ctx=512 = 5.9716 +/- 0.04130

<details> <summary>👈 Secret Recipe</summary>

#!/usr/bin/env bash

custom="
# 93 Repeating Layers [0-92]

# Attention
blk\.(0|1|2)\.attn_q.*=iq6_k
blk\.(0|1|2)\.attn_k.*=q8_0
blk\.(0|1|2)\.attn_v.*=q8_0
blk\.(0|1|2)\.attn_output.*=iq6_k

blk\..*\.attn_q.*=iq5_ks
blk\..*\.attn_k.*=iq6_k
blk\..*\.attn_v.*=iq6_k
blk\..*\.attn_output.*=iq5_ks

# First 3 Dense Layers [0-2]
blk\..*\.ffn_down\.weight=iq5_ks
blk\..*\.ffn_(gate|up)\.weight=iq5_ks

# Shared Expert Layers [3-92]
blk\..*\.ffn_down_shexp\.weight=iq5_ks
blk\..*\.ffn_(gate|up)_shexp\.weight=iq5_ks

# Routed Experts Layers [3-92]
blk\..*\.ffn_down_exps\.weight=iq2_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_ks

# NextN MTP Layer [92]
blk\..*\.nextn\.embed_tokens\.weight=q8_0
blk\..*\.nextn\.shared_head_head\.weight=q8_0
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 ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/GLM-4.7-GGUF/imatrix-GLM-4.7-BF16.dat \
    /mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-160x21B-4.7-BF16-00001-of-00015.gguf \
    /mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-4.7-v12-smol-IQ2_KS.gguf \
    IQ2_KS \
    128

</details>

smol-IQ1_KT 82.442 GiB (1.976 BPW)

Final estimate: PPL over 565 chunks for n_ctx=512 = 6.7720 +/- 0.04745

only for the desperate!

<details> <summary>👈 Secret Recipe</summary>

#!/usr/bin/env bash

custom="
# 93 Repeating Layers [0-92]

# Attention
blk\.(0|1|2)\.attn_q.*=q8_0
blk\.(0|1|2)\.attn_k.*=q8_0
blk\.(0|1|2)\.attn_v.*=q8_0
blk\.(0|1|2)\.attn_output.*=q8_0

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

# First 3 Dense Layers [0-2]
blk\..*\.ffn_down\.weight=iq5_ks
blk\..*\.ffn_(gate|up)\.weight=iq5_ks

# Shared Expert Layers [3-92]
blk\..*\.ffn_down_shexp\.weight=iq5_ks
blk\..*\.ffn_(gate|up)_shexp\.weight=iq5_ks

# Routed Experts Layers [3-92]
blk\..*\.ffn_down_exps\.weight=iq1_kt
blk\..*\.ffn_(gate|up)_exps\.weight=iq1_kt

# NextN MTP Layer [92]
blk\..*\.nextn\.embed_tokens\.weight=q8_0
blk\..*\.nextn\.shared_head_head\.weight=q8_0
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 ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/GLM-4.7-GGUF/imatrix-GLM-4.7-BF16.dat \
    /mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-160x21B-4.7-BF16-00001-of-00015.gguf \
    /mnt/data/models/ubergarm/GLM-4.7-GGUF/GLM-4.7-smol-IQ1_KT.gguf \
    IQ1_KT \
    128

</details>

Quick Start

# 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
$ cmake --build build --config Release -j $(nproc)

# Hybrid CPU + 1 GPU
./build/bin/llama-sweep-bench \
    --model "$model" \
    --alias ubergarm/GLM-4.7 \
    --ctx-size 65536 \
    -ger \
    --merge-qkv \
    -ngl 99 \
    --n-cpu-moe 72 \
    -ub 4096 -b 4096 \
    --threads 24 \
    --parallel 1 \
    --host 127.0.0.1 \
    --port 8080 \
    --no-mmap \
    --jinja

# Hybrid CPU + 2 or more GPUs
# using new "-sm graph" 'tensor parallel' feature!
# https://github.com/ikawrakow/ik_llama.cpp/pull/1080
./build/bin/llama-sweep-bench \
    --model "$model" \
    --alias ubergarm/GLM-4.7 \
    --ctx-size 65536 \
    -ger \
    -sm graph \
    -smgs \
    -mea 256 \
    -ngl 99 \
    --n-cpu-moe 72 \
    -ts 41,48 \
    -ub 4096 -b 4096 \
    --threads 24 \
    --parallel 1 \
    --host 127.0.0.1 \
    --port 8080 \
    --no-mmap \
    --jinja
# --max-gpu=3 # 3 or 4 usually if >2 GPUs available

# CPU Only
SOCKET=0 numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-server \
    --model "$model"\
    --alias ubergarm/GLM-4.7 \
    --ctx-size 65536 \
    -ger \
    --merge-qkv \
    -ctk q8_0 -ctv q8_0 \
    -ub 4096 -b 4096 \
    --parallel 1 \
    --threads 96 \
    --threads-batch 128 \
    --numa numactl \
    --host 127.0.0.1 \
    --port 8080 \
    --no-mmap \
    --jinja

NOTE: For tool/agentic use you can bring your own template with --chat-template-file myTemplate.jinja and might need --special etc.

References

• ik_llama.cpp
• Getting Started Guide (already out of date lol)
• ubergarm-imatrix-calibration-corpus-v02.txt
• Solid mainline quants by AesSedai/GLM-4.7-GGUF