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GLM-4.7-GGUF

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ik_llama.cpp
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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 (more coming Dec 24th...)

NOTE About 2.466 GiB of each quant is MTP/nextn tensors (so doesn't take RAM/VRAM if not using MTP)

Perplexity computed against wiki.test.raw.

Perplexity Chart

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

IQ5_K 250.635 GiB (6.008 BPW)

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

👈 Secret Recipe 
#!/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

IQ3_KS 155.219 GiB (3.721 BPW)

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

👈 Secret Recipe 
#!/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

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!

👈 Secret Recipe 
#!/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

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

References

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