upload smol-IQ4_K very nice quant

43637ae about 4 hours ago

15 kB

quantized_by: ubergarm
pipeline_tag: text-generation
base_model: zai-org/GLM-5.1
base_model_relation: quantized
license: mit
tags:
  - imatrix
  - conversational
  - glm_moe_dsa
  - ik_llama.cpp
language:
  - en
  - zh

`ik_llama.cpp` imatrix Quantizations of zai-org/GLM-5.1

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. Also check for ik_llama.cpp windows builds by Thireus here..

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. (lower is "better")

These two are just test quants for baseline perplexity comparison and not available for download here:

BF16 1404.406 GiB (16.003 BPW)
- PPL over 565 chunks for n_ctx=512 = 2.7257 +/- 0.01497
Q8_0 746.302 GiB (8.504 BPW)
- PPL over 565 chunks for n_ctx=512 = 2.7257 +/- 0.01498

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

smol-IQ4_K 405.502 GiB (4.621 BPW)

PPL over 565 chunks for n_ctx=512 = 2.7547 +/- 0.01516

NOTE: Actual used RAM/VRAM will be about ~397.8 GiB despite larger model size reported due to unused blk.78/indexer/nextn tensors.

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 79 Repeating Layers [0-78]

## Attention [0-78]
blk\..*\.attn_k_b\.weight=q8_0
blk\..*\.attn_v_b\.weight=q8_0
blk\..*\.attn_kv_a_mqa\.weight=q8_0
blk\..*\.attn_q_a\.weight=q8_0
blk\..*\.attn_q_b\.weight=q8_0
blk\..*\.attn_output\.weight=q8_0

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

# Shared Expert Layers [3-78]
blk\..*\.ffn_down_shexp\.weight=iq6_k
blk\..*\.ffn_(gate|up)_shexp\.weight=iq6_k

# Routed Experts Layers [3-78]
# NOTE: blk.78.* NOT implemented at time of quantizing so no imatrix data available
blk\.(78)\.ffn_down_exps\.weight=iq6_k
blk\.(78)\.ffn_(gate|up)_exps\.weight=iq6_k
blk\..*\.ffn_down_exps\.weight=iq4_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_k

# Lightning indexer tensors [0-78]
# NOTE: indexer.* NOT implemented at time of quantizing so no imatrix data available
blk\..*\.indexer\.proj\.weight=q8_0
blk\..*\.indexer\.attn_k\.weight=q8_0
blk\..*\.indexer\.attn_q_b\.weight=q8_0

# NextN MTP Layer [78]
# NOTE: nextn.* NOT implemented at time of quantizing so no imatrix data available
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-5.1-GGUF/imatrix-GLM-5.1-BF16.dat \
    /mnt/data/models/ubergarm/GLM-5.1-GGUF/GLM-256x22B-5.1-BF16-00001-of-00033.gguf \
    /mnt/data/models/ubergarm/GLM-5.1-GGUF/GLM-5.1-smol-IQ4_K.gguf \
    IQ4_K \
    128

IQ3_KS 320.216 GiB (3.649 BPW)

PPL over 565 chunks for n_ctx=512 = 2.8780 +/- 0.01609

NOTE: Actual used RAM/VRAM will be about 314.07 GiB despite larger model size reported due to unused blk.78/indexer/nextn tensors.

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 79 Repeating Layers [0-78]

## Attention [0-78]
blk\..*\.attn_k_b\.weight=q8_0
blk\..*\.attn_v_b\.weight=q8_0
blk\..*\.attn_kv_a_mqa\.weight=q8_0
blk\..*\.attn_q_a\.weight=iq6_k
blk\..*\.attn_q_b\.weight=iq6_k
blk\..*\.attn_output\.weight=iq6_k

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

# Shared Expert Layers [3-78]
blk\..*\.ffn_down_shexp\.weight=iq5_ks
blk\..*\.ffn_(gate|up)_shexp\.weight=iq5_ks

# Routed Experts Layers [3-78]
# NOTE: blk.78.* NOT implemented at time of quantizing so no imatrix data available
blk\.(78)\.ffn_down_exps\.weight=iq5_ks
blk\.(78)\.ffn_(gate|up)_exps\.weight=iq5_ks
blk\..*\.ffn_down_exps\.weight=iq4_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq3_ks

# Lightning indexer tensors [0-78]
# NOTE: indexer.* NOT implemented at time of quantizing so no imatrix data available
blk\..*\.indexer\.proj\.weight=q8_0
blk\..*\.indexer\.attn_k\.weight=q8_0
blk\..*\.indexer\.attn_q_b\.weight=iq6_k

# NextN MTP Layer [78]
# NOTE: nextn.* NOT implemented at time of quantizing so no imatrix data available
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-5.1-GGUF/imatrix-GLM-5.1-BF16.dat \
    /mnt/data/models/ubergarm/GLM-5.1-GGUF/GLM-256x22B-5.1-BF16-00001-of-00033.gguf \
    /mnt/data/models/ubergarm/GLM-5.1-GGUF/GLM-5.1-IQ3_KS.gguf \
    IQ3_KS \
    128

IQ2_KL 261.988 GiB (2.985 BPW)

PPL over 565 chunks for n_ctx=512 = 3.1275 +/- 0.01759

NOTE: Actual used RAM/VRAM will be about 255.84 GiB despite larger model size reported due to unused blk.78/indexer/nextn tensors.

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 79 Repeating Layers [0-78]

## Attention [0-78]
blk\..*\.attn_k_b\.weight=q8_0
blk\..*\.attn_v_b\.weight=q8_0
blk\..*\.attn_kv_a_mqa\.weight=q8_0
blk\..*\.attn_q_a\.weight=iq6_k
blk\..*\.attn_q_b\.weight=iq6_k
blk\..*\.attn_output\.weight=iq6_k

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

# Shared Expert Layers [3-78]
blk\..*\.ffn_down_shexp\.weight=iq5_ks
blk\..*\.ffn_(gate|up)_shexp\.weight=iq5_ks

# Routed Experts Layers [3-78]
# NOTE: blk.78.* NOT implemented at time of quantizing so no imatrix data available
blk\.(78)\.ffn_down_exps\.weight=iq5_ks
blk\.(78)\.ffn_(gate|up)_exps\.weight=iq5_ks
blk\..*\.ffn_down_exps\.weight=iq3_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_kl

# Lightning indexer tensors [0-78]
# NOTE: indexer.* NOT implemented at time of quantizing so no imatrix data available
blk\..*\.indexer\.proj\.weight=q8_0
blk\..*\.indexer\.attn_k\.weight=q8_0
blk\..*\.indexer\.attn_q_b\.weight=iq6_k

# NextN MTP Layer [78]
# NOTE: nextn.* NOT implemented at time of quantizing so no imatrix data available
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-5.1-GGUF/imatrix-GLM-5.1-BF16.dat \
    /mnt/data/models/ubergarm/GLM-5.1-GGUF/GLM-256x22B-5.1-BF16-00001-of-00033.gguf \
    /mnt/data/models/ubergarm/GLM-5.1-GGUF/GLM-5.1-IQ2_KL.gguf \
    IQ2_KL \
    128

smol-IQ2_KS 205.738 GiB (2.344 BPW)

PPL over 565 chunks for n_ctx=512 = 3.8818 +/- 0.02300

NOTE: Actual used RAM/VRAM will be about 200 GiB despite larger model size reported due to unused blk.78/indexer/nextn tensors.

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 79 Repeating Layers [0-78]

## Attention [0-78]
blk\..*\.attn_k_b\.weight=q8_0
blk\..*\.attn_v_b\.weight=q8_0
blk\..*\.attn_kv_a_mqa\.weight=q8_0
blk\..*\.attn_q_a\.weight=iq6_k
blk\..*\.attn_q_b\.weight=iq6_k
blk\..*\.attn_output\.weight=iq6_k

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

# Shared Expert Layers [3-78]
blk\..*\.ffn_down_shexp\.weight=iq5_ks
blk\..*\.ffn_(gate|up)_shexp\.weight=iq5_ks

# Routed Experts Layers [3-78]
# NOTE: blk.78.* NOT implemented at time of quantizing so no imatrix data available
blk\.(78)\.ffn_down_exps\.weight=iq5_ks
blk\.(78)\.ffn_(gate|up)_exps\.weight=iq5_ks
blk\..*\.ffn_down_exps\.weight=iq2_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_ks

# Lightning indexer tensors [0-78]
# NOTE: indexer.* NOT implemented at time of quantizing so no imatrix data available
blk\..*\.indexer\.proj\.weight=q8_0
blk\..*\.indexer\.attn_k\.weight=q8_0
blk\..*\.indexer\.attn_q_b\.weight=iq6_k

# NextN MTP Layer [78]
# NOTE: nextn.* NOT implemented at time of quantizing so no imatrix data available
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-5.1-GGUF/imatrix-GLM-5.1-BF16.dat \
    /mnt/data/models/ubergarm/GLM-5.1-GGUF/GLM-256x22B-5.1-BF16-00001-of-00033.gguf \
    /mnt/data/models/ubergarm/GLM-5.1-GGUF/GLM-5.1-smol-IQ2_KS.gguf \
    IQ2_KS \
    128

smol-IQ1_KT 169.190 GiB (1.928 BPW)

PPL over 565 chunks for n_ctx=512 = 4.6654 +/- 0.02830

NOTE: Actual used RAM/VRAM will be about 163.046 GiB despite larger model size reported due to unused blk.78/indexer/nextn tensors.

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 79 Repeating Layers [0-78]

## Attention [0-78]
blk\..*\.attn_k_b\.weight=q8_0
blk\..*\.attn_v_b\.weight=q8_0
blk\..*\.attn_kv_a_mqa\.weight=q8_0
blk\..*\.attn_q_a\.weight=iq6_k
blk\..*\.attn_q_b\.weight=iq6_k
blk\..*\.attn_output\.weight=iq6_k

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

# Shared Expert Layers [3-78]
blk\..*\.ffn_down_shexp\.weight=iq5_ks
blk\..*\.ffn_(gate|up)_shexp\.weight=iq5_ks

# Routed Experts Layers [3-78]
# NOTE: blk.78.* NOT implemented at time of quantizing so no imatrix data available
blk\.(78)\.ffn_down_exps\.weight=iq5_ks
blk\.(78)\.ffn_(gate|up)_exps\.weight=iq5_ks
blk\..*\.ffn_down_exps\.weight=iq1_kt
blk\..*\.ffn_(gate|up)_exps\.weight=iq1_kt

# Lightning indexer tensors [0-78]
# NOTE: indexer.* NOT implemented at time of quantizing so no imatrix data available
blk\..*\.indexer\.proj\.weight=q8_0
blk\..*\.indexer\.attn_k\.weight=q8_0
blk\..*\.indexer\.attn_q_b\.weight=iq6_k

# NextN MTP Layer [78]
# NOTE: nextn.* NOT implemented at time of quantizing so no imatrix data available
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-5.1-GGUF/imatrix-GLM-5.1-BF16.dat \
    /mnt/data/models/ubergarm/GLM-5.1-GGUF/GLM-256x22B-5.1-BF16-00001-of-00033.gguf
    /mnt/data/models/ubergarm/GLM-5.1-GGUF/GLM-5.1-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)

# Download Quants
$ pip install huggingface_hub
$ hf download --local-dir ./GLM-5.1-GGUF/ --include=smol-IQ2_KS/*.gguf ubergarm/GLM-5.1-GGUF

# Hybrid CPU and Single GPU
# *NOTE* -fit might work on ik_llama.cpp now so give it a try
./build/bin/llama-server \
    --model "$model"\
    --alias ubergarm/GLM-5.1 \
    -muge \
    --merge-qkv \
    --ctx-size 131072 \
    -ctk f16 \
    -mla 3 \
    -amb 512 \
    -ngl 999 \
    --n-cpu-moe 50 \
    --parallel 1 \
    --threads 96 \
    --threads-batch 128 \
    --host 127.0.0.1 \
    --port 8080 \
    --no-mmap \
    -cram 8192 \
    --jinja

# CPU-Only
numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-server \
    --model "$model"\
    --alias ubergarm/GLM-5.1 \
    -muge \
    --merge-qkv \
    --ctx-size 131072 \
    -ctk q8_0 \
    -mla 3 \
    --parallel 1 \
    --threads 96 \
    --threads-batch 128 \
    --numa numactl \
    --host 127.0.0.1 \
    --port 8080 \
    --no-mmap \
    -cram 8192 \
    --jinja

You can also bring your own template with --chat-template-file myTemplate.jinja. Adjust caching with -cram <RAM Cache Size MiB>

No -sm graph support as of today for MLA models like DeepSeek, Kimi, and both GLM-5s.

QAT Speculation

Assuming GLM-5.1 uses similar training as GLM-5 including INT4 QAT, there may be some tweaks to the quantization algorithm to match that target better.

2.4.3 INT4 Quantization-aware training

To provide better accuracy at low-precision, we apply INT4 QAT in the SFT stage. Moreover, to further mitigate the training time overhead, we have developed a quantization kernel applicable to both training and offline weight quantization, which ensures bitwise-identical behavior between training and inference. https://arxiv.org/html/2602.15763v2

jukofyork mentioned useful links for details and experimental modified q4_K quantization implementation patch:

I may try that patch to quantize_row_q4_0_ref() to change const float d = max / -8; to -7 similar to how we did Kimi-K2's Q4_X quantization type without imatrix on routed experts? Or try jukofyork's modified q4_K? I'll play around with some low 4ish BPW quants.

ubergarm
/

GLM-5.1-GGUF

`ik_llama.cpp` imatrix Quantizations of zai-org/GLM-5.1

Big Thanks

Quant Collection

smol-IQ4_K 405.502 GiB (4.621 BPW)

IQ3_KS 320.216 GiB (3.649 BPW)

IQ2_KL 261.988 GiB (2.985 BPW)

smol-IQ2_KS 205.738 GiB (2.344 BPW)

smol-IQ1_KT 169.190 GiB (1.928 BPW)

Quick Start

QAT Speculation

2.4.3 INT4 Quantization-aware training

References

ik_llama.cpp imatrix Quantizations of zai-org/GLM-5.1

Big Thanks

Quant Collection

smol-IQ4_K 405.502 GiB (4.621 BPW)

IQ3_KS 320.216 GiB (3.649 BPW)

IQ2_KL 261.988 GiB (2.985 BPW)

smol-IQ2_KS 205.738 GiB (2.344 BPW)

smol-IQ1_KT 169.190 GiB (1.928 BPW)

Quick Start

QAT Speculation

2.4.3 INT4 Quantization-aware training

References

`ik_llama.cpp` imatrix Quantizations of zai-org/GLM-5.1