metadata
quantized_by: AesSedai
pipeline_tag: text-generation
base_model: zai-org/GLM-4.5
license: mit
base_model_relation: quantized
ik_llama.cpp imatrix Quantizations of zai-org/GLM-4.5
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.
See Ubergarm's GLM-4.5 quants for info on how to use the recipe or make your own quant.
IQ2_KT: 109.269 GiB (2.619 BPW), Final estimate: PPL = 4.1170 +/- 0.02457
👈 Recipe
# 93 Repeating Layers [0-92]
# Attention
blk\..*\.attn_q.*=iq4_k
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=iq4_ks
blk\..*\.ffn_(gate|up)\.weight=iq3_ks
# Shared Expert Layers [3-92]
blk\..*\.ffn_down_shexp\.weight=iq6_k
blk\..*\.ffn_(gate|up)_shexp\.weight=iq6_k
# Routed Experts Layers [3-92]
blk\..*\.ffn_down_exps\.weight=iq3_kt
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_kt
# NextN MTP Layer [92]
blk\..*\.nextn\.embed_tokens\.weight=iq4_k
blk\..*\.nextn\.shared_head_head\.weight=iq6_k
blk\..*\.nextn\.eh_proj\.weight=iq6_k
# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
IQ4_KSS: 176.499 GiB (4.231 BPW), Final estimate: PPL = 3.3031 +/- 0.01871
👈 Recipe
# 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.*=iq6_k
blk\..*\.attn_k.*=iq6_k
blk\..*\.attn_v.*=iq6_k
blk\..*\.attn_output.*=iq6_k
# First 3 Dense Layers [0-2]
blk\..*\.ffn_down\.weight=iq5_ks
blk\..*\.ffn_(gate|up)\.weight=iq4_ks
# 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_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_kss
# NextN MTP Layer [92]
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
IQ4_KS-IQ4_KS-IQ5_KS: 200.326 GiB (4.802 BPW), Final estimate: PPL = TBD (but better than IQ5_K)
👈 Recipe
Default quant level @ 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_up_exps\.weight=iq4_ks
blk\..*\.ffn_gate_exps\.weight=iq4_ks
blk\..*\.ffn_down_exps\.weight=iq5_ks
IQ5_K: 204.948 GiB (4.913 BPW), Final estimate: PPL = 3.1992 +/- 0.01801
👈 Recipe
# 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_k
blk\..*\.attn_k.*=iq5_k
blk\..*\.attn_v.*=iq5_k
blk\..*\.attn_output.*=iq5_k
# 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]
blk\..*\.nextn\.embed_tokens\.weight=iq5_k
blk\..*\.nextn\.shared_head_head\.weight=iq5_k
blk\..*\.nextn\.eh_proj\.weight=q8_0
# Non-Repeating Layers
token_embd\.weight=q8_0
output\.weight=q8_0