--- quantized_by: ubergarm pipeline_tag: text-generation base_model: zai-org/GLM-4.7 license: mit base_model_relation: quantized tags: - imatrix - conversational - ik_llama.cpp - glm4_moe language: - en - zh --- ## WIP Currently cooking this now! - [x] download bf16 safetensors https://huggingface.co/zai-org/GLM-4.7 - [x] use llama.cpp/convert_hf_to_gguf.py to create bf16 GGUF - [x] calculate imatrix and upload to HF first so others can use as desired - [x] cook Q8_0 and test perplexity of BF16 and Q8_0 for baseline data - [x] adjust MTP nextn tensors to full q8_0 (won't effect RAM+VRAM usage otherwise) - [x] cook IQ5_K with full q8_0 attn/shexp/first 3 dense layers and test - [x] upload IQ5_K if all looking good - [ ] upload smol-IQ4_KSS if all looking good - [ ] continue with smaller quants - [ ] check if any folks open discussions with desired RAM/VRAM breakpoints ## `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](https://github.com/Nexesenex/croco.cpp) fork of KoboldCPP with Windows builds for CUDA 12.9. Also check for [Windows builds by Thireus here.](https://github.com/Thireus/ik_llama.cpp/releases) 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](https://forum.level1techs.com/t/deepseek-deep-dive-r1-at-home/225826), [YouTube Channel](https://www.youtube.com/@Level1Techs)! **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](https://huggingface.co/BeaverAI) and on [r/LocalLLaMA](https://www.reddit.com/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](https://aifoundry.org) so check out their open source RISC-V based solutions! ## Quant Collection Perplexity computed against *wiki.test.raw*. ![Perplexity Chart](images/perplexity.png "Chart showing Perplexity improving as BPW increases.") 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 ```bash #!/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 ```
## smol-IQ4_KSS TODO Final estimate: PPL over 565 chunks for n_ctx=512 = TODO
👈 Secret Recipe ```bash #!/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=iq4_kss # 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-IQ4_KSS.gguf \ IQ4_KSS \ 128 ```
## Quick Start ```bash # 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) # I'll follow up with updated commands especially for 2 to 4 CUDA GPUs for "graph parallel" # https://github.com/ikawrakow/ik_llama.cpp/pull/1080 echo TODO ``` ## References * [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp) * [Getting Started Guide (already out of date lol)](https://github.com/ikawrakow/ik_llama.cpp/discussions/258) * [ubergarm-imatrix-calibration-corpus-v02.txt](https://gist.github.com/ubergarm/edfeb3ff9c6ec8b49e88cdf627b0711a?permalink_comment_id=5682584#gistcomment-5682584) * [Solid mainline quants by AesSedai/GLM-4.7-GGUF](https://huggingface.co/AesSedai/GLM-4.7-GGUF)