aessedai/glm-4.5-gguf overview

{
  "metadata": {},
  "card_data": {
    "quantized_by": "AesSedai",
    "pipeline_tag": "text-generation",
    "base_model": "zai-org/GLM-4.5",
    "license": "mit",
    "base_model_relation": "quantized",
    "frontmatter": {
      "quantized_by": "AesSedai",
      "pipeline_tag": "text-generation",
      "base_model": "zai-org/GLM-4.5",
      "license": "mit",
      "base_model_relation": "quantized"
    },
    "hero_image_url": "plots-glm-4.5-8192/01_kld_vs_filesize-pareto.png \"Chart showing KLD improving as BPW increases.\"",
    "summary": "# 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 ```",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nquantized_by: AesSedai\npipeline_tag: text-generation\nbase_model: zai-org/GLM-4.5\nlicense: mit\nbase_model_relation: quantized\n---\n\nThis repository contains some custom quants of GLM-4.5 that focus on a couple of different schemas compared to the usual quantization schemas.\n\nThe idea being that given the huge size of the FFN tensors compared to the rest of the tensors in the model, it should be possible to achieve a better quality while keeping the overall size of the entire model smaller compared to a similar naive quantization.\n\nThe following charts showcase a very wide variety of GLM-4.5 quants that were tested for KL Divergence using the reference logits and corpus included in this repo: `GLM-4.5-KLD-8192-ref-logits-ed-combined-all-micro-Q8_0.bin` and `combined_all_micro.txt`\n\nA full CSV with the data is included as well in `glm-4.5-quantization-output.csv`.\n\nThe naming convention is (inconsistently) as follows, generally: `[Default Type]-[FFN_UP]-[FFN_GATE]-[FFN_DOWN]`, eg: `Q6_K-IQ2_S-IQ2_S-IQ3_S`. This means:\n- Q6_K is the default type (attention, shared expert, etc.)\n- IQ2_S was used for the FFN_UP and FFN_GATE conditional expert tensors\n- IQ3_S was used for the FFN_DOWN conditional expert tensors\n\nGenerally speaking, quants following the above convention tend to have a better KLD and PPL compared to quants from other providers. Visualized here are the Mean KLD and Mean PPL of the quants on the Pareto frontier (so, best for a given size). Full graphs are available in the `plots-glm-4.5-8192` folder.\n\n![KLD vs File Size](plots-glm-4.5-8192/01_kld_vs_filesize-pareto.png \"Chart showing KLD improving as BPW increases.\")\n![PPL vs File Size](plots-glm-4.5-8192/02_ppl_vs_filesize-pareto.png \"Chart showing Perplexity improving as BPW increases.\")\n\nProvided here are a few quants, separated into `llama.cpp` and `ik_llama.cpp` folders for convenience (though, `ik_llama.cpp` is capable of running the quants in `llama.cpp`, but the opposite is not true).\n\n## `llama.cpp` imatrix Quantizations of zai-org/GLM-4.5\nThis quant collection can be run on `llama.cpp` or `kobold.cpp` like normal. \n\n## Q6_K-IQ2_S-IQ2_S-IQ3_S: 128.18 GiB (3.07 BPW), Final estimate: PPL = 4.786993 ± 0.031213, KLD = 0.145117 ± 0.002232\n\n## GLM-4.5-Q6_K-Q2_K-Q2_K-Q3_K: 129.57 GiB (3.11 BPW), Final estimate: PPL = 4.700384 ± 0.030202, KLD = 0.164863 ± 0.002339\n\n## GLM-4.5-Q8_0-IQ3_XXS-IQ3_XXS-IQ3_S: 144.68 GiB (3.47 BPW), Final estimate: PPL = 4.729934 ± 0.030769, KLD = 0.116520 ± 0.002072\n\n## `ik_llama.cpp` imatrix Quantizations of zai-org/GLM-4.5\nThis quant collection **REQUIRES** [ik_llama.cpp](https://github.com/ikawrakow/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!\n\n*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.\n\nSome 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.\n\nSee [Ubergarm's GLM-4.5 quants](https://huggingface.co/ubergarm/GLM-4.5-GGUF) for info on how to use the recipe or make your own quant.\n\n## IQ2_KT: 109.269 GiB (2.619 BPW): Lost the results somewhere, oops.\n\n<details>\n\n<summary>👈 Recipe</summary>\n\n```bash\n# 93 Repeating Layers [0-92]\n\n# Attention\nblk\\..*\\.attn_q.*=iq4_k\nblk\\..*\\.attn_k.*=iq6_k\nblk\\..*\\.attn_v.*=iq6_k\nblk\\..*\\.attn_output.*=iq5_ks\n\n# First 3 Dense Layers [0-2]\nblk\\..*\\.ffn_down\\.weight=iq4_ks\nblk\\..*\\.ffn_(gate|up)\\.weight=iq3_ks\n\n# Shared Expert Layers [3-92]\nblk\\..*\\.ffn_down_shexp\\.weight=iq6_k\nblk\\..*\\.ffn_(gate|up)_shexp\\.weight=iq6_k\n\n# Routed Experts Layers [3-92]\nblk\\..*\\.ffn_down_exps\\.weight=iq3_kt\nblk\\..*\\.ffn_(gate|up)_exps\\.weight=iq2_kt\n\n# NextN MTP Layer [92]\nblk\\..*\\.nextn\\.embed_tokens\\.weight=iq4_k\nblk\\..*\\.nextn\\.shared_head_head\\.weight=iq6_k\nblk\\..*\\.nextn\\.eh_proj\\.weight=iq6_k\n\n# Non-Repeating Layers\ntoken_embd\\.weight=iq4_k\noutput\\.weight=iq6_k\n```\n\n</details>\n\n## IQ4_KSS: 176.499 GiB (4.231 BPW): Lost the results somewhere, oops.\n\n<details>\n\n<summary>👈 Recipe</summary>\n\n```bash\n# 93 Repeating Layers [0-92]\n\n# Attention\nblk\\.(0|1|2)\\.attn_q.*=q8_0\nblk\\.(0|1|2)\\.attn_k.*=q8_0\nblk\\.(0|1|2)\\.attn_v.*=q8_0\nblk\\.(0|1|2)\\.attn_output.*=q8_0\n\nblk\\..*\\.attn_q.*=iq6_k\nblk\\..*\\.attn_k.*=iq6_k\nblk\\..*\\.attn_v.*=iq6_k\nblk\\..*\\.attn_output.*=iq6_k\n\n# First 3 Dense Layers [0-2]\nblk\\..*\\.ffn_down\\.weight=iq5_ks\nblk\\..*\\.ffn_(gate|up)\\.weight=iq4_ks\n\n# Shared Expert Layers [3-92]\nblk\\..*\\.ffn_down_shexp\\.weight=q8_0\nblk\\..*\\.ffn_(gate|up)_shexp\\.weight=q8_0\n\n# Routed Experts Layers [3-92]\nblk\\..*\\.ffn_down_exps\\.weight=iq4_ks\nblk\\..*\\.ffn_(gate|up)_exps\\.weight=iq4_kss\n\n# NextN MTP Layer [92]\nblk\\..*\\.nextn\\.embed_tokens\\.weight=iq5_ks\nblk\\..*\\.nextn\\.shared_head_head\\.weight=iq5_ks\nblk\\..*\\.nextn\\.eh_proj\\.weight=q8_0\n\n# Non-Repeating Layers\ntoken_embd\\.weight=iq4_k\noutput\\.weight=iq6_k\n```\n\n</details>\n\n## IQ4_KS-IQ4_KS-IQ5_KS: 200.326 GiB (4.802 BPW), Final estimate: PPL = 4.618597 ± 0.029981, KLD = 0.072590 ± 0.001816\n\n<details>\n\n<summary>👈 Recipe</summary>\n\n```bash\nDefault quant level @ Q8_0\n\n# Shared Expert Layers [3-92]\nblk\\..*\\.ffn_down_shexp\\.weight=q8_0\nblk\\..*\\.ffn_(gate|up)_shexp\\.weight=q8_0\n\n# Routed Experts Layers [3-92]\nblk\\..*\\.ffn_up_exps\\.weight=iq4_ks\nblk\\..*\\.ffn_gate_exps\\.weight=iq4_ks\nblk\\..*\\.ffn_down_exps\\.weight=iq5_ks\n```\n\n</details>\n\n## IQ5_K: 204.948 GiB (4.913 BPW), Final estimate: PPL = 4.665419 ± 0.030393, KLD = 0.078092 ± 0.001891\n\n<details>\n\n<summary>👈 Recipe</summary>\n\n```bash\n# 93 Repeating Layers [0-92]\n\n# Attention\nblk\\.(0|1|2)\\.attn_q.*=q8_0\nblk\\.(0|1|2)\\.attn_k.*=q8_0\nblk\\.(0|1|2)\\.attn_v.*=q8_0\nblk\\.(0|1|2)\\.attn_output.*=q8_0\n\nblk\\..*\\.attn_q.*=iq5_k\nblk\\..*\\.attn_k.*=iq5_k\nblk\\..*\\.attn_v.*=iq5_k\nblk\\..*\\.attn_output.*=iq5_k\n\n# First 3 Dense Layers [0-2]\nblk\\..*\\.ffn_down\\.weight=q8_0\nblk\\..*\\.ffn_(gate|up)\\.weight=q8_0\n\n# Shared Expert Layers [3-92]\nblk\\..*\\.ffn_down_shexp\\.weight=q8_0\nblk\\..*\\.ffn_(gate|up)_shexp\\.weight=q8_0\n\n# Routed Experts Layers [3-92]\nblk\\..*\\.ffn_down_exps\\.weight=iq5_k\nblk\\..*\\.ffn_(gate|up)_exps\\.weight=iq4_k\n\n# NextN MTP Layer [92]\nblk\\..*\\.nextn\\.embed_tokens\\.weight=iq5_k\nblk\\..*\\.nextn\\.shared_head_head\\.weight=iq5_k\nblk\\..*\\.nextn\\.eh_proj\\.weight=q8_0\n\n# Non-Repeating Layers\ntoken_embd\\.weight=q8_0\noutput\\.weight=q8_0\n```\n\n</details>",
    "related_quantizations": []
  },
  "tags": [
    "gguf",
    "text-generation",
    "base_model:zai-org/GLM-4.5",
    "base_model:quantized:zai-org/GLM-4.5",
    "license:mit",
    "endpoints_compatible",
    "region:us",
    "imatrix",
    "conversational"
  ],
  "likes": 6,
  "downloads": 3325,
  "gated": false,
  "private": false,
  "last_modified": "2025-10-11T19:07:51.000Z",
  "created_at": "2025-08-09T05:44:48.000Z",
  "pipeline_tag": "text-generation",
  "library_name": ""
}

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  "id": "AesSedai/GLM-4.5-GGUF",
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  "sha": "27f7e713e1b55d22705ea6f762e2ba94a59d5c6b",
  "createdAt": "2025-08-09T05:44:48.000Z",
  "lastModified": "2025-10-11T19:07:51.000Z",
  "author": "AesSedai",
  "downloads": 3325,
  "likes": 6,
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  "pipeline_tag": "text-generation",
  "library_name": "",
  "siblings_count": 45
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