initial commit

.gitattributes

@@ -0,0 +1,38 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+imatrix-*.dat filter=lfs diff=lfs merge=lfs -text
+*.gguf filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,191 @@
+---
+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
+---
+
+## WIP
+- [x] download original bf16 safetensors
+- [x] `convert_hf_to_gguf.py` using mainline llama.cpp
+- [x] quantize `--pure` q8_0 and confirm it looks similar enough to existing GLM-5 model architechture
+- [ ] run ik_llama.cpp llama-imatrix against full bf16 to get high quality imatrix
+- [ ] upload imatrix so other people can begin quantizing with it as desired
+- [ ] quantize/test/release `smol-IQ1_KT` and `smol-IQ2_KS` re-using previous GLM-5 recipe
+- [ ] experiment some with jukofyorks patch to see if any low 4ish BPW quants seem to align with QAT and give better PPL/KLD
+- [ ] potentially release some larger quants this time
+
+## `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](https://github.com/Nexesenex/croco.cpp) fork of KoboldCPP with Windows builds. Also check for [ik_llama.cpp windows builds by Thireus here.](https://github.com/Thireus/ik_llama.cpp/releases).
+
+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*. (lower is "better")
+
+![Perplexity Chart](images/perplexity.png "Chart showing Perplexity vs Model Size.")
+
+These two are just test quants for baseline perplexity comparison and not available for download here:
+* `BF16` TODO
+  - PPL TODO
+* `Q8_0` TODO
+  - PPL TODO
+
+*NOTE*: The first split file is much smaller on purpose to only contain metadata, its fine!
+
+## IQ3_KS TODO
+TODO
+
+NOTE: Actual used RAM/VRAM will be about 314.07 GiB despite larger model size reported due to unused blk.78/indexer/nextn tensors.
+
+<details>
+
+<summary>👈 Secret Recipe</summary>
+
+```bash
+TODO
+```
+
+</details>
+
+## IQ2_KL TODO
+TODO
+
+NOTE: Actual used RAM/VRAM will be about 255.84 GiB despite larger model size reported due to unused blk.78/indexer/nextn tensors.
+
+<details>
+
+<summary>👈 Secret Recipe</summary>
+
+```bash
+TODO
+```
+
+</details>
+
+## smol-IQ2_KS TODO
+TODO
+
+NOTE: Actual used RAM/VRAM will be about 200 GiB despite larger model size reported due to unused blk.78/indexer/nextn tensors.
+
+<details>
+
+<summary>👈 Secret Recipe</summary>
+
+```bash
+TODO
+```
+
+</details>
+
+## smol-IQ1_KT TODO
+TODO
+
+NOTE: Actual used RAM/VRAM will be about 163.046 GiB despite larger model size reported due to unused blk.78/indexer/nextn tensors.
+
+<details>
+
+<summary>👈 Secret Recipe</summary>
+
+```bash
+TODO
+```
+
+</details>
+
+## 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 $(nproc)
+
+# Download Quants
+$ 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 \
+    --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 f16 \
+    -mla 3 \
+    --parallel 1 \
+    --threads 96 \
+    --threads-batch 128 \
+    --numa numactl \
+    --host 127.0.0.1 \
+    --port 8080 \
+    --no-mmap \
+    --jinja
+```
+
+You can also bring your own template with `--chat-template-file myTemplate.jinja`.
+
+## 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:
+
+* https://github.com/zai-org/GLM-5/issues/21
+* https://github.com/ywhhh/vllm-ascend-afd/blob/main/vllm_ascend/quantization/w4a8_dynamic.py
+* https://github.com/ggml-org/llama.cpp/pull/17064#issuecomment-3528891329
+* https://github.com/ggml-org/llama.cpp/pull/19460#issuecomment-4200617220
+
+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 maybe `iq4_kss` would work well here. I'll release some smaller quants first then play around with this a bit if I have time.
+
+## 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)