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 +1,153 @@
+---
+quantized_by: ubergarm
+pipeline_tag: text-generation
+base_model: Qwen/Qwen3.6-27B
+base_model_relation: quantized
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen3.6-27B/blob/main/LICENSE
+tags:
+- imatrix
+- conversational
+- qwen3_5
+- ik_llama.cpp
+---
+
+## `ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3.6-27B
+*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. Only a couple quants in this collection are compatible with mainline llamma.cpp/LMStudio/KoboldCPP/etc as mentioned in the specific description, all others require ik_llama.cpp.
+
+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 quantizing 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")
+
+These two are just test quants for baseline perplexity comparison and not available for download here:
+* `BF16` 50.103 GiB (16.002 BPW)
+  - TODO
+* `Q8_0` 26.622 GiB (8.502 BPW)
+  - TODO
+
+*NOTE*: If the models are split, the first file is much smaller and only contains metadata, that is on purpose, its fine!
+
+## IQ5_KS 18.532 GiB (5.919 BPW)
+TODO
+
+This ik_llama.cpp exclusive quant is likely among the best quality available for 24GB full offload.
+
+<details>
+
+<summary>👈 Secret Recipe</summary>
+
+```bash
+#!/usr/bin/env bash
+
+custom="
+# 64 Repeating Layers [0-63]
+
+## Gated Attention/Delta Net [Blended 0-63]
+blk\..*\.attn_gate\.weight=q6_0
+blk\..*\.attn_qkv\.weight=q6_0
+blk\..*\.attn_output\.weight=q6_0
+blk\..*\.attn_q\.weight=q6_0
+blk\..*\.attn_k\.weight=q6_0
+blk\..*\.attn_v\.weight=q6_0
+blk\..*\.ssm_alpha\.weight=q8_0
+blk\..*\.ssm_beta\.weight=q8_0
+blk\..*\.ssm_out\.weight=q8_0
+
+# Dense Layers [0-63]
+blk\.[0-4]\.ffn_down_exps\.weight=q6_0
+blk\..*\.ffn_down\.weight=iq5_ks
+blk\..*\.ffn_(gate|up)\.weight=iq5_ks
+
+# Non-Repeating Layers
+token_embd\.weight=q6_0
+output\.weight=q8_0
+"
+
+custom=$(
+  echo "$custom" | grep -v '^#' | \
+  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
+)
+
+    #--dry-run \
+numactl -N ${SOCKET} -m ${SOCKET} \
+./build/bin/llama-quantize \
+    --custom-q "$custom" \
+    --imatrix /mnt/data/models/ubergarm/Qwen3.6-27B-GGUF/imatrix-Qwen3.6-27B-BF16.dat \
+    /mnt/data/models/ubergarm/Qwen3.6-27B-GGUF/Qwen3.6-27B-BF16-00001-of-00002.gguf \
+    /mnt/data/models/ubergarm/Qwen3.6-27B-GGUF/Qwen3.6-27B-IQ5_KS.gguf \
+    IQ5_KS \
+    128
+```
+
+</details>
+
+## smol-IQ4_NL 15.405 GiB (4.920 BPW)
+TODO
+
+This mainline compatible custom mix using quantization types hopefully optimized for Vulkan/ROCm (and possibly Mac)?
+
+<details>
+
+<summary>👈 Secret Recipe</summary>
+
+```bash
+#!/usr/bin/env bash
+
+custom="
+# 64 Repeating Layers [0-63]
+
+## Gated Attention/Delta Net [Blended 0-63]
+blk\..*\.attn_gate\.weight=iq4_nl
+blk\..*\.attn_qkv\.weight=iq4_nl
+blk\..*\.attn_output\.weight=iq4_nl
+blk\..*\.attn_q\.weight=iq4_nl
+blk\..*\.attn_k\.weight=iq4_nl
+blk\..*\.attn_v\.weight=iq4_nl
+blk\..*\.ssm_alpha\.weight=q8_0
+blk\..*\.ssm_beta\.weight=q8_0
+blk\..*\.ssm_out\.weight=q8_0
+
+# Dense Layers [0-63]
+blk\..*\.ffn_down\.weight=iq4_nl
+blk\..*\.ffn_(gate|up)\.weight=iq4_nl
+
+# Non-Repeating Layers
+token_embd\.weight=iq4_nl
+output\.weight=q8_0
+"
+
+custom=$(
+  echo "$custom" | grep -v '^#' | \
+  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
+)
+
+    #--dry-run \
+numactl -N ${SOCKET} -m ${SOCKET} \
+./build/bin/llama-quantize \
+    --custom-q "$custom" \
+    --imatrix /mnt/data/models/ubergarm/Qwen3.6-27B-GGUF/imatrix-Qwen3.6-27B-BF16.dat \
+    /mnt/data/models/ubergarm/Qwen3.6-27B-GGUF/Qwen3.6-27B-BF16-00001-of-00002.gguf \
+    /mnt/data/models/ubergarm/Qwen3.6-27B-GGUF/Qwen3.6-27B-smol-IQ4_NL.gguf \
+    IQ4_NL \
+    128
+```
+
+</details>
+
+## References
+* [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp)
+* [ubergarm on quantizing LLMs and tuning GPUs with aifoundry.org](https://blog.aifoundry.org/p/adventures-in-model-quantization)
+* [ubergarm-imatrix-calibration-corpus-v02.txt](https://gist.github.com/ubergarm/edfeb3ff9c6ec8b49e88cdf627b0711a?permalink_comment_id=5682584#gistcomment-5682584)
+* [Getting Started Guide (out of date)](https://github.com/ikawrakow/ik_llama.cpp/discussions/258)
+* [Quant Cookers Guide (out of date)](https://github.com/ikawrakow/ik_llama.cpp/discussions/434)
+* [high quality imatrix MoE optimized mainline llama.cpp quants AesSedai](https://huggingface.co/AesSedai/)