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---
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quantized_by: ubergarm
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pipeline_tag: text-generation
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base_model: moonshotai/Kimi-K2-Instruct-0905
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license: other
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license_name: modified-mit
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license_link: https://huggingface.co/moonshotai/Kimi-K2-Instruct-0905/blob/main/LICENSE
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base_model_relation: quantized
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tags:
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- mla
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- imatrix
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- conversational
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- ik_llama.cpp
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---
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## `ik_llama.cpp` imatrix Quantizations of moonshotai/Kimi-K2-Instruct-0905
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This 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!
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*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.
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Some of ik's new quants are supported with [Nexesenex/croco.cpp](https://github.com/Nexesenex/croco.cpp) fork of KoboldCPP. For pre-built Windows binaries of ik_llama.cpp check out [Thireus' fork here](https://github.com/Thireus/ik_llama.cpp/releases).
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These quants provide best in class perplexity for the given memory footprint.
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## Big Thanks
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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!!!
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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!
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## Notes
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* The current imatrix dat file seems to be missing entries for just the single dense layer and shared expert so all my recipes are using `q8_0` for those.
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* For notes on tool calling api endpoints checkout details from this PR: https://github.com/ikawrakow/ik_llama.cpp/pull/723
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* `smol` here simply means the routed experts recipe uses the same quantization for down as well as (gate|up) tensors.
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## Quant Collection
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Compare with baseline perplexity of full size `Q8_0` 1016.117 GiB (8.504 BPW)
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Final estimate: PPL = 2.4443 +/- 0.01175
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### `smol-IQ5_KS` 632.664 GiB (5.295 BPW)
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Final estimate: PPL = 2.4526 +/- 0.01182
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<details>
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<summary>👈 Secret Recipe</summary>
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```bash
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#!/usr/bin/env bash
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custom="
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## Attention [0-60] (GPU)
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blk\..*\.attn_k_b\.weight=q8_0
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blk\..*\.attn_v_b\.weight=q8_0
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# Balance of attn tensors
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blk\..*\.attn_kv_a_mqa\.weight=q8_0
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blk\..*\.attn_q_a\.weight=q8_0
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blk\..*\.attn_q_b\.weight=q8_0
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blk\..*\.attn_output\.weight=q8_0
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## First Single Dense Layer [0] (GPU)
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blk\..*\.ffn_down\.weight=q8_0
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blk\..*\.ffn_(gate|up)\.weight=q8_0
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## Shared Expert [1-60] (GPU)
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blk\..*\.ffn_down_shexp\.weight=q8_0
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blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0
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## Routed Experts [1-60] (CPU)
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blk\..*\.ffn_down_exps\.weight=iq5_ks
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blk\..*\.ffn_(gate|up)_exps\.weight=iq5_ks
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## Token embedding and output tensors (GPU)
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token_embd\.weight=iq6_k
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output\.weight=iq6_k
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"
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custom=$(
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echo "$custom" | grep -v '^#' | \
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sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
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)
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numactl -N 0 -m 0 \
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./build/bin/llama-quantize \
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--custom-q "$custom" \
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--imatrix /mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/imatrix-Kimi-K2-Instruct-0905-Q8_0.dat \
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/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-384x14B-Instruct-safetensors-0905-BF16-00001-of-00046.gguf \
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/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-Instruct-0905-smol-IQ5_KS.gguf \
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IQ5_KS \
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192
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```
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</details>
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### `smol-IQ4_KSS` 485.008 GiB (4.059 BPW)
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Final estimate: PPL = 2.5185 +/- 0.01221
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<details>
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<summary>👈 Secret Recipe</summary>
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```bash
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#!/usr/bin/env bash
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custom="
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## Attention [0-60] (GPU)
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blk\..*\.attn_k_b\.weight=q8_0
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blk\..*\.attn_v_b\.weight=q8_0
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# Balance of attn tensors
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blk\..*\.attn_kv_a_mqa\.weight=q8_0
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blk\..*\.attn_q_a\.weight=q8_0
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blk\..*\.attn_q_b\.weight=q8_0
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blk\..*\.attn_output\.weight=q8_0
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## First Single Dense Layer [0] (GPU)
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blk\..*\.ffn_down\.weight=q8_0
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blk\..*\.ffn_(gate|up)\.weight=q8_0
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## Shared Expert [1-60] (GPU)
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blk\..*\.ffn_down_shexp\.weight=q8_0
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blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0
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## Routed Experts [1-60] (CPU)
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blk\..*\.ffn_down_exps\.weight=iq4_kss
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blk\..*\.ffn_(gate|up)_exps\.weight=iq4_kss
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## Token embedding and output tensors (GPU)
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token_embd\.weight=iq6_k
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output\.weight=iq6_k
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"
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custom=$(
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echo "$custom" | grep -v '^#' | \
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sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
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)
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numactl -N 0 -m 0 \
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./build/bin/llama-quantize \
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--custom-q "$custom" \
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--imatrix /mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/imatrix-Kimi-K2-Instruct-0905-Q8_0.dat \
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/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-384x14B-Instruct-safetensors-0905-BF16-00001-of-00046.gguf \
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/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-Instruct-0905-smol-IQ4_KSS.gguf \
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IQ4_KSS \
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192
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```
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</details>
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### `IQ4_KS` 553.624 GiB (4.633 BPW)
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Final estimate: PPL = 2.4641 +/- 0.01190
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<details>
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<summary>👈 Secret Recipe</summary>
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```bash
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#!/usr/bin/env bash
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custom="
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## Attention [0-60] (GPU)
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blk\..*\.attn_k_b\.weight=q8_0
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blk\..*\.attn_v_b\.weight=q8_0
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# Balance of attn tensors
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blk\..*\.attn_kv_a_mqa\.weight=q8_0
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blk\..*\.attn_q_a\.weight=q8_0
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blk\..*\.attn_q_b\.weight=q8_0
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blk\..*\.attn_output\.weight=q8_0
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## First Single Dense Layer [0] (GPU)
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blk\..*\.ffn_down\.weight=q8_0
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blk\..*\.ffn_(gate|up)\.weight=q8_0
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## Shared Expert [1-60] (GPU)
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blk\..*\.ffn_down_shexp\.weight=q8_0
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blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0
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## Routed Experts [1-60] (CPU)
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blk\..*\.ffn_down_exps\.weight=iq5_ks
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blk\..*\.ffn_(gate|up)_exps\.weight=iq4_ks
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## Token embedding and output tensors (GPU)
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token_embd\.weight=iq4_k
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output\.weight=iq6_k
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"
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custom=$(
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echo "$custom" | grep -v '^#' | \
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sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
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)
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numactl -N 1 -m 1 \
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./build/bin/llama-quantize \
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--custom-q "$custom" \
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--imatrix /mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/imatrix-Kimi-K2-Instruct-0905-Q8_0.dat \
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/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-384x14B-Instruct-safetensors-0905-BF16-00001-of-00046.gguf \
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/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-Instruct-0905-IQ4_KS.gguf \
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IQ4_KS \
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192
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```
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</details>
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### `IQ3_KS` 420.558 GiB (3.520 BPW)
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Final estimate: PPL = 2.5640 +/- 0.01262
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<details>
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<summary>👈 Secret Recipe</summary>
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```bash
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#!/usr/bin/env bash
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custom="
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## Attention [0-60] (GPU)
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blk\..*\.attn_k_b\.weight=q8_0
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blk\..*\.attn_v_b\.weight=q8_0
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# Balance of attn tensors
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blk\..*\.attn_kv_a_mqa\.weight=q8_0
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blk\..*\.attn_q_a\.weight=q8_0
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blk\..*\.attn_q_b\.weight=q8_0
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blk\..*\.attn_output\.weight=q8_0
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## First Single Dense Layer [0] (GPU)
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blk\..*\.ffn_down\.weight=q8_0
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blk\..*\.ffn_(gate|up)\.weight=q8_0
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## Shared Expert [1-60] (GPU)
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blk\..*\.ffn_down_shexp\.weight=q8_0
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blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0
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## Routed Experts [1-60] (CPU)
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blk\..*\.ffn_down_exps\.weight=iq4_kss
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blk\..*\.ffn_(gate|up)_exps\.weight=iq3_ks
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## Token embedding and output tensors (GPU)
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token_embd\.weight=iq4_k
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output\.weight=iq6_k
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"
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custom=$(
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echo "$custom" | grep -v '^#' | \
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sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
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)
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numactl -N 0 -m 0 \
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./build/bin/llama-quantize \
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--custom-q "$custom" \
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--imatrix /mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/imatrix-Kimi-K2-Instruct-0905-Q8_0.dat \
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/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-384x14B-Instruct-safetensors-0905-BF16-00001-of-00046.gguf \
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/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-Instruct-0905-IQ3_KS.gguf \
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IQ3_KS \
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192
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```
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</details>
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### `smol-IQ3_KS` 388.258 GiB (3.249 BPW)
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Final estimate: PPL = 2.5902 +/- 0.01284
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<details>
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<summary>👈 Secret Recipe</summary>
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```bash
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#!/usr/bin/env bash
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custom="
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## Attention [0-60] (GPU)
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blk\..*\.attn_k_b\.weight=q8_0
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blk\..*\.attn_v_b\.weight=q8_0
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# Balance of attn tensors
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blk\..*\.attn_kv_a_mqa\.weight=q8_0
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blk\..*\.attn_q_a\.weight=q8_0
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blk\..*\.attn_q_b\.weight=q8_0
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blk\..*\.attn_output\.weight=q8_0
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## First Single Dense Layer [0] (GPU)
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blk\..*\.ffn_down\.weight=q8_0
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blk\..*\.ffn_(gate|up)\.weight=q8_0
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## Shared Expert [1-60] (GPU)
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blk\..*\.ffn_down_shexp\.weight=q8_0
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blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0
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## Routed Experts [1-60] (CPU)
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blk\..*\.ffn_down_exps\.weight=iq3_ks
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blk\..*\.ffn_(gate|up)_exps\.weight=iq3_ks
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## Token embedding and output tensors (GPU)
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token_embd\.weight=iq4_k
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output\.weight=iq6_k
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"
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custom=$(
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echo "$custom" | grep -v '^#' | \
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sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
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)
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numactl -N 0 -m 0 \
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./build/bin/llama-quantize \
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--custom-q "$custom" \
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--imatrix /mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/imatrix-Kimi-K2-Instruct-0905-Q8_0.dat \
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/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-384x14B-Instruct-safetensors-0905-BF16-00001-of-00046.gguf \
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/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-Instruct-0905-smol-IQ3_KS.gguf \
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IQ3_KS \
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192
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```
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</details>
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### `IQ2_KL` 358.419 GiB (3.000 BPW)
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Final estimate: PPL = 2.7993 +/- 0.01416
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<details>
|
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|
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<summary>👈 Secret Recipe</summary>
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```bash
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#!/usr/bin/env bash
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custom="
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## Attention [0-60] (GPU)
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blk\..*\.attn_k_b\.weight=q8_0
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blk\..*\.attn_v_b\.weight=q8_0
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# Balance of attn tensors
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blk\..*\.attn_kv_a_mqa\.weight=q8_0
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blk\..*\.attn_q_a\.weight=q8_0
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blk\..*\.attn_q_b\.weight=q8_0
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blk\..*\.attn_output\.weight=q8_0
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## First Single Dense Layer [0] (GPU)
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blk\..*\.ffn_down\.weight=q8_0
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blk\..*\.ffn_(gate|up)\.weight=q8_0
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## Shared Expert [1-60] (GPU)
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blk\..*\.ffn_down_shexp\.weight=q8_0
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blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0
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## Routed Experts [1-60] (CPU)
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blk\..*\.ffn_down_exps\.weight=iq3_k
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blk\..*\.ffn_(gate|up)_exps\.weight=iq2_kl
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## Token embedding and output tensors (GPU)
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token_embd\.weight=iq4_k
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output\.weight=iq6_k
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"
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|
|
|
custom=$(
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echo "$custom" | grep -v '^#' | \
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sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
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)
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numactl -N 0 -m 0 \
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./build/bin/llama-quantize \
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--custom-q "$custom" \
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--imatrix /mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/imatrix-Kimi-K2-Instruct-0905-Q8_0.dat \
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/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-384x14B-Instruct-safetensors-0905-BF16-00001-of-00046.gguf \
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|
|
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-Instruct-0905-IQ2_KL.gguf \
|
|
|
IQ2_KL \
|
|
|
192
|
|
|
```
|
|
|
|
|
|
</details>
|
|
|
|
|
|
### `smol-IQ2_KL` 329.195 GiB (2.755 BPW)
|
|
|
Final estimate: PPL = 2.9294 +/- 0.01499
|
|
|
|
|
|
<details>
|
|
|
|
|
|
<summary>👈 Secret Recipe</summary>
|
|
|
|
|
|
```bash
|
|
|
#!/usr/bin/env bash
|
|
|
|
|
|
custom="
|
|
|
## Attention [0-60] (GPU)
|
|
|
blk\..*\.attn_k_b\.weight=q8_0
|
|
|
blk\..*\.attn_v_b\.weight=q8_0
|
|
|
|
|
|
# Balance of attn tensors
|
|
|
blk\..*\.attn_kv_a_mqa\.weight=q8_0
|
|
|
blk\..*\.attn_q_a\.weight=q8_0
|
|
|
blk\..*\.attn_q_b\.weight=q8_0
|
|
|
blk\..*\.attn_output\.weight=q8_0
|
|
|
|
|
|
## First Single Dense Layer [0] (GPU)
|
|
|
blk\..*\.ffn_down\.weight=q8_0
|
|
|
blk\..*\.ffn_(gate|up)\.weight=q8_0
|
|
|
|
|
|
## Shared Expert [1-60] (GPU)
|
|
|
blk\..*\.ffn_down_shexp\.weight=q8_0
|
|
|
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0
|
|
|
|
|
|
## Routed Experts [1-60] (CPU)
|
|
|
blk\..*\.ffn_down_exps\.weight=iq2_kl
|
|
|
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_kl
|
|
|
|
|
|
## Token embedding and output tensors (GPU)
|
|
|
token_embd\.weight=iq4_k
|
|
|
output\.weight=iq6_k
|
|
|
"
|
|
|
|
|
|
custom=$(
|
|
|
echo "$custom" | grep -v '^#' | \
|
|
|
sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
|
|
|
)
|
|
|
|
|
|
numactl -N 1 -m 1 \
|
|
|
./build/bin/llama-quantize \
|
|
|
--custom-q "$custom" \
|
|
|
--imatrix /mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/imatrix-Kimi-K2-Instruct-0905-Q8_0.dat \
|
|
|
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-384x14B-Instruct-safetensors-0905-BF16-00001-of-00046.gguf \
|
|
|
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-Instruct-0905-smol-IQ2_KL.gguf \
|
|
|
IQ2_KL \
|
|
|
192
|
|
|
```
|
|
|
|
|
|
</details>
|
|
|
|
|
|
### `IQ2_KS` 289.820 GiB (2.425 BPW)
|
|
|
Final estimate: PPL = 3.2478 +/- 0.01721
|
|
|
|
|
|
<details>
|
|
|
|
|
|
<summary>👈 Secret Recipe</summary>
|
|
|
|
|
|
```bash
|
|
|
#!/usr/bin/env bash
|
|
|
|
|
|
custom="
|
|
|
## Attention [0-60] (GPU)
|
|
|
blk\..*\.attn_k_b\.weight=q8_0
|
|
|
blk\..*\.attn_v_b\.weight=q8_0
|
|
|
|
|
|
# Balance of attn tensors
|
|
|
blk\..*\.attn_kv_a_mqa\.weight=q8_0
|
|
|
blk\..*\.attn_q_a\.weight=q8_0
|
|
|
blk\..*\.attn_q_b\.weight=q8_0
|
|
|
blk\..*\.attn_output\.weight=q8_0
|
|
|
|
|
|
## First Single Dense Layer [0] (GPU)
|
|
|
blk\..*\.ffn_down\.weight=q8_0
|
|
|
blk\..*\.ffn_(gate|up)\.weight=q8_0
|
|
|
|
|
|
## Shared Expert [1-60] (GPU)
|
|
|
blk\..*\.ffn_down_shexp\.weight=q8_0
|
|
|
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0
|
|
|
|
|
|
## Routed Experts [1-60] (CPU)
|
|
|
blk\..*\.ffn_down_exps\.weight=iq2_kl
|
|
|
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_ks
|
|
|
|
|
|
## Token embedding and output tensors (GPU)
|
|
|
token_embd\.weight=iq4_k
|
|
|
output\.weight=iq6_k
|
|
|
"
|
|
|
|
|
|
custom=$(
|
|
|
echo "$custom" | grep -v '^#' | \
|
|
|
sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
|
|
|
)
|
|
|
|
|
|
numactl -N 1 -m 1 \
|
|
|
./build/bin/llama-quantize \
|
|
|
--custom-q "$custom" \
|
|
|
--imatrix /mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/imatrix-Kimi-K2-Instruct-0905-Q8_0.dat \
|
|
|
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-384x14B-Instruct-safetensors-0905-BF16-00001-of-00046.gguf \
|
|
|
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-Instruct-0905-IQ2_KS.gguf \
|
|
|
IQ2_KS \
|
|
|
192
|
|
|
```
|
|
|
|
|
|
</details>
|
|
|
|
|
|
### `smol-IQ2_KS` 270.133 GiB (2.261 BPW)
|
|
|
Final estimate: PPL = 3.4977 +/- 0.01924
|
|
|
|
|
|
<details>
|
|
|
|
|
|
<summary>👈 Secret Recipe</summary>
|
|
|
|
|
|
```bash
|
|
|
#!/usr/bin/env bash
|
|
|
|
|
|
custom="
|
|
|
## Attention [0-60] (GPU)
|
|
|
blk\..*\.attn_k_b\.weight=q8_0
|
|
|
blk\..*\.attn_v_b\.weight=q8_0
|
|
|
|
|
|
# Balance of attn tensors
|
|
|
blk\..*\.attn_kv_a_mqa\.weight=q8_0
|
|
|
blk\..*\.attn_q_a\.weight=q8_0
|
|
|
blk\..*\.attn_q_b\.weight=q8_0
|
|
|
blk\..*\.attn_output\.weight=q8_0
|
|
|
|
|
|
## First Single Dense Layer [0] (GPU)
|
|
|
blk\..*\.ffn_down\.weight=q8_0
|
|
|
blk\..*\.ffn_(gate|up)\.weight=q8_0
|
|
|
|
|
|
## Shared Expert [1-60] (GPU)
|
|
|
blk\..*\.ffn_down_shexp\.weight=q8_0
|
|
|
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0
|
|
|
|
|
|
## Routed Experts [1-60] (CPU)
|
|
|
blk\..*\.ffn_down_exps\.weight=iq2_ks
|
|
|
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_ks
|
|
|
|
|
|
## Token embedding and output tensors (GPU)
|
|
|
token_embd\.weight=iq4_k
|
|
|
output\.weight=iq6_k
|
|
|
"
|
|
|
|
|
|
custom=$(
|
|
|
echo "$custom" | grep -v '^#' | \
|
|
|
sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
|
|
|
)
|
|
|
|
|
|
numactl -N 0 -m 0 \
|
|
|
./build/bin/llama-quantize \
|
|
|
--custom-q "$custom" \
|
|
|
--imatrix /mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/imatrix-Kimi-K2-Instruct-0905-Q8_0.dat \
|
|
|
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-384x14B-Instruct-safetensors-0905-BF16-00001-of-00046.gguf \
|
|
|
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-Instruct-0905-smol-IQ2_KS.gguf \
|
|
|
IQ2_KS \
|
|
|
192
|
|
|
```
|
|
|
|
|
|
</details>
|
|
|
|
|
|
### `smol-IQ1_KT` 218.936 GiB (1.832 BPW)
|
|
|
Final estimate: PPL = 4.2224 +/- 0.02443
|
|
|
|
|
|
<details>
|
|
|
|
|
|
<summary>👈 Secret Recipe</summary>
|
|
|
|
|
|
```bash
|
|
|
#!/usr/bin/env bash
|
|
|
|
|
|
custom="
|
|
|
## Attention [0-60] (GPU)
|
|
|
blk\..*\.attn_k_b\.weight=q8_0
|
|
|
blk\..*\.attn_v_b\.weight=q8_0
|
|
|
|
|
|
# Balance of attn tensors
|
|
|
blk\..*\.attn_kv_a_mqa\.weight=q8_0
|
|
|
blk\..*\.attn_q_a\.weight=q8_0
|
|
|
blk\..*\.attn_q_b\.weight=q8_0
|
|
|
blk\..*\.attn_output\.weight=q8_0
|
|
|
|
|
|
## First Single Dense Layer [0] (GPU)
|
|
|
blk\..*\.ffn_down\.weight=q8_0
|
|
|
blk\..*\.ffn_(gate|up)\.weight=q8_0
|
|
|
|
|
|
## Shared Expert [1-60] (GPU)
|
|
|
blk\..*\.ffn_down_shexp\.weight=q8_0
|
|
|
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0
|
|
|
|
|
|
## Routed Experts [1-60] (CPU)
|
|
|
blk\..*\.ffn_down_exps\.weight=iq1_kt
|
|
|
blk\..*\.ffn_(gate|up)_exps\.weight=iq1_kt
|
|
|
|
|
|
## Token embedding and output tensors (GPU)
|
|
|
token_embd\.weight=iq4_k
|
|
|
output\.weight=iq6_k
|
|
|
"
|
|
|
|
|
|
custom=$(
|
|
|
echo "$custom" | grep -v '^#' | \
|
|
|
sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
|
|
|
)
|
|
|
|
|
|
numactl -N 0 -m 0 \
|
|
|
./build/bin/llama-quantize \
|
|
|
--custom-q "$custom" \
|
|
|
--imatrix /mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/imatrix-Kimi-K2-Instruct-0905-Q8_0.dat \
|
|
|
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-384x14B-Instruct-safetensors-0905-BF16-00001-of-00046.gguf \
|
|
|
/mnt/data/models/ubergarm/Kimi-K2-Instruct-0905-GGUF/Kimi-K2-Instruct-0905-smol-IQ1_KT.gguf \
|
|
|
IQ1_KT \
|
|
|
192
|
|
|
```
|
|
|
|
|
|
</details>
|
|
|
|
|
|
## Example Commands
|
|
|
### Hybrid (multiple) CUDA + CPU
|
|
|
```bash
|
|
|
# Two CUDA devices with enough VRAM to offload more layers
|
|
|
# Keep in mind Kimi-K2 starts at 1 unlike DeepSeek at 3 (first dense layers)
|
|
|
./build/bin/llama-server \
|
|
|
--model "$model"\
|
|
|
--alias ubergarm/Kimi-K2-Instruct-0905 \
|
|
|
--ctx-size 32768 \
|
|
|
-ctk q8_0 \
|
|
|
-fa -fmoe \
|
|
|
-mla 3 \
|
|
|
-ngl 99 \
|
|
|
-ot "blk\.(1|2|3)\.ffn_.*=CUDA0" \
|
|
|
-ot "blk\.(4|5|6)\.ffn_.*=CUDA1" \
|
|
|
-ot exps=CPU \
|
|
|
--parallel 1 \
|
|
|
--threads 48 \
|
|
|
--threads-batch 64 \
|
|
|
--host 127.0.0.1 \
|
|
|
--port 8080
|
|
|
```
|
|
|
|
|
|
### CPU-Only (no GPU)
|
|
|
```bash
|
|
|
# compile
|
|
|
cmake -B build -DGGML_CUDA=0 -DGGML_BLAS=0 -DGGML_VULKAN=0
|
|
|
cmake --build build --config Release -j $(nproc)
|
|
|
|
|
|
# run server
|
|
|
# single CPU of a dual socket rig configured one NUMA per socket
|
|
|
numactl -N 0 -m 0 \
|
|
|
./build/bin/llama-server \
|
|
|
--model "$model"\
|
|
|
--alias ubergarm/Kimi-K2-Instruct-0905 \
|
|
|
--ctx-size 98304 \
|
|
|
-ctk q8_0 \
|
|
|
-fa -fmoe \
|
|
|
-mla 3 \
|
|
|
--parallel 1 \
|
|
|
--threads 128 \
|
|
|
--threads-batch 192 \
|
|
|
--numa numactl \
|
|
|
--host 127.0.0.1 \
|
|
|
--port 8080
|
|
|
```
|
|
|
|
|
|
## References
|
|
|
* [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp)
|
|
|
|
