--- quantized_by: ubergarm pipeline_tag: text-generation base_model: moonshotai/Kimi-K2-Instruct-0905 license: other license_name: modified-mit license_link: https://huggingface.co/moonshotai/Kimi-K2-Instruct-0905/blob/main/LICENSE base_model_relation: quantized tags: - mla - imatrix - conversational - ik_llama.cpp --- ## `ik_llama.cpp` imatrix Quantizations of moonshotai/Kimi-K2-Instruct-0905 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! *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. 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! ## Notes * 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. * For notes on tool calling api endpoints checkout details from this PR: https://github.com/ikawrakow/ik_llama.cpp/pull/723 * `smol` here simply means the routed experts recipe uses the same quantization for down as well as (gate|up) tensors. ## Quant Collection Compare with baseline perplexity of full size `Q8_0` 1016.117 GiB (8.504 BPW) Final estimate: PPL = 2.4443 +/- 0.01175 ![Perplexity Chart](images/perplexity.png "Chart showing Perplexity improving as BPW increases.") ### `smol-IQ5_KS` 632.664 GiB (5.295 BPW) Final estimate: PPL = 2.4526 +/- 0.01182
👈 Secret Recipe ```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=iq5_ks blk\..*\.ffn_(gate|up)_exps\.weight=iq5_ks ## Token embedding and output tensors (GPU) token_embd\.weight=iq6_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-IQ5_KS.gguf \ IQ5_KS \ 192 ```
### `smol-IQ4_KSS` 485.008 GiB (4.059 BPW) Final estimate: PPL = 2.5185 +/- 0.01221
👈 Secret Recipe ```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=iq4_kss blk\..*\.ffn_(gate|up)_exps\.weight=iq4_kss ## Token embedding and output tensors (GPU) token_embd\.weight=iq6_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-IQ4_KSS.gguf \ IQ4_KSS \ 192 ```
### `IQ3_KS` TODO Final estimate: PPL = TODO
👈 Secret Recipe ```bash echo TODO ```
### `smol-IQ2_KL` 329.195 GiB (2.755 BPW) Final estimate: PPL = 2.9294 +/- 0.01499
👈 Secret Recipe ```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 ```
### `IQ2_KS` 289.820 GiB (2.425 BPW) Final estimate: PPL = 3.2478 +/- 0.01721
👈 Secret Recipe ```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 ```
### `smol-IQ2_KS` 270.133 GiB (2.261 BPW) Final estimate: PPL = 3.4977 +/- 0.01924
👈 Secret Recipe ```bash echo TODO ```
### `smol-IQ1_KT` 218.936 GiB (1.832 BPW) Final estimate: PPL = 4.2224 +/- 0.02443
👈 Secret Recipe ```bash echo TODO ```
## 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)