README.md

---

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
---


## **WIP**

- [x] download fp8 safetensors
- [x] cast fp8 safetensors to bf16 safetensors
- [x] convert to bf16 GGUF
- [x] quantize Q8_0 without imatrix

- [x] calculate and upload imatrix from Q8_0 (note imatrix is missing data for a few tensors: https://huggingface.co/ubergarm/Kimi-K2-Instruct-0905-GGUF/discussions/1#68bc58de31fa67452e075b9f )
- [x] begin quantizing and releasing
- [x] IQ2_KS

- [ ] smol-IQ4_KSS
- [ ] smol-IQ2_KL

- [ ] etc...



Open a discussion if you have a specific target RAM+VRAM in mind for your rig and I'll see what I can do given the available quants. Cheers!



## `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!

## Quant Collection
Compare with Perplexity of full size `Q8_0` TODO

Final estimate: PPL = TODO

![Perplexity Chart](images/perplexity.png "Chart showing Perplexity improving as BPW increases.")

### `smol-IQ4_KSS` TODO

Final estimate: PPL = TODO



<details>



<summary>👈 Secret Recipe</summary>



```bash

echo TODO

```



</details>



### `IQ3_KS` TODO
Final estimate: PPL = TODO

<details>

<summary>👈 Secret Recipe</summary>

```bash

echo TODO

```

</details>


### `IQ2_KL` TODO

Final estimate: PPL = TODO



<details>



<summary>👈 Secret Recipe</summary>



```bash

echo TODO

```



</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>

### `IQ1_KT` TODO

Final estimate: PPL = TODO



<details>



<summary>👈 Secret Recipe</summary>



```bash

echo TODO

```



</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)