Instructions to use ubergarm/Kimi-K2-Instruct-0905-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ubergarm/Kimi-K2-Instruct-0905-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ubergarm/Kimi-K2-Instruct-0905-GGUF",
	filename="IQ2_KL/Kimi-K2-Instruct-0905-IQ2_KL-00001-of-00008.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ubergarm/Kimi-K2-Instruct-0905-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ubergarm/Kimi-K2-Instruct-0905-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/Kimi-K2-Instruct-0905-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ubergarm/Kimi-K2-Instruct-0905-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/Kimi-K2-Instruct-0905-GGUF:Q2_K

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ubergarm/Kimi-K2-Instruct-0905-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/Kimi-K2-Instruct-0905-GGUF:Q2_K

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ubergarm/Kimi-K2-Instruct-0905-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/Kimi-K2-Instruct-0905-GGUF:Q2_K

Use Docker

docker model run hf.co/ubergarm/Kimi-K2-Instruct-0905-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use ubergarm/Kimi-K2-Instruct-0905-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/Kimi-K2-Instruct-0905-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ubergarm/Kimi-K2-Instruct-0905-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/Kimi-K2-Instruct-0905-GGUF:Q2_K

Ollama
How to use ubergarm/Kimi-K2-Instruct-0905-GGUF with Ollama:
```
ollama run hf.co/ubergarm/Kimi-K2-Instruct-0905-GGUF:Q2_K
```

Unsloth Studio

How to use ubergarm/Kimi-K2-Instruct-0905-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ubergarm/Kimi-K2-Instruct-0905-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ubergarm/Kimi-K2-Instruct-0905-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ubergarm/Kimi-K2-Instruct-0905-GGUF to start chatting

How to use ubergarm/Kimi-K2-Instruct-0905-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/Kimi-K2-Instruct-0905-GGUF:Q2_K

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ubergarm/Kimi-K2-Instruct-0905-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ubergarm/Kimi-K2-Instruct-0905-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/Kimi-K2-Instruct-0905-GGUF:Q2_K

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ubergarm/Kimi-K2-Instruct-0905-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ubergarm/Kimi-K2-Instruct-0905-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/Kimi-K2-Instruct-0905-GGUF:Q2_K

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ubergarm/Kimi-K2-Instruct-0905-GGUF:Q2_K" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use ubergarm/Kimi-K2-Instruct-0905-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/Kimi-K2-Instruct-0905-GGUF:Q2_K
```

Lemonade

How to use ubergarm/Kimi-K2-Instruct-0905-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/Kimi-K2-Instruct-0905-GGUF:Q2_K

Run and chat with the model

lemonade run user.Kimi-K2-Instruct-0905-GGUF-Q2_K

List all available models

lemonade list

`ik_llama.cpp` imatrix Quantizations of moonshotai/Kimi-K2-Instruct-0905

This quant collection REQUIRES 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 fork of KoboldCPP. For pre-built Windows binaries of ik_llama.cpp check out Thireus' fork here.

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, YouTube Channel! 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 and on 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

`smol-IQ5_KS` 632.664 GiB (5.295 BPW)

Final estimate: PPL = 2.4526 +/- 0.01182

👈 Secret Recipe

#!/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

#!/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

`IQ4_KS` 553.624 GiB (4.633 BPW)

Final estimate: PPL = 2.4641 +/- 0.01190

👈 Secret Recipe

#!/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=iq4_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-IQ4_KS.gguf \
    IQ4_KS \
    192

`IQ3_KS` 420.558 GiB (3.520 BPW)

Final estimate: PPL = 2.5640 +/- 0.01262

👈 Secret Recipe

#!/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=iq3_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-IQ3_KS.gguf \
    IQ3_KS \
    192

`smol-IQ3_KS` 388.258 GiB (3.249 BPW)

Final estimate: PPL = 2.5902 +/- 0.01284

👈 Secret Recipe

#!/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=iq3_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq3_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-IQ3_KS.gguf \
    IQ3_KS \
    192

`IQ2_KL` 358.419 GiB (3.000 BPW)

Final estimate: PPL = 2.7993 +/- 0.01416

👈 Secret Recipe

#!/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=iq3_k
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 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-IQ2_KL.gguf \
    IQ2_KL \
    192

`smol-IQ2_KL` 329.195 GiB (2.755 BPW)

Final estimate: PPL = 2.9294 +/- 0.01499

👈 Secret Recipe

#!/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

#!/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

#!/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

`smol-IQ1_KT` 218.936 GiB (1.832 BPW)

Final estimate: PPL = 4.2224 +/- 0.02443

👈 Secret Recipe

#!/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

Example Commands

Hybrid (multiple) CUDA + CPU

# 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)

# 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

Downloads last month: 225

GGUF

Model size

1T params

Architecture

deepseek2

Hardware compatibility

2-bit

View +1 variant

Model tree for ubergarm/Kimi-K2-Instruct-0905-GGUF

Base model

moonshotai/Kimi-K2-Instruct-0905

Quantized

(16)

this model

ik_llama.cpp imatrix Quantizations of moonshotai/Kimi-K2-Instruct-0905

Big Thanks

Notes

Quant Collection

smol-IQ5_KS 632.664 GiB (5.295 BPW)

smol-IQ4_KSS 485.008 GiB (4.059 BPW)

IQ4_KS 553.624 GiB (4.633 BPW)

IQ3_KS 420.558 GiB (3.520 BPW)

smol-IQ3_KS 388.258 GiB (3.249 BPW)

IQ2_KL 358.419 GiB (3.000 BPW)

smol-IQ2_KL 329.195 GiB (2.755 BPW)

IQ2_KS 289.820 GiB (2.425 BPW)

smol-IQ2_KS 270.133 GiB (2.261 BPW)

smol-IQ1_KT 218.936 GiB (1.832 BPW)

Example Commands

Hybrid (multiple) CUDA + CPU

CPU-Only (no GPU)

References

Model tree for ubergarm/Kimi-K2-Instruct-0905-GGUF

`ik_llama.cpp` imatrix Quantizations of moonshotai/Kimi-K2-Instruct-0905

`smol-IQ5_KS` 632.664 GiB (5.295 BPW)

`smol-IQ4_KSS` 485.008 GiB (4.059 BPW)

`IQ4_KS` 553.624 GiB (4.633 BPW)

`IQ3_KS` 420.558 GiB (3.520 BPW)

`smol-IQ3_KS` 388.258 GiB (3.249 BPW)

`IQ2_KL` 358.419 GiB (3.000 BPW)

`smol-IQ2_KL` 329.195 GiB (2.755 BPW)

`IQ2_KS` 289.820 GiB (2.425 BPW)

`smol-IQ2_KS` 270.133 GiB (2.261 BPW)

`smol-IQ1_KT` 218.936 GiB (1.832 BPW)