Instructions to use ling1000T/Kimi-K2-Thinking-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ling1000T/Kimi-K2-Thinking-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ling1000T/Kimi-K2-Thinking-gguf",
	filename="Kimi-K2-Thinking-q2_k.gguf-00001-of-00037.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ling1000T/Kimi-K2-Thinking-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ling1000T/Kimi-K2-Thinking-gguf:Q2_K
# Run inference directly in the terminal:
llama-cli -hf ling1000T/Kimi-K2-Thinking-gguf:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ling1000T/Kimi-K2-Thinking-gguf:Q2_K
# Run inference directly in the terminal:
llama-cli -hf ling1000T/Kimi-K2-Thinking-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 ling1000T/Kimi-K2-Thinking-gguf:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf ling1000T/Kimi-K2-Thinking-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 ling1000T/Kimi-K2-Thinking-gguf:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ling1000T/Kimi-K2-Thinking-gguf:Q2_K

Use Docker

docker model run hf.co/ling1000T/Kimi-K2-Thinking-gguf:Q2_K

LM Studio
Jan
Ollama
How to use ling1000T/Kimi-K2-Thinking-gguf with Ollama:
```
ollama run hf.co/ling1000T/Kimi-K2-Thinking-gguf:Q2_K
```

Unsloth Studio

How to use ling1000T/Kimi-K2-Thinking-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 ling1000T/Kimi-K2-Thinking-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 ling1000T/Kimi-K2-Thinking-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ling1000T/Kimi-K2-Thinking-gguf to start chatting

How to use ling1000T/Kimi-K2-Thinking-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ling1000T/Kimi-K2-Thinking-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": "ling1000T/Kimi-K2-Thinking-gguf:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ling1000T/Kimi-K2-Thinking-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ling1000T/Kimi-K2-Thinking-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 ling1000T/Kimi-K2-Thinking-gguf:Q2_K

Run Hermes

hermes

Docker Model Runner
How to use ling1000T/Kimi-K2-Thinking-gguf with Docker Model Runner:
```
docker model run hf.co/ling1000T/Kimi-K2-Thinking-gguf:Q2_K
```

Lemonade

How to use ling1000T/Kimi-K2-Thinking-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ling1000T/Kimi-K2-Thinking-gguf:Q2_K

Run and chat with the model

lemonade run user.Kimi-K2-Thinking-gguf-Q2_K

List all available models

lemonade list

Kimi K2 Thinking gguf

One of best thinking models. modified-mit license (see moonshotai/Kimi-K2-Thinking)

Evaluation

Reasoning Tasks

Benchmark	Setting	K2 Thinking	GPT-5 (High)	Claude Sonnet 4.5 (Thinking)	K2 0905	DeepSeek-V3.2	Grok-4
HLE (Text-only)	no tools	23.9	26.3	19.8*	7.9	19.8	25.4
	w/ tools	44.9	41.7*	32.0*	21.7	20.3*	41.0
	heavy	51.0	42.0	-	-	-	50.7
AIME25	no tools	94.5	94.6	87.0	51.0	89.3	91.7
	w/ python	99.1	99.6	100.0	75.2	58.1*	98.8
	heavy	100.0	100.0	-	-	-	100.0
HMMT25	no tools	89.4	93.3	74.6*	38.8	83.6	90.0
	w/ python	95.1	96.7	88.8*	70.4	49.5*	93.9
	heavy	97.5	100.0	-	-	-	96.7
IMO-AnswerBench	no tools	78.6	76.0*	65.9*	45.8	76.0*	73.1
GPQA	no tools	84.5	85.7	83.4	74.2	79.9	87.5

Agentic Search Tasks

Benchmark	Setting	K2 Thinking	GPT-5 (High)	Claude Sonnet 4.5 (Thinking)	K2 0905	DeepSeek-V3.2
BrowseComp	w/ tools	60.2	54.9	24.1	7.4	40.1
BrowseComp-ZH	w/ tools	62.3	63.0*	42.4*	22.2	47.9
Seal-0	w/ tools	56.3	51.4*	53.4*	25.2	38.5*
FinSearchComp-T3	w/ tools	47.4	48.5*	44.0*	10.4	27.0*
Frames	w/ tools	87.0	86.0*	85.0*	58.1	80.2*

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GGUF

Model size

1T params

Architecture

deepseek2

Hardware compatibility

2-bit

3-bit

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Model tree for ling1000T/Kimi-K2-Thinking-gguf

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moonshotai/Kimi-K2-Thinking

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