Instructions to use unsloth/Kimi-K2-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unsloth/Kimi-K2-Instruct-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("unsloth/Kimi-K2-Instruct-GGUF", dtype="auto") - llama-cpp-python
How to use unsloth/Kimi-K2-Instruct-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="unsloth/Kimi-K2-Instruct-GGUF", filename="BF16/Kimi-K2-Instruct-BF16-00001-of-00045.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 unsloth/Kimi-K2-Instruct-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf unsloth/Kimi-K2-Instruct-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama-cli -hf unsloth/Kimi-K2-Instruct-GGUF:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf unsloth/Kimi-K2-Instruct-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama-cli -hf unsloth/Kimi-K2-Instruct-GGUF:UD-Q4_K_XL
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 unsloth/Kimi-K2-Instruct-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf unsloth/Kimi-K2-Instruct-GGUF:UD-Q4_K_XL
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 unsloth/Kimi-K2-Instruct-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf unsloth/Kimi-K2-Instruct-GGUF:UD-Q4_K_XL
Use Docker
docker model run hf.co/unsloth/Kimi-K2-Instruct-GGUF:UD-Q4_K_XL
- LM Studio
- Jan
- Ollama
How to use unsloth/Kimi-K2-Instruct-GGUF with Ollama:
ollama run hf.co/unsloth/Kimi-K2-Instruct-GGUF:UD-Q4_K_XL
- Unsloth Studio
How to use unsloth/Kimi-K2-Instruct-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 unsloth/Kimi-K2-Instruct-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 unsloth/Kimi-K2-Instruct-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for unsloth/Kimi-K2-Instruct-GGUF to start chatting
- Pi
How to use unsloth/Kimi-K2-Instruct-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf unsloth/Kimi-K2-Instruct-GGUF:UD-Q4_K_XL
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": "unsloth/Kimi-K2-Instruct-GGUF:UD-Q4_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use unsloth/Kimi-K2-Instruct-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 unsloth/Kimi-K2-Instruct-GGUF:UD-Q4_K_XL
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 unsloth/Kimi-K2-Instruct-GGUF:UD-Q4_K_XL
Run Hermes
hermes
- Docker Model Runner
How to use unsloth/Kimi-K2-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/unsloth/Kimi-K2-Instruct-GGUF:UD-Q4_K_XL
- Lemonade
How to use unsloth/Kimi-K2-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull unsloth/Kimi-K2-Instruct-GGUF:UD-Q4_K_XL
Run and chat with the model
lemonade run user.Kimi-K2-Instruct-GGUF-UD-Q4_K_XL
List all available models
lemonade list
Good llama.cpp -ot offloading parameter for 24 GB / 32 GB cards?
I'm trying out this model on my 32 GB GPU, and my usual llama.cpp DeepSeek offloading string, which is:
-ot "blk.*.ffn_.*._exps.=CPU"
is only using up just under 50% of my VRAM (with my current context size of 19000). Would you be able to suggest a better -ot string for a 32 GB card that moves more to VRAM? Also, for people with 24 GB cards, would you know a good offloading parameter for those too?
Thanks!
take the model size, divide it by 61, you get approximately the size of each layer. if you look at the meta data of the layers, you can calculate about how much size each tensor takes and can use that to determine which ones to load. with loading tensors, the idea is to load as much shared experts as possible onto the card. This model is a huge model!!!