Instructions to use terribleplan/moonshotai_Kimi-K2-Instruct_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use terribleplan/moonshotai_Kimi-K2-Instruct_GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="terribleplan/moonshotai_Kimi-K2-Instruct_GGUF", filename="BF16/moonshotai-Kimi-K2-384x14B-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 terribleplan/moonshotai_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 terribleplan/moonshotai_Kimi-K2-Instruct_GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf terribleplan/moonshotai_Kimi-K2-Instruct_GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf terribleplan/moonshotai_Kimi-K2-Instruct_GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf terribleplan/moonshotai_Kimi-K2-Instruct_GGUF:Q4_K_M
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 terribleplan/moonshotai_Kimi-K2-Instruct_GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf terribleplan/moonshotai_Kimi-K2-Instruct_GGUF:Q4_K_M
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 terribleplan/moonshotai_Kimi-K2-Instruct_GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf terribleplan/moonshotai_Kimi-K2-Instruct_GGUF:Q4_K_M
Use Docker
docker model run hf.co/terribleplan/moonshotai_Kimi-K2-Instruct_GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use terribleplan/moonshotai_Kimi-K2-Instruct_GGUF with Ollama:
ollama run hf.co/terribleplan/moonshotai_Kimi-K2-Instruct_GGUF:Q4_K_M
- Unsloth Studio
How to use terribleplan/moonshotai_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 terribleplan/moonshotai_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 terribleplan/moonshotai_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 terribleplan/moonshotai_Kimi-K2-Instruct_GGUF to start chatting
- Pi
How to use terribleplan/moonshotai_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 terribleplan/moonshotai_Kimi-K2-Instruct_GGUF:Q4_K_M
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": "terribleplan/moonshotai_Kimi-K2-Instruct_GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use terribleplan/moonshotai_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 terribleplan/moonshotai_Kimi-K2-Instruct_GGUF:Q4_K_M
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 terribleplan/moonshotai_Kimi-K2-Instruct_GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use terribleplan/moonshotai_Kimi-K2-Instruct_GGUF with Docker Model Runner:
docker model run hf.co/terribleplan/moonshotai_Kimi-K2-Instruct_GGUF:Q4_K_M
- Lemonade
How to use terribleplan/moonshotai_Kimi-K2-Instruct_GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull terribleplan/moonshotai_Kimi-K2-Instruct_GGUF:Q4_K_M
Run and chat with the model
lemonade run user.moonshotai_Kimi-K2-Instruct_GGUF-Q4_K_M
List all available models
lemonade list
GGUF quants of moonshotai/Kimi-K2-Instruct
Quantization was performed without imatrix for the purposes comparison and experimentation. Perplixity may be worse than expected due to this naive approach.
Read sample outputs of the quants and my report on them
| Name | Version |
|---|---|
| moonshotai/Kimi-K2-Instruct | c2fee60e5323 |
| convert_hf_to_gguf.py | full-b7588 |
llama-quantize and llama-gguf-split |
c8a37980419e |
See the original model card here.
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Base model
moonshotai/Kimi-K2-Instruct