Instructions to use QuantPasture/Kimi-K2.5-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use QuantPasture/Kimi-K2.5-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 QuantPasture/Kimi-K2.5-GGUF:IQ2_S # Run inference directly in the terminal: llama cli -hf QuantPasture/Kimi-K2.5-GGUF:IQ2_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf QuantPasture/Kimi-K2.5-GGUF:IQ2_S # Run inference directly in the terminal: llama cli -hf QuantPasture/Kimi-K2.5-GGUF:IQ2_S
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 QuantPasture/Kimi-K2.5-GGUF:IQ2_S # Run inference directly in the terminal: ./llama-cli -hf QuantPasture/Kimi-K2.5-GGUF:IQ2_S
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 QuantPasture/Kimi-K2.5-GGUF:IQ2_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantPasture/Kimi-K2.5-GGUF:IQ2_S
Use Docker
docker model run hf.co/QuantPasture/Kimi-K2.5-GGUF:IQ2_S
- LM Studio
- Jan
- Ollama
How to use QuantPasture/Kimi-K2.5-GGUF with Ollama:
ollama run hf.co/QuantPasture/Kimi-K2.5-GGUF:IQ2_S
- Unsloth Desktop
- Pi
How to use QuantPasture/Kimi-K2.5-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf QuantPasture/Kimi-K2.5-GGUF:IQ2_S
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "QuantPasture/Kimi-K2.5-GGUF:IQ2_S" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use QuantPasture/Kimi-K2.5-GGUF with Docker Model Runner:
docker model run hf.co/QuantPasture/Kimi-K2.5-GGUF:IQ2_S
- Lemonade
How to use QuantPasture/Kimi-K2.5-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantPasture/Kimi-K2.5-GGUF:IQ2_S
Run and chat with the model
lemonade run user.Kimi-K2.5-GGUF-IQ2_S
List all available models
lemonade list
- Hermes Agent
How to use QuantPasture/Kimi-K2.5-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 QuantPasture/Kimi-K2.5-GGUF:IQ2_S
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 QuantPasture/Kimi-K2.5-GGUF:IQ2_S
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use QuantPasture/Kimi-K2.5-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf QuantPasture/Kimi-K2.5-GGUF:IQ2_S
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 "QuantPasture/Kimi-K2.5-GGUF:IQ2_S" \ --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"
Any plans to Derestrict it?
Hi!
Do you have plans to derestrict this model and create the control vectors? Would be awesome for creative writing.
Thanks.
Hi @kabachuha , I'm considering it. I've been trying to work around the intelligence drops that comes from abliteration but that hasn't been as successful as I'd hoped and I don't really have the VRAM readily available to really iterate on the method on mid-sized models like I'd want.
I'm concerned that doing the norm-preserving biprojected ablation (grimjim's process) would end up producing a semi-lobotomized model still. My GLM-4.6-Derestriction had some serious derps in it, too.
I'll keep experimenting with the process but no promises, sorry.
Yes, VRAM / experimentation problem is awful even for enterprise nowadays, with all the availability problems, and I see your perspective. Still, glad you are considering it!
Thank you for the GLM-4.6 Derestriction so much, it's currently my daily model! (Not for coding, but for RP) And it perfectly works with control vectors, making it even more awesome.
Hope you will figure the method out!