Instructions to use gtest23/K3-Q4-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 gtest23/K3-Q4-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 gtest23/K3-Q4-GGUF # Run inference directly in the terminal: llama cli -hf gtest23/K3-Q4-GGUF
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf gtest23/K3-Q4-GGUF # Run inference directly in the terminal: llama cli -hf gtest23/K3-Q4-GGUF
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 gtest23/K3-Q4-GGUF # Run inference directly in the terminal: ./llama-cli -hf gtest23/K3-Q4-GGUF
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 gtest23/K3-Q4-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf gtest23/K3-Q4-GGUF
Use Docker
docker model run hf.co/gtest23/K3-Q4-GGUF
- LM Studio
- Jan
- Ollama
How to use gtest23/K3-Q4-GGUF with Ollama:
ollama run hf.co/gtest23/K3-Q4-GGUF
- Unsloth Desktop
- Pi
How to use gtest23/K3-Q4-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf gtest23/K3-Q4-GGUF
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": "gtest23/K3-Q4-GGUF" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use gtest23/K3-Q4-GGUF with Docker Model Runner:
docker model run hf.co/gtest23/K3-Q4-GGUF
- Lemonade
How to use gtest23/K3-Q4-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull gtest23/K3-Q4-GGUF
Run and chat with the model
lemonade run user.K3-Q4-GGUF-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use gtest23/K3-Q4-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 gtest23/K3-Q4-GGUF
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 gtest23/K3-Q4-GGUF
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use gtest23/K3-Q4-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf gtest23/K3-Q4-GGUF
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 "gtest23/K3-Q4-GGUF" \ --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"
Insert [your mom so fat] joke here
Wow this model is huge. Nice work though, I love the outputs. Looking forward to running this sometime in 2035.
Maybe some brave soul will distill this down to ~70B?
Maybe some brave soul will distill this down to ~70B?
I thought the whole purpose was to make this big?
Wow this model is huge. Nice work though, I love the outputs. Looking forward to running this sometime in 2035.
Maybe some brave soul will distill this down to ~70B?
Is that a challenge?
I can cut it down for whatever task you want. Just tell me the category. You can see our other models on my profile
Is that a challenge?
I can cut it down for whatever task you want. Just tell me the category. You can see our other models on my profile
Cool, I assume your method is dataset calibration, similar to the REAP method?
Creative writing is the main focus here.
It's called Unstructued Sparsity.
Yes, we can do writing. Don't know the ETA though π
We must replicate this on Kimi K2.5 and name it Kimi K3.5
lol >_>
But I dont have nearly enough compute for that, just a 5090, good CPU, and loads storage.
Nobodexistsontheinternet should make it.
You probably can if you do it sharded
Hmmm
It looks like he just merged models, Im pruning them.