Libraries llama-cpp-python How to use sinatras/qwen3-8b-split with llama-cpp-python:
# !pip install llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="sinatras/qwen3-8b-split",
filename="IQ3_XXS/Qwen3-8B-IQ3_XXS-00001-of-00003.gguf",
)
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
) Notebooks Google Colab Kaggle Local Apps llama.cpp How to use sinatras/qwen3-8b-split with llama.cpp:
Install from brew brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sinatras/qwen3-8b-split:IQ3_XXS
# Run inference directly in the terminal:
llama-cli -hf sinatras/qwen3-8b-split:IQ3_XXS Install from WinGet (Windows) winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sinatras/qwen3-8b-split:IQ3_XXS
# Run inference directly in the terminal:
llama-cli -hf sinatras/qwen3-8b-split:IQ3_XXS 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 sinatras/qwen3-8b-split:IQ3_XXS
# Run inference directly in the terminal:
./llama-cli -hf sinatras/qwen3-8b-split:IQ3_XXS 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 sinatras/qwen3-8b-split:IQ3_XXS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sinatras/qwen3-8b-split:IQ3_XXS Use Docker docker model run hf.co/sinatras/qwen3-8b-split:IQ3_XXS LM Studio Jan Ollama How to use sinatras/qwen3-8b-split with Ollama:
ollama run hf.co/sinatras/qwen3-8b-split:IQ3_XXS Unsloth Studio new How to use sinatras/qwen3-8b-split 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 sinatras/qwen3-8b-split 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 sinatras/qwen3-8b-split to start chatting Using HuggingFace Spaces for Unsloth # No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sinatras/qwen3-8b-split to start chatting Pi new How to use sinatras/qwen3-8b-split with Pi:
Start the llama.cpp server # Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf sinatras/qwen3-8b-split:IQ3_XXS 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": "sinatras/qwen3-8b-split:IQ3_XXS"
}
]
}
}
} Run Pi # Start Pi in your project directory:
pi Hermes Agent new How to use sinatras/qwen3-8b-split with Hermes Agent:
Start the llama.cpp server # Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf sinatras/qwen3-8b-split:IQ3_XXS 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 sinatras/qwen3-8b-split:IQ3_XXS Run Hermes hermes Docker Model Runner How to use sinatras/qwen3-8b-split with Docker Model Runner:
docker model run hf.co/sinatras/qwen3-8b-split:IQ3_XXS Lemonade How to use sinatras/qwen3-8b-split with Lemonade:
Pull the model # Download Lemonade from https://lemonade-server.ai/
lemonade pull sinatras/qwen3-8b-split:IQ3_XXS Run and chat with the model lemonade run user.qwen3-8b-split-IQ3_XXS List all available models lemonade list 
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="sinatras/qwen3-8b-split", filename="", )