Instructions to use tarruda/Hy3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use tarruda/Hy3-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tarruda/Hy3-GGUF", filename="Hy3-MTP-Q8_0.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use tarruda/Hy3-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 tarruda/Hy3-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf tarruda/Hy3-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tarruda/Hy3-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf tarruda/Hy3-GGUF:Q8_0
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 tarruda/Hy3-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf tarruda/Hy3-GGUF:Q8_0
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 tarruda/Hy3-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf tarruda/Hy3-GGUF:Q8_0
Use Docker
docker model run hf.co/tarruda/Hy3-GGUF:Q8_0
- LM Studio
- Jan
- vLLM
How to use tarruda/Hy3-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tarruda/Hy3-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tarruda/Hy3-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tarruda/Hy3-GGUF:Q8_0
- Ollama
How to use tarruda/Hy3-GGUF with Ollama:
ollama run hf.co/tarruda/Hy3-GGUF:Q8_0
- Unsloth Studio
How to use tarruda/Hy3-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 tarruda/Hy3-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 tarruda/Hy3-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tarruda/Hy3-GGUF to start chatting
- Pi
How to use tarruda/Hy3-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tarruda/Hy3-GGUF:Q8_0
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": "tarruda/Hy3-GGUF:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use tarruda/Hy3-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 tarruda/Hy3-GGUF:Q8_0
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 tarruda/Hy3-GGUF:Q8_0
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use tarruda/Hy3-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tarruda/Hy3-GGUF:Q8_0
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 "tarruda/Hy3-GGUF:Q8_0" \ --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"
- Docker Model Runner
How to use tarruda/Hy3-GGUF with Docker Model Runner:
docker model run hf.co/tarruda/Hy3-GGUF:Q8_0
- Lemonade
How to use tarruda/Hy3-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tarruda/Hy3-GGUF:Q8_0
Run and chat with the model
lemonade run user.Hy3-GGUF-Q8_0
List all available models
lemonade list
| set -euo pipefail | |
| usage() { | |
| cat <<'EOF' | |
| Usage: split.sh <llama_cpp_dir> <input_gguf> <output_dir> [output_prefix] [--remove-input] | |
| Environment overrides: | |
| SPLIT_MAX_SIZE=50G Maximum size for each split file. | |
| Examples: | |
| ./scripts/split.sh ~/code/llama.cpp model.gguf ./Q5_K_S | |
| ./scripts/split.sh ~/code/llama.cpp model.gguf ./Q5_K_S model-Q5_K_S --remove-input | |
| EOF | |
| } | |
| if [ $# -lt 3 ]; then | |
| usage | |
| exit 1 | |
| fi | |
| LLAMA_CPP_DIR="$1" | |
| INPUT_GGUF="$2" | |
| OUTPUT_DIR="$3" | |
| OUTPUT_PREFIX_NAME= | |
| REMOVE_INPUT=false | |
| shift 3 | |
| while [ $# -gt 0 ]; do | |
| case "$1" in | |
| --remove-input) | |
| REMOVE_INPUT=true | |
| ;; | |
| --*) | |
| usage | |
| exit 1 | |
| ;; | |
| *) | |
| if [ -n "$OUTPUT_PREFIX_NAME" ]; then | |
| usage | |
| exit 1 | |
| fi | |
| OUTPUT_PREFIX_NAME="$1" | |
| ;; | |
| esac | |
| shift | |
| done | |
| if [ ! -d "$LLAMA_CPP_DIR" ]; then | |
| echo "Error: llama.cpp directory not found: $LLAMA_CPP_DIR" >&2 | |
| exit 1 | |
| fi | |
| if [ ! -e "$INPUT_GGUF" ]; then | |
| echo "Error: input GGUF not found: $INPUT_GGUF" >&2 | |
| exit 1 | |
| fi | |
| SPLIT_BIN="$LLAMA_CPP_DIR/build/bin/llama-gguf-split" | |
| if [ ! -x "$SPLIT_BIN" ]; then | |
| echo "Error: llama-gguf-split binary not found: $SPLIT_BIN" >&2 | |
| exit 1 | |
| fi | |
| if [ -z "$OUTPUT_PREFIX_NAME" ]; then | |
| input_basename="$(basename "$INPUT_GGUF")" | |
| OUTPUT_PREFIX_NAME="${input_basename%.gguf}" | |
| fi | |
| SPLIT_MAX_SIZE="${SPLIT_MAX_SIZE:-50G}" | |
| mkdir -p "$OUTPUT_DIR" | |
| OUTPUT_PREFIX="$OUTPUT_DIR/$OUTPUT_PREFIX_NAME" | |
| "$SPLIT_BIN" \ | |
| --split-max-size "$SPLIT_MAX_SIZE" \ | |
| --no-tensor-first-split \ | |
| "$INPUT_GGUF" \ | |
| "$OUTPUT_PREFIX" | |
| if [ "$REMOVE_INPUT" = true ]; then | |
| rm -f "$INPUT_GGUF" | |
| fi | |
| echo "Split complete. Output saved to: $OUTPUT_DIR" | |