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
File size: 1,681 Bytes
9b6e316 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | #!/usr/bin/env bash
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"
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