Instructions to use AGofficial/MyName_RPG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use AGofficial/MyName_RPG with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="AGofficial/MyName_RPG", filename="llm/dolphin.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use AGofficial/MyName_RPG 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 AGofficial/MyName_RPG # Run inference directly in the terminal: llama cli -hf AGofficial/MyName_RPG
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf AGofficial/MyName_RPG # Run inference directly in the terminal: llama cli -hf AGofficial/MyName_RPG
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 AGofficial/MyName_RPG # Run inference directly in the terminal: ./llama-cli -hf AGofficial/MyName_RPG
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 AGofficial/MyName_RPG # Run inference directly in the terminal: ./build/bin/llama-cli -hf AGofficial/MyName_RPG
Use Docker
docker model run hf.co/AGofficial/MyName_RPG
- LM Studio
- Jan
- Ollama
How to use AGofficial/MyName_RPG with Ollama:
ollama run hf.co/AGofficial/MyName_RPG
- Unsloth Studio
How to use AGofficial/MyName_RPG 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 AGofficial/MyName_RPG 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 AGofficial/MyName_RPG to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for AGofficial/MyName_RPG to start chatting
- Atomic Chat new
- Docker Model Runner
How to use AGofficial/MyName_RPG with Docker Model Runner:
docker model run hf.co/AGofficial/MyName_RPG
- Lemonade
How to use AGofficial/MyName_RPG with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AGofficial/MyName_RPG
Run and chat with the model
lemonade run user.MyName_RPG-{{QUANT_TAG}}List all available models
lemonade list
| """Asset loading helpers for backgrounds and character sprites.""" | |
| from __future__ import annotations | |
| from pathlib import Path | |
| from typing import Iterable | |
| from PIL import Image | |
| class AssetNotFoundError(FileNotFoundError): | |
| """Raised when an asset cannot be resolved from the project folders.""" | |
| class AssetManager: | |
| def __init__(self, project_root: str | Path) -> None: | |
| self.project_root = Path(project_root).resolve() | |
| self.characters_dir = self.project_root / "characters" | |
| self.backgrounds_dir = self.project_root / "backgrounds" | |
| def load_background(self, asset: str | Path) -> Image.Image: | |
| path = self.resolve(asset, (self.backgrounds_dir, self.project_root)) | |
| return Image.open(path).convert("RGBA") | |
| def load_character(self, asset: str | Path) -> Image.Image: | |
| path = self.resolve(asset, (self.characters_dir, self.project_root)) | |
| image = Image.open(path).convert("RGBA") | |
| return self._crop_alpha(image) | |
| def resolve(self, asset: str | Path, roots: Iterable[Path]) -> Path: | |
| asset_path = Path(asset) | |
| if asset_path.is_absolute() and asset_path.exists(): | |
| return asset_path | |
| candidates: list[Path] = [] | |
| for root in roots: | |
| candidates.append(root / asset_path) | |
| if asset_path.suffix: | |
| continue | |
| candidates.extend(sorted(root.glob(f"{asset_path.name}.*"))) | |
| for candidate in candidates: | |
| if candidate.exists() and candidate.is_file(): | |
| return candidate.resolve() | |
| searched = ", ".join(str(root) for root in roots) | |
| raise AssetNotFoundError(f"Could not find asset {asset!r} in {searched}.") | |
| def _crop_alpha(image: Image.Image) -> Image.Image: | |
| bbox = image.getbbox() | |
| if bbox is None: | |
| return image | |
| return image.crop(bbox) | |