Instructions to use vidfom/Ltx-3 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 vidfom/Ltx-3 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 vidfom/Ltx-3:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf vidfom/Ltx-3:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf vidfom/Ltx-3:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf vidfom/Ltx-3:UD-Q4_K_XL
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 vidfom/Ltx-3:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf vidfom/Ltx-3:UD-Q4_K_XL
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 vidfom/Ltx-3:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf vidfom/Ltx-3:UD-Q4_K_XL
Use Docker
docker model run hf.co/vidfom/Ltx-3:UD-Q4_K_XL
- LM Studio
- Jan
- Ollama
How to use vidfom/Ltx-3 with Ollama:
ollama run hf.co/vidfom/Ltx-3:UD-Q4_K_XL
- Unsloth Studio
How to use vidfom/Ltx-3 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 vidfom/Ltx-3 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 vidfom/Ltx-3 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for vidfom/Ltx-3 to start chatting
- Docker Model Runner
How to use vidfom/Ltx-3 with Docker Model Runner:
docker model run hf.co/vidfom/Ltx-3:UD-Q4_K_XL
- Lemonade
How to use vidfom/Ltx-3 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull vidfom/Ltx-3:UD-Q4_K_XL
Run and chat with the model
lemonade run user.Ltx-3-UD-Q4_K_XL
List all available models
lemonade list
- Atomic Chat
| from typing import Any | |
| from datetime import datetime | |
| from sqlalchemy import MetaData | |
| from sqlalchemy.orm import DeclarativeBase | |
| NAMING_CONVENTION = { | |
| "ix": "ix_%(table_name)s_%(column_0_N_name)s", | |
| "uq": "uq_%(table_name)s_%(column_0_N_name)s", | |
| "ck": "ck_%(table_name)s_%(constraint_name)s", | |
| "fk": "fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s", | |
| "pk": "pk_%(table_name)s", | |
| } | |
| class Base(DeclarativeBase): | |
| metadata = MetaData(naming_convention=NAMING_CONVENTION) | |
| def to_dict(obj: Any, include_none: bool = False) -> dict[str, Any]: | |
| fields = obj.__table__.columns.keys() | |
| out: dict[str, Any] = {} | |
| for field in fields: | |
| val = getattr(obj, field) | |
| if val is None and not include_none: | |
| continue | |
| if isinstance(val, datetime): | |
| out[field] = val.isoformat() | |
| else: | |
| out[field] = val | |
| return out | |
| # TODO: Define models here | |