Instructions to use vidfom/Ltx-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use vidfom/Ltx-3 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="vidfom/Ltx-3", filename="ComfyUI/models/text_encoders/gemma-3-12b-it-qat-UD-Q4_K_XL.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 vidfom/Ltx-3 with llama.cpp:
Install from brew
brew install llama.cpp # 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
Install from WinGet (Windows)
winget install llama.cpp # 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
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 new
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
| import os | |
| from .utils import load_json_file, path_exists, save_json_file | |
| THIS_DIR = os.path.dirname(os.path.abspath(__file__)) | |
| USERDATA = os.path.join(THIS_DIR, '..', 'userdata') | |
| def read_userdata_file(rel_path: str): | |
| """Reads a file from the userdata directory.""" | |
| file_path = clean_path(rel_path) | |
| if path_exists(file_path): | |
| with open(file_path, 'r', encoding='UTF-8') as file: | |
| return file.read() | |
| return None | |
| def save_userdata_file(rel_path: str, content: str): | |
| """Saves a file from the userdata directory.""" | |
| file_path = clean_path(rel_path) | |
| with open(file_path, 'w+', encoding='UTF-8') as file: | |
| file.write(content) | |
| def delete_userdata_file(rel_path: str): | |
| """Deletes a file from the userdata directory.""" | |
| file_path = clean_path(rel_path) | |
| if os.path.isfile(file_path): | |
| os.remove(file_path) | |
| def read_userdata_json(rel_path: str): | |
| """Reads a json file from the userdata directory.""" | |
| file_path = clean_path(rel_path) | |
| return load_json_file(file_path) | |
| def save_userdata_json(rel_path: str, data: dict): | |
| """Saves a json file from the userdata directory.""" | |
| file_path = clean_path(rel_path) | |
| return save_json_file(file_path, data) | |
| def clean_path(rel_path: str): | |
| """Cleans a relative path by splitting on forward slash and os.path.joining.""" | |
| cleaned = USERDATA | |
| paths = rel_path.split('/') | |
| for path in paths: | |
| cleaned = os.path.join(cleaned, path) | |
| return cleaned | |