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 pytest | |
| from typing import List | |
| from api_server.utils.file_operations import FileSystemOperations, FileSystemItem, is_file_info | |
| def temp_directory(tmp_path): | |
| # Create a temporary directory structure | |
| dir1 = tmp_path / "dir1" | |
| dir2 = tmp_path / "dir2" | |
| dir1.mkdir() | |
| dir2.mkdir() | |
| (dir1 / "file1.txt").write_text("content1") | |
| (dir2 / "file2.txt").write_text("content2") | |
| (tmp_path / "file3.txt").write_text("content3") | |
| return tmp_path | |
| def test_walk_directory(temp_directory): | |
| result: List[FileSystemItem] = FileSystemOperations.walk_directory(str(temp_directory)) | |
| assert len(result) == 5 # 2 directories and 3 files | |
| files = [item for item in result if item['type'] == 'file'] | |
| dirs = [item for item in result if item['type'] == 'directory'] | |
| assert len(files) == 3 | |
| assert len(dirs) == 2 | |
| file_names = {file['name'] for file in files} | |
| assert file_names == {'file1.txt', 'file2.txt', 'file3.txt'} | |
| dir_names = {dir['name'] for dir in dirs} | |
| assert dir_names == {'dir1', 'dir2'} | |
| def test_walk_directory_empty(tmp_path): | |
| result = FileSystemOperations.walk_directory(str(tmp_path)) | |
| assert len(result) == 0 | |
| def test_walk_directory_file_size(temp_directory): | |
| result: List[FileSystemItem] = FileSystemOperations.walk_directory(str(temp_directory)) | |
| files = [item for item in result if is_file_info(item)] | |
| for file in files: | |
| assert file['size'] > 0 # Assuming all files have some content | |