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
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
| .easy-dropdown, .easy-nested-dropdown { | |
| position: relative; | |
| box-sizing: border-box; | |
| background-color: #171717; | |
| box-shadow: 0 4px 4px rgba(255, 255, 255, .25); | |
| padding: 0; | |
| margin: 0; | |
| list-style: none; | |
| z-index: 1000; | |
| overflow: visible; | |
| max-height: fit-content; | |
| max-width: fit-content; | |
| } | |
| .easy-dropdown { | |
| position: absolute; | |
| border-radius: 0; | |
| } | |
| /* Style for final items */ | |
| .easy-dropdown li.item, .easy-nested-dropdown li.item { | |
| font-weight: normal; | |
| min-width: max-content; | |
| } | |
| /* Style for folders (parent items) */ | |
| .easy-dropdown li.folder, .easy-nested-dropdown li.folder { | |
| cursor: default; | |
| position: relative; | |
| border-right: 3px solid cyan; | |
| } | |
| .easy-dropdown li.folder::after, .easy-nested-dropdown li.folder::after { | |
| content: ">"; | |
| position: absolute; | |
| right: 2px; | |
| font-weight: normal; | |
| } | |
| .easy-dropdown li, .easy-nested-dropdown li { | |
| padding: 4px 10px; | |
| cursor: pointer; | |
| font-family: system-ui; | |
| font-size: 0.7rem; | |
| position: relative; | |
| } | |
| /* Style for nested dropdowns */ | |
| .easy-nested-dropdown { | |
| position: absolute; | |
| top: 0; | |
| left: 100%; | |
| margin: 0; | |
| border: none; | |
| display: none; | |
| } | |
| .easy-dropdown li.selected > .easy-nested-dropdown, | |
| .easy-nested-dropdown li.selected > .easy-nested-dropdown { | |
| display: block; | |
| border: none; | |
| } | |
| .easy-dropdown li.selected, | |
| .easy-nested-dropdown li.selected { | |
| background-color: #e5e5e5; | |
| border: none; | |
| } |