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 itertools | |
| from typing import Optional | |
| class TaggedCache: | |
| def __init__(self, tag_settings: Optional[dict]=None): | |
| self._tag_settings = tag_settings or {} # tag cache size | |
| self._data = {} | |
| def __getitem__(self, key): | |
| for tag_data in self._data.values(): | |
| if key in tag_data: | |
| return tag_data[key] | |
| raise KeyError(f'Key `{key}` does not exist') | |
| def __setitem__(self, key, value: tuple): | |
| # value: (tag: str, (islist: bool, data: *)) | |
| # if key already exists, pop old value | |
| for tag_data in self._data.values(): | |
| if key in tag_data: | |
| tag_data.pop(key, None) | |
| break | |
| tag = value[0] | |
| if tag not in self._data: | |
| try: | |
| from cachetools import LRUCache | |
| default_size = 20 | |
| if 'ckpt' in tag: | |
| default_size = 5 | |
| elif tag in ['latent', 'image']: | |
| default_size = 100 | |
| self._data[tag] = LRUCache(maxsize=self._tag_settings.get(tag, default_size)) | |
| except (ImportError, ModuleNotFoundError): | |
| # TODO: implement a simple lru dict | |
| self._data[tag] = {} | |
| self._data[tag][key] = value | |
| def __delitem__(self, key): | |
| for tag_data in self._data.values(): | |
| if key in tag_data: | |
| del tag_data[key] | |
| return | |
| raise KeyError(f'Key `{key}` does not exist') | |
| def __contains__(self, key): | |
| return any(key in tag_data for tag_data in self._data.values()) | |
| def items(self): | |
| yield from itertools.chain(*map(lambda x :x.items(), self._data.values())) | |
| def get(self, key, default=None): | |
| """D.get(k[,d]) -> D[k] if k in D, else d. d defaults to None.""" | |
| for tag_data in self._data.values(): | |
| if key in tag_data: | |
| return tag_data[key] | |
| return default | |
| def clear(self): | |
| # clear all cache | |
| self._data = {} | |
| cache_settings = {} | |
| cache = TaggedCache(cache_settings) | |
| cache_count = {} | |
| def update_cache(k, tag, v): | |
| cache[k] = (tag, v) | |
| cnt = cache_count.get(k) | |
| if cnt is None: | |
| cnt = 0 | |
| cache_count[k] = cnt | |
| else: | |
| cache_count[k] += 1 | |
| def remove_cache(key): | |
| global cache | |
| if key == '*': | |
| cache = TaggedCache(cache_settings) | |
| elif key in cache: | |
| del cache[key] | |
| else: | |
| print(f"invalid {key}") |