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
| from collections import deque | |
| from datetime import datetime | |
| import io | |
| import logging | |
| import sys | |
| import threading | |
| logs = None | |
| stdout_interceptor = None | |
| stderr_interceptor = None | |
| class LogInterceptor(io.TextIOWrapper): | |
| def __init__(self, stream, *args, **kwargs): | |
| buffer = stream.buffer | |
| encoding = stream.encoding | |
| super().__init__(buffer, *args, **kwargs, encoding=encoding, line_buffering=stream.line_buffering) | |
| self._lock = threading.Lock() | |
| self._flush_callbacks = [] | |
| self._logs_since_flush = [] | |
| def write(self, data): | |
| entry = {"t": datetime.now().isoformat(), "m": data} | |
| with self._lock: | |
| self._logs_since_flush.append(entry) | |
| # Simple handling for cr to overwrite the last output if it isnt a full line | |
| # else logs just get full of progress messages | |
| if isinstance(data, str) and data.startswith("\r") and not logs[-1]["m"].endswith("\n"): | |
| logs.pop() | |
| logs.append(entry) | |
| super().write(data) | |
| def flush(self): | |
| super().flush() | |
| for cb in self._flush_callbacks: | |
| cb(self._logs_since_flush) | |
| self._logs_since_flush = [] | |
| def on_flush(self, callback): | |
| self._flush_callbacks.append(callback) | |
| def get_logs(): | |
| return logs | |
| def on_flush(callback): | |
| if stdout_interceptor is not None: | |
| stdout_interceptor.on_flush(callback) | |
| if stderr_interceptor is not None: | |
| stderr_interceptor.on_flush(callback) | |
| def setup_logger(log_level: str = 'INFO', capacity: int = 300, use_stdout: bool = False): | |
| global logs | |
| if logs: | |
| return | |
| # Override output streams and log to buffer | |
| logs = deque(maxlen=capacity) | |
| global stdout_interceptor | |
| global stderr_interceptor | |
| stdout_interceptor = sys.stdout = LogInterceptor(sys.stdout) | |
| stderr_interceptor = sys.stderr = LogInterceptor(sys.stderr) | |
| # Setup default global logger | |
| logger = logging.getLogger() | |
| logger.setLevel(log_level) | |
| stream_handler = logging.StreamHandler() | |
| stream_handler.setFormatter(logging.Formatter("%(message)s")) | |
| if use_stdout: | |
| # Only errors and critical to stderr | |
| stream_handler.addFilter(lambda record: not record.levelno < logging.ERROR) | |
| # Lesser to stdout | |
| stdout_handler = logging.StreamHandler(sys.stdout) | |
| stdout_handler.setFormatter(logging.Formatter("%(message)s")) | |
| stdout_handler.addFilter(lambda record: record.levelno < logging.ERROR) | |
| logger.addHandler(stdout_handler) | |
| logger.addHandler(stream_handler) | |
| STARTUP_WARNINGS = [] | |
| def log_startup_warning(msg): | |
| logging.warning(msg) | |
| STARTUP_WARNINGS.append(msg) | |
| def print_startup_warnings(): | |
| for s in STARTUP_WARNINGS: | |
| logging.warning(s) | |
| STARTUP_WARNINGS.clear() | |