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
File size: 2,373 Bytes
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def MakeSmartType(t):
if isinstance(t, str):
return SmartType(t)
return t
class SmartType(str):
def __ne__(self, other):
if self == "*" or other == "*":
return False
selfset = set(self.split(','))
otherset = set(other.split(','))
return not selfset.issubset(otherset)
def VariantSupport():
def decorator(cls):
if hasattr(cls, "INPUT_TYPES"):
old_input_types = getattr(cls, "INPUT_TYPES")
def new_input_types(*args, **kwargs):
types = old_input_types(*args, **kwargs)
for category in ["required", "optional"]:
if category not in types:
continue
for key, value in types[category].items():
if isinstance(value, tuple):
types[category][key] = (MakeSmartType(value[0]),) + value[1:]
return types
setattr(cls, "INPUT_TYPES", new_input_types)
if hasattr(cls, "RETURN_TYPES"):
old_return_types = cls.RETURN_TYPES
setattr(cls, "RETURN_TYPES", tuple(MakeSmartType(x) for x in old_return_types))
if hasattr(cls, "VALIDATE_INPUTS"):
# Reflection is used to determine what the function signature is, so we can't just change the function signature
raise NotImplementedError("VariantSupport does not support VALIDATE_INPUTS yet")
else:
def validate_inputs(input_types):
inputs = cls.INPUT_TYPES()
for key, value in input_types.items():
if isinstance(value, SmartType):
continue
if "required" in inputs and key in inputs["required"]:
expected_type = inputs["required"][key][0]
elif "optional" in inputs and key in inputs["optional"]:
expected_type = inputs["optional"][key][0]
else:
expected_type = None
if expected_type is not None and MakeSmartType(value) != expected_type:
return f"Invalid type of {key}: {value} (expected {expected_type})"
return True
setattr(cls, "VALIDATE_INPUTS", validate_inputs)
return cls
return decorator
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