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: 2,502 Bytes
e00eceb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | 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}") |