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: 8,998 Bytes
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import uuid
from datetime import datetime
from typing import Any
from sqlalchemy import (
JSON,
BigInteger,
Boolean,
CheckConstraint,
DateTime,
ForeignKey,
Index,
Integer,
Numeric,
String,
Text,
)
from sqlalchemy.orm import Mapped, foreign, mapped_column, relationship
from app.assets.helpers import get_utc_now
from app.database.models import Base
class Asset(Base):
__tablename__ = "assets"
id: Mapped[str] = mapped_column(
String(36), primary_key=True, default=lambda: str(uuid.uuid4())
)
hash: Mapped[str | None] = mapped_column(String(256), nullable=True)
size_bytes: Mapped[int] = mapped_column(BigInteger, nullable=False, default=0)
mime_type: Mapped[str | None] = mapped_column(String(255))
created_at: Mapped[datetime] = mapped_column(
DateTime(timezone=False), nullable=False, default=get_utc_now
)
references: Mapped[list[AssetReference]] = relationship(
"AssetReference",
back_populates="asset",
primaryjoin=lambda: Asset.id == foreign(AssetReference.asset_id),
foreign_keys=lambda: [AssetReference.asset_id],
cascade="all,delete-orphan",
passive_deletes=True,
)
# preview_id on AssetReference is a self-referential FK to asset_references.id
__table_args__ = (
Index("uq_assets_hash", "hash", unique=True),
Index("ix_assets_mime_type", "mime_type"),
CheckConstraint("size_bytes >= 0", name="ck_assets_size_nonneg"),
)
def __repr__(self) -> str:
return f"<Asset id={self.id} hash={(self.hash or '')[:12]}>"
class AssetReference(Base):
"""Unified model combining file cache state and user-facing metadata.
Each row represents either:
- A filesystem reference (file_path is set) with cache state
- An API-created reference (file_path is NULL) without cache state
"""
__tablename__ = "asset_references"
id: Mapped[str] = mapped_column(
String(36), primary_key=True, default=lambda: str(uuid.uuid4())
)
asset_id: Mapped[str] = mapped_column(
String(36), ForeignKey("assets.id", ondelete="CASCADE"), nullable=False
)
# Cache state fields (from former AssetCacheState)
file_path: Mapped[str | None] = mapped_column(Text, nullable=True)
mtime_ns: Mapped[int | None] = mapped_column(BigInteger, nullable=True)
needs_verify: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
is_missing: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
enrichment_level: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
# Info fields (from former AssetInfo)
owner_id: Mapped[str] = mapped_column(String(128), nullable=False, default="")
name: Mapped[str] = mapped_column(String(512), nullable=False)
preview_id: Mapped[str | None] = mapped_column(
String(36), ForeignKey("asset_references.id", ondelete="SET NULL")
)
user_metadata: Mapped[dict[str, Any] | None] = mapped_column(
JSON(none_as_null=True)
)
system_metadata: Mapped[dict[str, Any] | None] = mapped_column(
JSON(none_as_null=True), nullable=True, default=None
)
job_id: Mapped[str | None] = mapped_column(String(36), nullable=True, default=None)
created_at: Mapped[datetime] = mapped_column(
DateTime(timezone=False), nullable=False, default=get_utc_now
)
updated_at: Mapped[datetime] = mapped_column(
DateTime(timezone=False), nullable=False, default=get_utc_now
)
last_access_time: Mapped[datetime] = mapped_column(
DateTime(timezone=False), nullable=False, default=get_utc_now
)
deleted_at: Mapped[datetime | None] = mapped_column(
DateTime(timezone=False), nullable=True, default=None
)
asset: Mapped[Asset] = relationship(
"Asset",
back_populates="references",
foreign_keys=[asset_id],
lazy="selectin",
)
preview_ref: Mapped[AssetReference | None] = relationship(
"AssetReference",
foreign_keys=[preview_id],
remote_side=lambda: [AssetReference.id],
)
metadata_entries: Mapped[list[AssetReferenceMeta]] = relationship(
back_populates="asset_reference",
cascade="all,delete-orphan",
passive_deletes=True,
)
tag_links: Mapped[list[AssetReferenceTag]] = relationship(
back_populates="asset_reference",
cascade="all,delete-orphan",
passive_deletes=True,
overlaps="tags,asset_references",
)
tags: Mapped[list[Tag]] = relationship(
secondary="asset_reference_tags",
back_populates="asset_references",
lazy="selectin",
viewonly=True,
overlaps="tag_links,asset_reference_links,asset_references,tag",
)
__table_args__ = (
Index("uq_asset_references_file_path", "file_path", unique=True),
Index("ix_asset_references_asset_id", "asset_id"),
Index("ix_asset_references_owner_id", "owner_id"),
Index("ix_asset_references_name", "name"),
Index("ix_asset_references_is_missing", "is_missing"),
Index("ix_asset_references_enrichment_level", "enrichment_level"),
Index("ix_asset_references_created_at", "created_at"),
Index("ix_asset_references_last_access_time", "last_access_time"),
Index("ix_asset_references_deleted_at", "deleted_at"),
Index("ix_asset_references_preview_id", "preview_id"),
Index("ix_asset_references_owner_name", "owner_id", "name"),
CheckConstraint(
"(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_ar_mtime_nonneg"
),
CheckConstraint(
"enrichment_level >= 0 AND enrichment_level <= 2",
name="ck_ar_enrichment_level_range",
),
)
def __repr__(self) -> str:
path_part = f" path={self.file_path!r}" if self.file_path else ""
return f"<AssetReference id={self.id} name={self.name!r}{path_part}>"
class AssetReferenceMeta(Base):
__tablename__ = "asset_reference_meta"
asset_reference_id: Mapped[str] = mapped_column(
String(36),
ForeignKey("asset_references.id", ondelete="CASCADE"),
primary_key=True,
)
key: Mapped[str] = mapped_column(String(256), primary_key=True)
ordinal: Mapped[int] = mapped_column(Integer, primary_key=True, default=0)
val_str: Mapped[str | None] = mapped_column(String(2048), nullable=True)
val_num: Mapped[float | None] = mapped_column(Numeric(38, 10), nullable=True)
val_bool: Mapped[bool | None] = mapped_column(Boolean, nullable=True)
val_json: Mapped[Any | None] = mapped_column(JSON(none_as_null=True), nullable=True)
asset_reference: Mapped[AssetReference] = relationship(
back_populates="metadata_entries"
)
__table_args__ = (
Index("ix_asset_reference_meta_key", "key"),
Index("ix_asset_reference_meta_key_val_str", "key", "val_str"),
Index("ix_asset_reference_meta_key_val_num", "key", "val_num"),
Index("ix_asset_reference_meta_key_val_bool", "key", "val_bool"),
CheckConstraint(
"val_str IS NOT NULL OR val_num IS NOT NULL OR val_bool IS NOT NULL OR val_json IS NOT NULL",
name="has_value",
),
)
class AssetReferenceTag(Base):
__tablename__ = "asset_reference_tags"
asset_reference_id: Mapped[str] = mapped_column(
String(36),
ForeignKey("asset_references.id", ondelete="CASCADE"),
primary_key=True,
)
tag_name: Mapped[str] = mapped_column(
String(512), ForeignKey("tags.name", ondelete="RESTRICT"), primary_key=True
)
origin: Mapped[str] = mapped_column(String(32), nullable=False, default="manual")
added_at: Mapped[datetime] = mapped_column(
DateTime(timezone=False), nullable=False, default=get_utc_now
)
asset_reference: Mapped[AssetReference] = relationship(back_populates="tag_links")
tag: Mapped[Tag] = relationship(back_populates="asset_reference_links")
__table_args__ = (
Index("ix_asset_reference_tags_tag_name", "tag_name"),
Index("ix_asset_reference_tags_asset_reference_id", "asset_reference_id"),
)
class Tag(Base):
__tablename__ = "tags"
name: Mapped[str] = mapped_column(String(512), primary_key=True)
tag_type: Mapped[str] = mapped_column(String(32), nullable=False, default="user")
asset_reference_links: Mapped[list[AssetReferenceTag]] = relationship(
back_populates="tag",
overlaps="asset_references,tags",
)
asset_references: Mapped[list[AssetReference]] = relationship(
secondary="asset_reference_tags",
back_populates="tags",
viewonly=True,
overlaps="asset_reference_links,tag_links,tags,asset_reference",
)
__table_args__ = (Index("ix_tags_tag_type", "tag_type"),)
def __repr__(self) -> str:
return f"<Tag {self.name}>"
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