> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lancedb.com/llms.txt
> Use this file to discover all available pages before exploring further.

# HuggingFace

We offer support for all HuggingFace models (which can be loaded via [transformers](https://huggingface.co/docs/transformers/en/index) library). The default model is `colbert-ir/colbertv2.0` which also has its own special callout - `registry.get("colbert")`. Some HuggingFace models might require custom models defined on the HuggingFace Hub in their own modeling files. You may enable this by setting `trust_remote_code=True`. This option should only be set to True for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. Pin `revision` to a commit hash, and note that tables using such a model need it approved with `get_registry().allow_remote_code(...)` when opened in a new process. See [Trusting remote code and custom endpoints](https://docs.lancedb.com/oss/v0-41-0/embedding.md#trusting-remote-code-and-custom-endpoints).

Example usage:

```python Python icon="python" theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
import tempfile
from pathlib import Path

import lancedb
import pandas as pd
from lancedb.embeddings import get_registry
from lancedb.pydantic import LanceModel, Vector

db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "huggingface-demo"))
model = get_registry().get("huggingface").create(name="facebook/bart-base")

class Words(LanceModel):
    text: str = model.SourceField()
    vector: Vector(model.ndims()) = model.VectorField()

df = pd.DataFrame({"text": ["hi hello sayonara", "goodbye world"]})
table = db.create_table("greets", schema=Words)
table.add(df)
query = "old greeting"
actual = table.search(query).limit(1).to_pydantic(Words)[0]
print(actual.text)
```


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