langchain/docs/versioned_docs/version-0.2.x/integrations/platforms/huggingface.mdx

# Hugging Face

All functionality related to the [Hugging Face Platform](https://huggingface.co/).

## Chat models

### Models from Hugging Face

We can use the `Hugging Face` LLM classes or directly use the `ChatHuggingFace` class.

We need to install several python packages.

```bash
pip install huggingface_hub
pip install transformers
```
See a [usage example](/docs/integrations/chat/huggingface).

```python
from langchain_community.chat_models.huggingface import ChatHuggingFace
```

## LLMs

### Hugging Face Local Pipelines

Hugging Face models can be run locally through the `HuggingFacePipeline` class.

We need to install `transformers` python package.

```bash
pip install transformers
```

See a [usage example](/docs/integrations/llms/huggingface_pipelines).

```python
from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
```

To use the OpenVINO backend in local pipeline wrapper, please install the optimum library and set HuggingFacePipeline's backend as `openvino`:

```bash
pip install --upgrade-strategy eager "optimum[openvino,nncf]"
```

See a [usage example](/docs/integrations/llms/huggingface_pipelines)

To export your model to the OpenVINO IR format with the CLI:

```bash
optimum-cli export openvino --model gpt2 ov_model
```

To apply [weight-only quantization](https://github.com/huggingface/optimum-intel?tab=readme-ov-file#export) when exporting your model.


## Embedding Models

### Hugging Face Hub

>The [Hugging Face Hub](https://huggingface.co/docs/hub/index) is a platform 
> with over 350k models, 75k datasets, and 150k demo apps (Spaces), all open source 
> and publicly available, in an online platform where people can easily 
> collaborate and build ML together. The Hub works as a central place where anyone 
> can explore, experiment, collaborate, and build technology with Machine Learning.

We need to install the `sentence_transformers` python package.

```bash
pip install sentence_transformers
```


#### HuggingFaceEmbeddings

See a [usage example](/docs/integrations/text_embedding/huggingfacehub).

```python
from langchain_community.embeddings import HuggingFaceEmbeddings
```
#### HuggingFaceInstructEmbeddings

See a [usage example](/docs/integrations/text_embedding/instruct_embeddings).

```python
from langchain_community.embeddings import HuggingFaceInstructEmbeddings
```

#### HuggingFaceBgeEmbeddings

>[BGE models on the HuggingFace](https://huggingface.co/BAAI/bge-large-en) are [the best open-source embedding models](https://huggingface.co/spaces/mteb/leaderboard).
>BGE model is created by the [Beijing Academy of Artificial Intelligence (BAAI)](https://en.wikipedia.org/wiki/Beijing_Academy_of_Artificial_Intelligence). `BAAI` is a private non-profit organization engaged in AI research and development.

See a [usage example](/docs/integrations/text_embedding/bge_huggingface).

```python
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
```

### Hugging Face Text Embeddings Inference (TEI)

>[Hugging Face Text Embeddings Inference (TEI)](https://huggingface.co/docs/text-generation-inference/index) is a toolkit for deploying and serving open-source
> text embeddings and sequence classification models. `TEI` enables high-performance extraction for the most popular models,
>including `FlagEmbedding`, `Ember`, `GTE` and `E5`.

We need to install `huggingface-hub` python package.

```bash
pip install huggingface-hub
```

See a [usage example](/docs/integrations/text_embedding/text_embeddings_inference).

```python
from langchain_community.embeddings import HuggingFaceHubEmbeddings
```


## Document Loaders

### Hugging Face dataset

>[Hugging Face Hub](https://huggingface.co/docs/hub/index) is home to over 75,000 
> [datasets](https://huggingface.co/docs/hub/index#datasets) in more than 100 languages 
> that can be used for a broad range of tasks across NLP, Computer Vision, and Audio.
> They used for a diverse range of tasks such as translation, automatic speech 
> recognition, and image classification.

We need to install `datasets` python package.

```bash
pip install datasets
```

See a [usage example](/docs/integrations/document_loaders/hugging_face_dataset).

```python
from langchain_community.document_loaders.hugging_face_dataset import HuggingFaceDatasetLoader
```



## Tools

### Hugging Face Hub Tools

>[Hugging Face Tools](https://huggingface.co/docs/transformers/v4.29.0/en/custom_tools) 
> support text I/O and are loaded using the `load_huggingface_tool` function.

We need to install several python packages.

```bash
pip install transformers huggingface_hub
```

See a [usage example](/docs/integrations/tools/huggingface_tools).

```python
from langchain.agents import load_huggingface_tool
```