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

# Instructor

The [instructor-embeddings](https://github.com/HKUNLP/instructor-embedding) library is another option, especially when running on a machine with a cuda-capable GPU. They are a good local alternative to OpenAI (see the [Massive Text Embedding Benchmark](https://huggingface.co/blog/mteb) rankings).  The embedding function requires the InstructorEmbedding package. To install it, run `pip install InstructorEmbedding`.

There are three models available. The default is `hkunlp/instructor-base`, and for better performance you can use `hkunlp/instructor-large` or `hkunlp/instructor-xl`. You can also specify whether to use `cpu` (default) or `cuda`. For example:

```python theme={null}
#uses base model and cpu
import chromadb.utils.embedding_functions as embedding_functions
ef = embedding_functions.InstructorEmbeddingFunction()
```

or

```python theme={null}
import chromadb.utils.embedding_functions as embedding_functions
ef = embedding_functions.InstructorEmbeddingFunction(
model_name="hkunlp/instructor-xl", device="cuda")
```

Keep in mind that the large and xl models are 1.5GB and 5GB respectively, and are best suited to running on a GPU.