langchain/docs/versioned_docs/version-0.2.x/integrations/providers/deepsparse.mdx

# DeepSparse

This page covers how to use the [DeepSparse](https://github.com/neuralmagic/deepsparse) inference runtime within LangChain.
It is broken into two parts: installation and setup, and then examples of DeepSparse usage.

## Installation and Setup

- Install the Python package with `pip install deepsparse`
- Choose a [SparseZoo model](https://sparsezoo.neuralmagic.com/?useCase=text_generation) or export a support model to ONNX [using Optimum](https://github.com/neuralmagic/notebooks/blob/main/notebooks/opt-text-generation-deepsparse-quickstart/OPT_Text_Generation_DeepSparse_Quickstart.ipynb)


## LLMs

There exists a DeepSparse LLM wrapper, which you can access with:

```python
from langchain_community.llms import DeepSparse
```

It provides a unified interface for all models:

```python
llm = DeepSparse(model='zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base-none')

print(llm('def fib():'))
```

Additional parameters can be passed using the `config` parameter:

```python
config = {'max_generated_tokens': 256}

llm = DeepSparse(model='zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base-none', config=config)
```