Skip to main content


This page covers how to use the DeepSparse inference runtime within LangChain. It is broken into two parts: installation and setup, and then examples of DeepSparse usage.

Installation and Setup​


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

from langchain_community.llms import DeepSparse
API Reference:DeepSparse

It provides a unified interface for all models:

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

print(llm.invoke('def fib():'))

Additional parameters can be passed using the config parameter:

config = {'max_generated_tokens': 256}

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

Was this page helpful?

You can also leave detailed feedback on GitHub.