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

​
LLMs

There exists a DeepSparse LLM wrapper, which you can access with: 
from langchain_community.llms import 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)
Edit the source of this page on GitHub.
Connect these docs programmatically to Claude, VSCode, and more via MCP for real-time answers.
⌘I