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DeepSparse | 🦜️🔗 LangChain

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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.

• Install the Python package with pip install deepsparse
• Choose a SparseZoo model or export a support model to ONNX using Optimum

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)

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