DeepSparse
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
- 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, that provides a unified interface for all models:
from langchain.llms import DeepSparse
llm = DeepSparse(model='zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base-none')
print(llm('def fib():'))
API Reference:
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)