repo_description: [ACL2023] We introduce LLM-Blender, an innovative ensembling framework to attain consistently superior performance by leveraging the diverse strengths of multiple open-source LLMs. LLM-Blender cut the weaknesses through ranking and integrate the strengths through fusing generation to enhance the capability of LLMs.
repo_topics: ensamble-methods large-language-models llm natural-language-processing
code: from setuptools import setup, find_packages
  # read the contents of your README file
  from pathlib import Path
  this_directory = Path(__file__).parent
  long_description = (this_directory / "README.md").read_text()
  
  setup(
      name='llm_blender',
      version='0.0.2',
      description='LLM-Blender, an innovative ensembling framework to attain consistently superior performance by leveraging the diverse strengths and weaknesses of multiple open-source large language models (LLMs). LLM-Blender cut the weaknesses through ranking and integrate the strengths through fusing generation to enhance the capability of LLMs.',
      long_description=long_description,
      long_description_content_type='text/markdown',
      author='Dongfu Jiang',
      author_email='[REDACTED_EMAIL]',
      packages=find_packages(),
      url='https://yuchenlin.xyz/LLM-Blender/',
      install_requires=[
          'transformers',
          'torch',
          'numpy',
          'accelerate',
          'safetensors',
          'dataclasses-json',
          'sentencepiece',
          'protobuf',
      ],
      extras_require={
          "example": [
              'datasets',
              'scipy',
              'jupyter'
          ],
          "train": [
              'datasets',
              'bitsandbytes',
              'deepspeed',
              'wandb',
          ],
          "data": [
              'datasets',
              'openai',
              'peft',
              'fschat',
          ],
          "eval": [
              'datasets',
              'pycocoevalcap',
              'spacy',
              'prettytable',
              'evaluate',
              'bert_score',
              'tabulate',
              'scipy',
              'nltk',
              'scikit-learn',
              'sacrebleu',
              'rouge_score',
          ],
      },
  )
  