AdrianBZG/LLM-distributed-finetune
Tune efficiently any LLM model from HuggingFace using distributed training (multiple GPU) and DeepSpeed. Uses Ray AIR to orchestrate the training on multiple AWS GPU instances
This project helps machine learning engineers efficiently customize large language models (LLMs) like FALCON-7B for specific tasks or languages. You provide a pre-trained HuggingFace LLM and a dataset of examples, and it outputs a fine-tuned model ready for deployment. This is for ML engineers working with powerful GPU clusters on cloud platforms like AWS.
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Use this if you need to quickly and efficiently fine-tune a HuggingFace Large Language Model using multiple GPUs and distributed training on AWS.
Not ideal if you do not have access to AWS GPU instances or prefer to fine-tune models on a single machine.
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Language
Python
License
MIT
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Last pushed
Jun 20, 2023
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