Sean Sheng - Productionizing Open Source LLMs | PyData Global 2023

Key Takeaways

• Open source LLMs can be productionized with Bento ML and OpenLLM
• LLMs are the future of AI applications
• Three advantages of using open source LLMs: cost efficiency, data privacy, and control
• LLMs are Large Language Models due to their large number of parameters
• Fine-tuning models using techniques like LoRa and training them with large context sizes can improve performance
• Traditional static batching is not suitable for LLMs; continuous batching is a better solution
• OpenLLM integrates with the Hugging Face model repository and supports various model runners
• Running LLMs on multiple GPUs or using spot instances can improve scalability and cost efficiency
• Fine-tuning models can improve performance, with some models outperforming GPT-4
• Open source LLMs can be used for various applications, such as image generation, segmentation, and chatbots
• Bento ML is an open source project that helps productionize LLMs
• OpenLLM can be used for various applications, such as text generation, summarization, and translation