Fine-tuning OpenAI's GPT 3.5 for LangChain Agents
Fine-tuning for GPT-3.5 turbo is finally here! The latest update gives OpenAI users the ability to create their own custom GPT-3.5 model that has been tuned towards a particular dataset. This feature means we can teach GPT-3.5 the language and terminology of our niche domain (like finance or tech), reply in Italian, or always respond with JSON. Fine-tuning represents one of the many ways that we can take our LLMs to the next level of performance. In the past, we'd need to spend hours or even days tweaking prompts to get the behavior we need just to see it work _at best_ 80% of the time. Now, we can gather examples of our ideal conversations and feed that to GPT-3.5 directly, acting as built-in "guidelines" — replacing that frustrating prompt engineering process and in most cases producing much better results. In this video, we'll explore how to fine-tune our own LLMs with OpenAI's GPT 3.5 turbo. 📕 Article: https://www.pinecone.io/learn/fine-tune-gpt-3.5 📌 Code: https://github.com/pinecone-io/examples/blob/master/learn/generation/openai/fine-tuning/gpt-3.5-agent-training/00-fine-tuning.ipynb 🌲 Subscribe for Latest Articles and Videos: https://www.pinecone.io/newsletter-signup/ 👋🏼 AI Consulting: https://aurelio.ai 👾 Discord: https://discord.gg/c5QtDB9RAP Twitter: https://twitter.com/jamescalam LinkedIn: https://www.linkedin.com/in/jamescalam/ 00:00 Fine-tuning GPT 3.5 Turbo 01:44 Downloading the Training Data 02:57 Why Fine-tune an Agent 04:19 Training Data Format 06:10 Running OpenAI Fine-Tuning 09:04 Using Fine-Tuned GPT 3.5 in LangChain 11:01 Chatting with the Fine-Tuned Agent
