Discover a comprehensive 12-minute crash course on fine-tuning GPT-3.5-TURBO in seven steps. Learn when to use fine-tuning, how to prepare data, generate prompts, and validate data for the OpenAI API. Explore the costs and duration of fine-tuning, as well as techniques for saving, visualizing, and analyzing results. Gain insights on manual inspection and determining the success of fine-tuning efforts. Access valuable resources including the OpenAI Fine-tuning Guide, API documentation, and optional Python scripts for data preparation and analysis.
Fine-Tuning GPT-3.5-Turbo - A 7-Step Crash Course
Overview
Syllabus
Fine-tune gpt 3.5 in seven steps
Should I use fine-tuning?
When does fine-tuning work?
Step 1 - Preparing data for fine-tuning
How much data do I need for fine-tuning?
Fine-tuning on Twitter or blog data
Step 2 - Using rewinder to automatically generate prompts for fine-tuning
Step 3 - Prepare messages for OpenAI api
Step 4 - Data validation for fine-tuning
How much does fine-tuning cost with OpenAI?
Step 5 - Running a fine-tuning job
How long does fine-tuning take?
Step 6 - Saving and visualising fine-tuning results
Plotting and analysing fine-tuning results
Step 7 - Manual inspection of results
How do I know if fine-tuning worked?
Where to find scripts for fine-tuning with OpenAi
Taught by
Trelis Research
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