Generalized models solve general problems. The real value comes from training a large language model (LLM) on your own data and finetuning it to deliver on your specific ML task. Now you can build your own custom LLM finetuned for your specific ML task in a few hours. The best part: you can do this all in just a few lines of code in a YAML file with declarative ML. This makes building LLMs fast, easy, and economical.
Watch this on-demand webinar to learn how you can get started building your own LLM.
Session topics include:
Session topics include:
- How declarative ML simplifies model building and training
- How to use off-the-shelf pretrained LLMs with Ludwig - the open-source declarative ML framework from Uber
- How to rapidly fine-tune an LLM on your data in < 10 lines of code with Ludwig using parameter efficient methods, deepspeed and Ray
