00:06 Discussion on tuning foundational models and its complexities 02:10 Introduction to tuning large language models 06:08 Different models like T5, Bison, Chat Bison, and DSS are available for generative AI. 08:00 Advantage of fine-tuning for model customization and personalization. 11:48 Tuning involves adding adaptation or optimizing layer activations 13:44 Adapted tuning allows model optimization without additional costs 17:30 Tuning smaller models from teacher model rationale 19:04 Tuning foundation models in Vertex Generative AI Studio 22:17 Fine-tuning with reward function and feedback for model refinement 23:50 Developing policy for language model responses 27:11 Adapter tuning is essential for tuning foundational models in Vertex AI Studio. 29:00 RLM helps optimize model performance with human feedback 32:45 Tuning foundation models involves pre-end LLM, fine-tuning, and adapter tuning. 34:42 Creating and tuning adapter models for task-specific datasets. 38:32 Key considerations for fine-tuning AI models 40:18 Using text to SQL with Code Bon model to generate queries for big query engine. 44:10 Model stored in GCB parameter decides the path, input data sets uploaded for tuning 46:15 Google Cloud stores artifacts related to the model in GCS bucket. 50:07 Fine-tuning and embedding concepts explained 51:53 Feedback for upcoming sessions and recommendations for customization 55:29 The decision to fine-tune or tweak the prompts depends on business stability and specific needs. 57:14 Incremental tuning recommended for new data and huge data volumes. 1:00:55 Tuning foundation models and using XAI with Google Cloud.
@35:38 json format is confusing. First example has 'Input_text' and 'output_text' only but second example has 'context' as well. What is the right format? is this intentional?
00:06 Discussion on tuning foundational models and its complexities
02:10 Introduction to tuning large language models
06:08 Different models like T5, Bison, Chat Bison, and DSS are available for generative AI.
08:00 Advantage of fine-tuning for model customization and personalization.
11:48 Tuning involves adding adaptation or optimizing layer activations
13:44 Adapted tuning allows model optimization without additional costs
17:30 Tuning smaller models from teacher model rationale
19:04 Tuning foundation models in Vertex Generative AI Studio
22:17 Fine-tuning with reward function and feedback for model refinement
23:50 Developing policy for language model responses
27:11 Adapter tuning is essential for tuning foundational models in Vertex AI Studio.
29:00 RLM helps optimize model performance with human feedback
32:45 Tuning foundation models involves pre-end LLM, fine-tuning, and adapter tuning.
34:42 Creating and tuning adapter models for task-specific datasets.
38:32 Key considerations for fine-tuning AI models
40:18 Using text to SQL with Code Bon model to generate queries for big query engine.
44:10 Model stored in GCB parameter decides the path, input data sets uploaded for tuning
46:15 Google Cloud stores artifacts related to the model in GCS bucket.
50:07 Fine-tuning and embedding concepts explained
51:53 Feedback for upcoming sessions and recommendations for customization
55:29 The decision to fine-tune or tweak the prompts depends on business stability and specific needs.
57:14 Incremental tuning recommended for new data and huge data volumes.
1:00:55 Tuning foundation models and using XAI with Google Cloud.
@35:38 json format is confusing. First example has 'Input_text' and 'output_text' only but second example has 'context' as well. What is the right format? is this intentional?
Are the slides from this talk available anywhere?
I thought about getting a cert in google and thought Microsoft, but I chose ibm because the future isn't one type of machine learning it's all.
Very confusing presentation and discussion. They are talking about too many things quickly.
Promo_SM