Published
An overview of why Google, Cohere, Midjourney, and other leaders use JAX for GenAI, including the role of JAX in the development of Gemma. Learn from a demonstration of LoRA fine-tuning with Flax on a responsibly curated dataset. You could use JAX in the Kaggle Gemma competition!
Subscribe to Google for Developers → https://goo.gle/developers
#Gemma #GemmaDeveloperDay
Subscribe to Google for Developers → https://goo.gle/developers
#Gemma #GemmaDeveloperDay
- Category
- Project
- Tags
- Google, developers, pr_pr: Core DevRel DEI;
Sign in or sign up to post comments.
Be the first to comment
Up Next
-
06:20
Demo: Multi-modal Gemma: PaliGemma with Keras
-
What is Gemma Scope?
-
26:21
Hands-on introduction to Gemma
-
07:20
Demo: Unleashing Gemma in production with Hugging Face Text Generation Inference (TGI)
-
Gemma Scope demo with Neuronpedia
-
08:12
Demo: Gemma 2 architecture: JAX, Flax, and more
-
11:34
Demo: Taking Gemma from prototype to production faster with Vertex AI
-
08:43
Demo: Post-training research with Gemma
-
10:07
Demo: Building a Gemma chatbot with Keras
-
12:21
Demo: Optimizing Gemma inference on NVIDIA GPUs with TensorRT-LLM
-
07:12
Demo: Gemma on-device with MediaPipe
-
14:29
What's new in Gemma 2
-
06:37
Demo: Teach Gemma to work in YOUR language
-
Biggest updates in Gemma 2
-
09:37
Demo: Using Gemma with the Hugging Face ecosystem
-
00:15
Run AI locally, with Gemma!
-
07:12
Demo: Gemma 2 2B on a Jetson Orin Nano
-
08:52
Demo: Gemma data, training, and the path to improvement
-
11:37
Demo: Rapid prototyping with Gemma and Llama.cpp
-
08:34
Demo: Deploying Gemma at dataflow scale
-
07:24
Demo: Gemma Scope: Sparse autoencoders on Gemma 2
-
07:57
Demo: Gemma on-device with MediaPipe and TensorFlow Lite
Loading...