Gemma 2 - New 9B and 27B You Can Try for Free

The video introduces Gemma 2, a language model available in 9 billion and 27 billion parameter versions, showcasing their competitive performance in various benchmarks. It discusses the technical aspects, training data, deployment options, and output quality of Gemma 2, highlighting its versatility and effectiveness in tasks like creative writing and generating detailed responses.

In the video, Gemma 2 is introduced with two models: a 9 billion parameter version and a 27 billion parameter version. The 9 billion model is found to outperform Llama-3 8 billion in various benchmarks, showing competitive performance. Additionally, the 27 billion model is claimed by Google to be able to compete with models of around 70 billion parameters. It is highlighted that the models are suitable for different GPU configurations, with the 9 billion model fitting on smaller GPUs like the L4 or T4, while the 27 billion model requires more powerful GPUs like Nvidia H100 or A100 with 80GB of VRAM.

The video discusses the technical aspects of Gemma 2, including its training data, post-training processes, and model merging techniques. The 27 billion parameter model was trained on 13 trillion tokens, while the 9 billion model was trained on 8 trillion tokens. The models have shown impressive performance in various tasks, with the 9 billion model proving competitive against Llama-3 8 billion and the 27 billion model setting new benchmarks by outperforming models like Llama-3 70 billion in certain scenarios, such as in the LMSys chatbot arena.

The video delves into the practical aspects of using Gemma 2, discussing its commercial license and potential deployment options on Google Cloud and Vertex AI. It also mentions ongoing efforts to open source some of the text watermarking technology associated with Gemma 2. The video provides insights into the models’ outputs, showcasing the detailed and creative responses generated by both the 9 billion and 27 billion parameter versions. The models are seen to excel in tasks like creative writing and provide informative and engaging responses.

Comparisons are drawn between the 9 billion and 27 billion models in terms of their outputs, with the 27 billion model offering more in-depth and creative responses, including the use of emojis and personalized content. Examples of model performance in tasks like generating poems, critical analyses, and answering questions are discussed, highlighting the models’ strengths and areas for improvement. The video also mentions the availability of Gemma 2 on AI Studio for testing purposes, allowing users to explore the capabilities of the 27 billion model before potentially running it locally.

Overall, the video provides a comprehensive overview of Gemma 2, discussing its performance, technical details, practical applications, and output quality. The models are shown to be versatile and effective in various tasks, showcasing their capabilities in generating detailed and creative responses. The comparison between the 9 billion and 27 billion models reveals differences in output style and depth, with both models offering unique strengths. The availability of Gemma 2 on AI Studio enables users to test the model’s capabilities before deciding on local deployment, making it accessible for experimentation and evaluation.