Gemini in Bullet Points:
- Gemini possesses a capacity of approximately 1.8 billion, depicted metaphorically as "Two large Chinchillas / One small Llama (overtoken)."
- Features a 32,000 token context window, enhanced with Model Quality Adjustments.
- Employs a tokenization method akin to the 'Flamingo' approach for processing interleaved inputs.
- Adopts an image output tokenization technique similar to that used in DALL-E 1.not support corresponding outputs.
- Its text performance benchmarks are comparable to those of GPT-4.
- Incorporates Reinforcement Learning with Human Feedback, augmented with principles of Constitutional AI.
Google #DeepMind has introduced #Gemini, a multidimensional revolutionary AI model designed to compete with OpenAI's GPT-4. It excels in major benchmarks. I haven't tested it yet, but I'll share my experience soon.
https://t.co/RalnXTKusV
What I really appreciate about @deno_land is its STD (standard library). It offers the essential tools for building #applications without depending on third-party modules.
https://t.co/dYvm9OXCwH
The future of cybersecurity with @elastic! Discover how they're leveraging GPT-4 for summarizing user sessions, enhancing security operations. A must-read for AI and security enthusiasts.
https://t.co/CjQ1CBTuiP