What is PaLM 2 βPathways Language Model
I know all of you are eager to continue exploring the world of prompt engineering techniques. As promised, Part 2 of our series is in the works and will be released soon.
Today, I want to take a brief detour from our prompt engineering journey and shift our focus to a specific Large Language Model. So Letβs talk about the next generation of PaLM LLM, Palm 2.
PaLM
If you havenβt heard about PaLM, wait; donβtβGPTβ it! π PaLM, which stands for the Pathways Language Model, was introduced by Google AI to create a single model capable of generalizing across domains and tasks while maintaining efficiency. It is a powerful language model trained on an enormous dataset consisting of 540 billion parameters. PaLM has demonstrated remarkable capabilities in language comprehension, reasoning, and even code-related tasks, marking a significant leap forward in the field.
PaLM 2
PaLM 2 is the next generation of PaLM and it is trained on a 1.1 trillion parameters in a large dataset of text and code. As a result, PaLM 2 is able to handle more complex patterns. PaLM 2 was introduced to the public on May 10, 2023, at Google I/O 2023.
PaLM VS PaLM2
Here are some of the key differences between first PaLM and PaLM 2.
The following are some of PaLM 2βs key characteristics:
- It is built on substantial text and code datasets, including text from publications like books and articles and code from GitHub repositories.
- Compared to other language models, it has more parameters β 1.1 trillion.
- PaLM 2 is stronger at understanding the complexity of human language and can break down complicated tasks into simpler subtasks.
- Compared to its predecessor, PaLM 2, it was pre-trained on parallel multilingual text and on a significantly larger corpus of languages.
- Excels at popular programming languages like Python and JavaScript but can also produce specialized code in Prolog, Fortran, and Verilog.
Conclusion
PaLM 2 is the next generation of the Pathways Language Model, which Google AI introduced to create a single model capable of generalizing across domains and tasks while maintaining efficiency. It is built on 1.1 trillion parameters and trained on a larger dataset of text and code, making it able to handle more complex patterns. It is stronger at understanding the complexity of human language and can break down complicated tasks into simpler subtasks.
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