Are you an innovative new player in the Large Language Model space and want to make your own fine-tuned version of the LLaMA models? Well, if you are a researcher looking for the correct variant of LLaMA 2 to build your app on or one trying to implement one of the existing variants various other research facilities have built upon the model, we are here to help!
One may note that LLaMA 2 models don’t come out of the box as strong as the GPT models, however, that’s the entire point! Give the people a model that can be tinkered with readily available hardware and fit it to your use case and pipeline. Even when you remove the OSS community versions of LLaMA 2 models, we are still left with Three incredible models that were initially released by Meta.
In this blog, we will explore the original versions of the legacy model and try to put into perspective how well or accessible these models are!
Trained over 184,000 GPU Hours, this model is the smallest and most compact version of the LLaMA 2 architecture. The model, however, needing more world knowledge compared to the other two models, performs almost equally well regarding text comprehension. This ability allows the model to be deployed and trained for purposes like enterprise chatbots, training on your custom data, and utilizing the comprehension power of the model.
Trained over 368,000 GPU Hours, this model serves as the middle ground between size, comprehension, and world knowledge, trained on a larger dataset, the model possesses more knowledge about general things than the smaller model, making it more suitable more web applications and with further context boost using open-source models to be used for advanced content writing and SEO based studies.
Trained for over 1,720,000 GPU Hours, the model offers the maximum world knowledge and comprehension. With its vast number of parameters, the model provides the backbone for scalable LLM architectures, making it ideal for education and e-commerce applications. The model can be fine-tuned to fit multiple closed-source applications as well.
When comparing Llama-2 to its peers, one should mention three main competitors: Llama-1, open-source models, and closed-source models. Of these, Llama-2 stands out as an open-source alternative.
As anticipated, Llama-2 is larger and more advanced than its predecessor. It has a more complex architecture and surpasses Llama-1 in benchmark performance. Llama-2 boasts more parameters, a longer context length, a more extensive training set, and Grouped Query Attention (GQA) for improved inference scalability.
We hope you were able to gauge what LLaMA 2 model fits your use case, and we were able to provide you with the arsenal to train and make your own fine-tuned LLM. The evolution of Llama-2 models, from the 7B to the 13B and finally the 70B variant, showcases the continuous advancements in natural language processing. Each iteration surpasses its predecessor, with the 70B variant as the epitome of performance.
These models have revolutionized the field, enabling more accurate and contextually aware language processing applications. As researchers and developers continue to explore the potential of Llama-2, we can expect further breakthroughs in the realm of natural language understanding and generation.