The blog post provides resources on how to use LLaMA 2, including how to prompt the chat model, how to train the model using PEFT, and how to deploy the model for inference. It also provides links to the research behind the model and its performance benchmarks. LLaMA 2 can be deployed in a local environment, using managed services like Hugging Face Inference Endpoints, or through cloud platforms like AWS, Google Cloud, and Microsoft Azure.
Key takeaways:
- LLaMA 2 is a large language model developed by Meta, trained on 2 trillion tokens, and is available for free for research and commercial use through providers like AWS, Hugging Face, and others.
- LLaMA 2 comes in 3 different sizes - 7B, 13B, and 70B parameters and includes improvements like a 4096 default context window, commercial use allowance, and the adoption of grouped-query attention (GQA) in the 70B model.
- LLaMA 2 can be tested through different playgrounds like HuggingChat, Hugging Face Spaces, and Perplexity, which provide interactive demos and opportunities to chat with the models.
- LLaMA 2 can be fine-tuned using techniques like PEFT and can be deployed in local environments, managed services like Hugging Face Inference Endpoints, or cloud platforms like AWS, Google Cloud, and Microsoft Azure.