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Movie Gen combines multiple AI models for video, image, and audio generation. It can create 16-second videos from text, edit existing videos, and generate 45-second audio clips.

Author sues Meta for using pirated books to train Llama AI. Lawsuit claims copyright infringement, highlighting tension between AI and IP rights.

MetaLearner integrates Llama 3.1 models, enhancing enterprise analytics. Open-source AI enables rapid innovation in data science.

Meta's Reality Labs release a dataset of 2,400+ detailed 3D object models, aiming to accelerate 3D reconstruction research for e-commerce and immersive reality.

Llama 3.2 introduces vision models and lightweight text models for edge devices. New 128K token context length enables advanced on-device AI tasks.

Meta awards $1.8M in grants for AI projects using Llama models, supporting education, agriculture, and healthcare initiatives globally.

Together AI's LlamaCoder uses Llama 3.1 405B to generate full apps from prompts. 200,000+ apps created in a month, democratising AI development.

Anadol fine-tunes Llama models with 500M+ nature images. "Open source information is like gold for art making," he says, emphasising AI's artistic potential.

Upeo Labs' Somo-GPT uses Llama 3 models as multi-subject teaching assistant in 500 Kenyan schools. Supports national curriculum digitisation efforts.

Llama models near 350M downloads, with 20M in the past month. Usage doubled from May to July 2024, and major firms are integrating Llama-based AI.

Infosys integrates Llama 3.1 into Topaz for document, video, and audio processing. It's also used in a legal assistant with RAG for cited information.

The guide suggests fine-tuning, particularly parameter-efficient fine-tuning, as a more viable approach for smaller teams with limited resources compared to pre-training methods.

The guide identifies five scenarios where fine-tuning excels: customising tone and format, improving accuracy, addressing niche domains, reducing costs via distillation, and developing new abilities.

Meta AI's guide emphasises dataset quality for fine-tuning LLMs, suggesting small high-quality datasets often outperform larger low-quality ones. It compares full fine-tuning and PEFT techniques.

Meta AI's $2 million grant programme supports innovative Llama 3.1 applications for global challenges, offering up to $500,000 per project in economic development, science, and public service.