Talk

In the rapidly evolving landscape of AI, Java developers deserve native building blocks for creating innovative AI applications. While Python has enabled rapid development and is the backbone of model training, production-grade services and enterprises running on Java require access to local models and AI tools.
Enter JLama, a modern inference engine designed to bring the power of AI directly into the Java ecosystem without requiring GPUs. JLama supports most open models like Llama, Gemma, and Mixtral, leveraging the new Vector API in Java 21 for faster inference.
Key features include:

• Advanced model support and tokenizer compatibility
• Implementation of the latest techniques like Flash Attention, Mixture of Experts, and Group Query Attention
• Support for HuggingFace standard model formats and quantization
• Distributed inference capabilities

JLama is integrated into the LangChain4j project and, combined with the Java native vector search capabilities of JVector, forms a comprehensive AI stack for Java.
This talk will delve into JLama's technical intricacies and practical applications, including a live demo. Discover how JLama revolutionizes Java-AI integration, paving the way for innovative applications that harness the full potential of large language models.