On The Bike Shed, hosts Joël Quenneville and Stephanie Minn discuss development experiences and challenges at thoughtbot with Ruby, Rails, JavaScript, and whatever else is drawing their attention, admiration, or ire this week.
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Revolutionizing AI with Java: From LLMs to Vector APIs
Manage episode 442468471 series 2469611
Content provided by Adam Bien. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Adam Bien or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.
An airhacks.fm conversation with Alfonso Peterssen (@TheMukel) about:
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Alfonso previously appeared on "#294 LLama2.java: LLM integration with A 100% Pure Java file", discussion of llama2.java and llama3.java projects for running LLMs in Java, performance comparison between Java and C implementations, use of Vector API in Java for matrix multiplication, challenges and potential improvements in Vector API implementation, integration of various LLM models like Mistral, phi, qwen or gemma, differences in model sizes and capabilities, tokenization and chat format challenges across different models, potential for Java Community Process (JCP) standardization of gguf parsing, quantization techniques and their impact on performance, plans for integrating with langchain4j, advantages of pure Java implementations for AI models, potential for GraalVM and native image optimizations, discussion on the future of specialized AI models for specific tasks, challenges in training models with language capabilities but limited world knowledge, importance of SIMD instructions and vector operations for performance optimization, potential improvements in Java's handling of different float formats like float16 and bfloat16, discussion on the role of smaller, specialized AI models in enterprise applications and development tools
Alfonso Peterssen on twitter: @TheMukel
344 episodes
Manage episode 442468471 series 2469611
Content provided by Adam Bien. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Adam Bien or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.
An airhacks.fm conversation with Alfonso Peterssen (@TheMukel) about:
…
continue reading
Alfonso previously appeared on "#294 LLama2.java: LLM integration with A 100% Pure Java file", discussion of llama2.java and llama3.java projects for running LLMs in Java, performance comparison between Java and C implementations, use of Vector API in Java for matrix multiplication, challenges and potential improvements in Vector API implementation, integration of various LLM models like Mistral, phi, qwen or gemma, differences in model sizes and capabilities, tokenization and chat format challenges across different models, potential for Java Community Process (JCP) standardization of gguf parsing, quantization techniques and their impact on performance, plans for integrating with langchain4j, advantages of pure Java implementations for AI models, potential for GraalVM and native image optimizations, discussion on the future of specialized AI models for specific tasks, challenges in training models with language capabilities but limited world knowledge, importance of SIMD instructions and vector operations for performance optimization, potential improvements in Java's handling of different float formats like float16 and bfloat16, discussion on the role of smaller, specialized AI models in enterprise applications and development tools
Alfonso Peterssen on twitter: @TheMukel
344 episodes
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