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Welcome at World Crypto Global. This portal is packed with useful content and resources to built out your own crypto skills. WorldCrypto is a site member of Gabriel Vega Network.
Circle and Blockchain at Berkeley introduce TXT2TXN, an AI-driven tool using Large Language Models to simplify blockchain transactions through intent-based applications. (Read More)
AI21 Labs introduces Jamba 1.5, a new family of large language models leveraging hybrid architecture for superior reasoning and long context handling. (Read More)
Anyscale's latest blog post delves into Direct Preference Optimization (DPO) with synthetic data, highlighting its methodology and applications in tuning language models. (Read More)
NVIDIA NIM microservices optimize throughput and latency for large language models, improving efficiency and user experience for AI applications. (Read More)
NVIDIA NIM introduces support for multilingual large language models, improving global business communication and efficiency with LoRA-tuned adapters. (Read More)
AMD's MI300X accelerators, with high memory bandwidth and capacity, enhance the performance and efficiency of large language models. (Read More)
NVIDIA NeMo enables enterprises to customize large language models for domain-specific needs, enhancing deployment efficiency and performance. (Read More)
NVIDIA's research into cyber language models aims to address cybersecurity challenges by training models on raw cyber logs, enhancing threat detection and defense. (Read More)
IBM and Red Hat launch InstructLab, enabling collaborative LLM customization without full retraining. (Read More)
Oracle's HeatWave GenAI now offers in-database LLMs and an automated vector store, enabling generative AI applications without AI expertise or additional costs. (Read More)
Apple has reportedly been luring experts from Google in its efforts to catch up with Big Tech companies in the race to develop and deploy high-level AI models.
NVIDIA's NIM microservices for LLMs are transforming the process of scientific literature reviews, offering enhanced speed and accuracy in information extraction and classification. (Read More)
Learn how to create detailed meeting summaries using AssemblyAI's LeMUR framework and large language models (LLMs) with just five lines of Python code. (Read More)
Explore the challenges and solutions in fine-tuning Large Language Models (LLMs) for effective domain-specific multi-turn conversations, as detailed by together.ai. (Read More)
Discover how model merging enhances the efficiency of large language models by repurposing resources and improving task-specific performance, according to NVIDIA's insights. (Read More)
Together.ai introduces LoLCATs, a novel approach for linearizing LLMs, enhancing efficiency and quality. This method promises significant improvements in AI model development. (Read More)
NVIDIA's latest advancements in parallelism techniques enhance Llama 3.1 405B throughput by 1.5x, using NVIDIA H200 Tensor Core GPUs and NVLink Switch, improving AI inference performance. (Read More)
Discover how integrating Large Language Models (LLMs) revolutionizes Conversation Intelligence platforms, enhancing user experience, customer understanding, and decision-making processes. (Read More)
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