Mastering Self-Adaptive LLMs with Transformer2
Transformer2 is a revolutionary framework enhancing LLMs with self-adaptive capabilities through Singular Value Fine-Tuning and
ADaSci Expert
Sourabh Mehta
A Low Code Approach to Build Powerful AI Agents with Smolagents
Smolagents enable large language models (LLMs) to handle dynamic workflows with ease. Learn how its
Mastering the Art of Mitigating AI Hallucinations
AI hallucinations challenge generative models’ reliability in critical applications. Learn about advanced mitigation techniques, including
Chartered Data Scientist (CDS™)
The highest distinction in the data science profession. Not just earn a charter, but use it as a designation.
Open-source Tools for LLM Observability and Monitoring
Open-source tools for LLM monitoring, addressing challenges and enhancing AI application performance.
LLaMA 3-70B Vs Mixtral 8x7B: Analyzing the Logical Prowess on NVIDIA NIM
Rigorous comparison of two cutting-edge models: LLaMA 3 70B and Mixtral 8x7B
How to optimize the infrastructure costs of LLMs
Learn how to reduce expenses and enhance scalability of AI solutions.
How to Select the Best Re-Ranking Model in RAG?
The success of RAG system depends on reranking model.
Understanding Okapi BM25: A Guide to Modern Information Retrieval
A ranking algorithm that enhances the relevance of search results
RAG with Milvus Vector Database and LangChain
RAG integrates Milvus and Langchain for improved responses.
Fine-Tuning Pre-Trained Multitask LLMs: A Comprehensive Guide
LLMs are finely tuned to deliver optimal results across diverse tasks.
Nvidia Neva 22B vs Microsoft kosmos-2: A Battle of Multimodal LLMs
Explore the capabilities of Nvidia’s Neva 22B and Microsoft’s Kosmos-2 multimodal LLM in event reporting,
How Much Energy Do LLMs Consume? Unveiling the Power Behind AI
Exploring the energy consumption of LLMs at different stages of applications
In-context learning vs RAG in LLMs: A Comprehensive Analysis
RAG and ICL have emerged as techniques to enhance the capabilities of LLMs
