Multilingual Tokenization Efficiency in Large Language Models: A Study on Indian Languages
Authors: Mohamed Azharudeen M, Balaji Dhamodharan
ADaSci Expert
Sachin Tripathi
Sachin Tripathi is the Manager of AI Research at AIM, with over a decade of experience in AI and Machine Learning. An expert in generative AI and large language models (LLMs), Sachin excels in education, delivering effective training programs. His expertise also includes programming, big data analytics, and cybersecurity. Known for simplifying complex concepts, Sachin is a leading figure in AI education and professional development.
Harnessing LLMs for Time Series Forecasting: Developing a Swap-Based Hedging Strategy for Commodity Trading
Authors: Sriram Gudimella, Rohit Zajaria, Jagmeet Sarna
Elevating Fairness in Consumer Credit Assessments: A Large Language Model (LLM) Driven Approach
Authors: Shubhradeep Nandi, Kalpita Roy
Chartered Data Scientist (CDS™)
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MongoDB Atlas Vector Search for RAG powered LLM Applications
MongoDB Atlas Vector Search combines document databases with semantic search for smarter LLM applications.
A Hands-on Guide on CometLLM for LLM Explainability
CometLLM enhances LLM explainability through prompt logging, tracking, and visualization, facilitating transparency and reproducibility in
A Hands-on Guide to Arize Phoenix for LLM Observability
Robust monitoring and observability tool Arize AI’s Phoenix aids LLM deployment and optimization.
AnythingLLM for Local Execution and Inferencing of LLMs: A Deep Dive
AnythingLLM excels in local execution of LLMs, offering robust features for secure, no-code LLM usage.
LangGraph Studio for Implementing AI Agents: A Hands-on Guide
Explore LangGraph Studio, the first AI agent IDE that simplifies agent visualization, interaction and debugging
Building RAG-Powered LLM Applications with LlamaIndex Workflows
LlamaIndex workflows enable flexible RAG-powered LLM applications, surpassing traditional DAG-based approaches.
Hands-on Guide to LLM Caching with LangChain to Boost LLM Responses
LLM caching in LangChain addresses deployment challenges by storing and reusing generated responses.
Implementing RAG Pipelines using LightRAG and GPT-4o mini
LightRAG simplifies and streamlines the development of retriever-agent-generator pipelines for LLM applications.
A Hands-on Guide to llama-agents: Building AI Agents as Microservices
Discover the power of llama-agents: a comprehensive framework for creating, iterating, and deploying efficient multi-agent
RAVEN for Enhancing Vision-Language Models with Multitask Retrieval-Augmented Learning
RAVEN enhances vision-language models using multitask retrieval-augmented learning for efficient, sustainable AI.