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Model Alignment Process

The article discusses methods to align large language models (LLMs) with human preferences, focusing on techniques like Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO). It also introduces non-RL methods such as Kahneman-Tversky Optimization (KTO).

Serverless Deployment of Mistral 7B v0.2 using Runpod

The article provides a step-by-step guide to deploying the Mistral 7B v0.2 model on RunPod's serverless GPU cloud infrastructure. It covers setting up the environment, writing deployment scripts, and configuring the Docker environment for efficient AI application scaling.

Serverless Deployment with Google Gemma using Beam Cloud

Deploy Google Gemma 2B on Beam Cloud using FastAPI for serverless inference. This guide covers model setup, Hugging Face token authentication, autoscaling, and seamless deployment. Learn how Beam Cloud simplifies LLM hosting with scalable infrastructure.

Serverless Deployment of Mistral 7B with Modal Labs and HuggingFace

Learn how to deploy Mistral-7B-Instruct serverlessly using Modal Labs for cost-efficient, scalable AI inference. This guide covers serverless benefits, cost savings, cold starts, and a step-by-step deployment process with Hugging Face Transformers.

SLM Journey Unveiled

Prem’s "SLM Journey Unveiled" details training a 1B parameter Small Language Model with 8K context length. It covers dataset challenges, Distributed Data Parallelism (DDP) with Ray, and optimization techniques for data partitioning and gradient synchronization.

The Synthetic Data Revolution

This article delves into the emergence of synthetic data in AI, discussing its generation methods, applications across various data types, and its significance in overcoming data scarcity and privacy challenges, ultimately contributing to the pursuit of Artificial General Intelligence (AGI)

RAGs are cool, but what about their privacy?

This article explores privacy concerns in Retrieval-Augmented Generation (RAG) applications, highlighting data protection challenges and offering actionable solutions to ensure secure and compliant AI systems while leveraging the benefits of RAG.