A tutorial on using LLM for text classification, addressing common challenges and providing practical tips to improve accuracy and usability.
The author describes building a GitHub repository assistant capable of answering user issues using Large Language Models (LLMs), specifically Gemini, and Redis.
Mem0 is a new solution that offers an intelligent, adaptive memory layer for Large Language Models (LLMs). It enhances personalized AI experiences by retaining and utilizing contextual information, leading to more relevant and effective interactions. Mem0's multi-level memory retention, adaptive personalization, and API integration make it a powerful tool for various applications like customer support, healthcare diagnostics, and more.
An article discussing the rise in interest among enterprises to build their own large language models (LLMs) using publicly available models as a starting point. The article discusses the challenges and benefits of this approach, as well as the need for enterprises to prepare for the integration of AI into their businesses.
A discussion post on Reddit's LocalLLaMA subreddit about logging the output of running models and monitoring performance, specifically for debugging errors, warnings, and performance analysis. The post also mentions the need for flags to output logs as flat files, GPU metrics (GPU utilization, RAM usage, TensorCore usage, etc.) for troubleshooting and analytics.
Learn how to repurpose an old PC to generate AI text and images, with a focus on using Ollama with Stable Diffusion. The guide covers installation, configuration, and setting up a web UI for a more organized user interface.
This article discusses the latest open LLM (large language model) releases, including Mixtral 8x22B, Meta AI's Llama 3, and Microsoft's Phi-3, and compares their performance on the MMLU benchmark. It also talks about Apple's OpenELM and its efficient language model family with an open-source training and inference framework. The article also explores the use of PPO and DPO algorithms for instruction finetuning and alignment in LLMs.
efficient method for fine-tuning LLM using LoRA and QLoRA, making it possible to train them even on consumer hardware
