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.
- standardization, governance, simplified troubleshooting, and reusability in ML application development.
- integrations with vector databases and LLM providers to support new applications -
provides tutorials on integrating
This article provides a beginner-friendly introduction to Large Language Models (LLMs) and explains the key concepts in a clear and organized way.
• Continuous Integration (CI) and Continuous Deployment (CD) pipelines for Machine Learning (ML) applications
• Importance of CI/CD in ML lifecycle
• Designing CI/CD pipelines for ML models
• Automating model training, deployment, and monitoring
• Overview of tools and platforms used for CI/CD in ML
• This is an MCU-based vision AI module powered by Arm Cortex-M55 and Ethos-U55, supporting TensorFlow and PyTorch frameworks.
• It has a standard CSI interface, onboard digital microphone, and SD card slot.
• Compatible with XIAO series, Arduino, Raspberry Pi, and ESP dev board.
• Supports off-the-shelf and custom AI models from SenseCraft AI, including Mobilenet V1, V2, Efficientnet-lite, Yolo v5 & v8.
• Can be used for industrial automation, smart cities, transportation, smart agriculture, and mobile IoT devices.
A simple and fast data pipeline foundation with sophisticated functionality.
• A beginner's guide to understanding Hugging Face Transformers, a library that provides access to thousands of pre-trained transformer models for natural language processing, computer vision, and more.
• The guide covers the basics of Hugging Face Transformers, including what it is, how it works, and how to use it with a simple example of running Microsoft's Phi-2 LLM in a notebook
• The guide is designed for non-technical individuals who want to understand open-source machine learning without prior knowledge of Python or machine learning.
LangChain has many advanced retrieval methods to help address these challenges. (1) Multi representation indexing: Create a document representation (like a summary) that is well-suited for retrieval (read about this using the Multi Vector Retriever in a blog post from last week). (2) Query transformation: in this post, we'll review a few approaches to transform humans questions in order to improve retrieval. (3) Query construction: convert human question into a particular query syntax or language, which will be covered in a future post
This article discusses cyclical encoding as an alternative to one-hot encoding for time series features in machine learning. Cyclical encoding provides the same information to the model with significantly fewer features.
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