|
- Optimizing LLM for Long Text Inputs and Chat Applications
Learning Objectives: - Understand the challenges traditional LLM architectures face in processing long text sequences and dynamic conversational flows
- 4 Powerful Long Text Summarization Methods With Real Examples
The key changes that have led to the new push in long text summarization are the introduction of transformer models such as BERT and GPT-3 that can handle much longer input sequences of text in a single run and a new understanding of chunking algorithms
- Managing Long Form Content: Strategies for Effective AI Prompting
The strategies in this guide for effectively handling long-form content are: preprocessing the text, chunking and iteratively responding to text chunks, post-processing, and refining responses, utilizing AI assistants with longer context support, and taking advantage of code libraries that can produce summaries or improving indexing
- 125+ Best AI Tools to Finish Hours of Work in Minutes (2025)
From small startups to huge enterprises, AI tools help save time, cut costs, and boost productivity Want to stay ahead? Here are the best AI tools you should consider to speed up tasks, improve accuracy, and finish hours of work in minutes without hiring a professional Experts believe AI will become a core part of most jobs in the coming years
- Understanding Padding in NLP: Types and When to Use Them
This is where padding becomes crucial — it ensures that all input sequences have the same length, making the data compatible with the model In NLP (Natural Language Processing), padding is essential for managing sequence lengths, especially when dealing with text data
- Optimizing LLMs for Speed and Memory - Hugging Face
This consequently amplifies the memory demands for inference In many real-world tasks, LLMs need to be given extensive contextual information This necessitates the model’s capability to manage very long input sequences during inference
- Long Context Fine-Tuning: A Technical Deep Dive - together. ai
To enhance performance for long context length tasks, you need to teach the model how to effectively use and perform with long sequences With the latest updates, the Together AI platform now supports fine-tuning on context lengths as large as 32k tokens, with longer sequence lengths to follow
|
|
|