This article discusses the process of training a large language model (LLM) using reinforcement learning from human feedback (RLHF) and a new alternative method called Direct Preference Optimization (DPO). The article explains how these methods help align the LLM with human expectations and make it more efficient.
This article explains the Long RoPE methodology used to expand the context lengths in LLMs without significant performance degradation. It discusses the importance of context length in LLMs and the limitations of previous positional encoding methods. The article then introduces Rotational Positional Encoding (RoPE) and its limitations, and explains how Long RoPE extends RoPE to larger contexts.
Podman AI Lab is the easiest way to work with Large Language Models (LLMs) on your local developer workstation. It provides a catalog of recipes, a curated list of open source models, experiment and compare the models, get ahead of the curve and take your development to new heights wth Podman AI Lab!
Introduces proxy-tuning, a lightweight decoding-time algorithm that operates on top of black-box LMs to achieve the same end as direct tuning. The method tunes a smaller LM, then applies the difference between the predictions of the small tuned and untuned LMs to shift the original predictions of the larger untuned model in the direction of tuning, while retaining the benefits of larger-scale pretraining.
In this tutorial, learn how to improve the performance of large language models (LLMs) by utilizing a proxy tuning approach, which enables more efficient fine-tuning and better integration with the AI model.
- Proxy fine-tuning is a method to improve large pre-trained language models without directly accessing their weights.
- It operates on top of black-box LLMs by utilizing only their predictions.
- The approach combines elements of retrieval-based techniques, fine-tuning, and domain-specific adaptations.
- Proxy fine-tuning can be used to achieve the performance of heavily-tuned large models by only tuning smaller models.
- 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.
AI Helps Make Web Scraping Faster And Easier: Scrapegraph-ai is a new tool that uses large language models (LLMs) to automate the process of web scraping and data processing.
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