fine-tuning of large language models - Labellerr

fine-tuning of large language models

A collection of 4 posts

5 Best Tools for LLM Fine-Tuning in 2024

5 Best Tools for LLM Fine-Tuning in 2024

Language Model Model (LLM) fine-tuning tools play a crucial role in optimizing Large Language Models (LLMs) for various natural language processing tasks. In this blog, we'll learn about five leading tools – Labellerr, Kili, Label Studio, Databricks Lakehouse, and Labelbox; each offering unique features and approaches to enhance the fine-tuning process.

Hands-on with Fine-Tuning LLM

fine-tuning of large language models

Fine-Tuning Tutorial: Falcon-7b LLM To A General Purpose Chatbot

LLM models undergo training on extensive text data sets, equipping them to grasp human language in depth and context. In the past, most models underwent training using the supervised method, where input features and corresponding labels were fed. In contrast, LLMs take a different route by undergoing unsupervised learning. In

Technical Knowhow for Fine-Tuning Model using Reinforcement Learning with Human Feedback

reinforcement learning with human feedback

Complete Guide On Fine-Tuning LLMs using RLHF

In discussions about why ChatGPT has captured our fascination, two common themes emerge: 1. Scale: Increasing data and computational resources. 2. User Experience (UX): Transitioning from prompt-based interactions to more natural chat interfaces. However, there's an aspect often overlooked – the remarkable technical innovation behind the success of models like ChatGPT.

Comprehensive Guide for Fine Tuning of LLMs

Large Language Models

Everything You Need To Know About Fine Tuning of LLMs

Pre-trained large language models (LLMs) come with impressive capabilities, such as text generation, summarization, and coding. Nevertheless, these LLMs are only universally suitable solutions for some scenarios. Depending on your specific application, you may encounter tasks the language model cannot handle successfully. In such situations, one viable approach is to