Entry Point AI es una plataforma de ajuste fino que permite a los usuarios entrenar, gestionar y evaluar modelos de lenguaje personalizados de gran tamaño. Proporciona una interfaz fácil de usar para entrenar modelos sin necesidad de habilidades de programación.
Para usar Entry Point AI, sigue estos pasos: 1. Identifica la tarea que deseas que realice tu modelo de lenguaje. 2. Importa ejemplos de la tarea deseada en Entry Point AI utilizando un archivo CSV. 3. Evalúa el rendimiento de los modelos ajustados utilizando las herramientas de evaluación incorporadas. 4. Colabora con compañeros de equipo para gestionar el proceso de entrenamiento y hacer un seguimiento del rendimiento del modelo. 5. Utiliza las herramientas de conjunto de datos para filtrar, editar y gestionar tu conjunto de datos. 6. Genera ejemplos sintéticos utilizando la función de Síntesis de Datos de IA. 7. Exporta los modelos ajustados o úsalos directamente en tus aplicaciones.
Aquí está el Discord de Entry Point AI: Plataforma de Ajuste Fino para Grandes Modelos de Lenguaje: https://discord.gg/BUNsbE4AJr. Para obtener más mensajes de Discord, haga clic aquí(/es/discord/bunsbe4ajr).
Entry Point AI: Plataforma de Ajuste Fino para Grandes Modelos de Lenguaje Nombre de la empresa: Entry Point AI Inc. .
Enlace de precios de Entry Point AI: Plataforma de Ajuste Fino para Grandes Modelos de Lenguaje: https://www.entrypointai.com/pricing/
Enlace de Youtube de Entry Point AI: Plataforma de Ajuste Fino para Grandes Modelos de Lenguaje: https://www.youtube.com/@EntryPointAI
Enlace de Linkedin de Entry Point AI: Plataforma de Ajuste Fino para Grandes Modelos de Lenguaje: https://www.linkedin.com/company/entrypointai
Por Amelia el Mayo 22 2024
Desbloquea el Futuro: ¡Descubre 9 Avances Inesperados en IA!
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LoRA & QLoRA Fine-tuning Explained In-Depth
👉 Start fine-tuning at www.entrypointai.com In this video, I dive into how LoRA works vs full-parameter fine-tuning, explain why QLoRA is a step up, and provide an in-depth look at the LoRA-specific hyperparameters: Rank, Alpha, and Dropout. 0:26 - Why We Need Parameter-efficient Fine-tuning 1:32 - Full-parameter Fine-tuning 2:19 - LoRA Explanation 6:29 - What should Rank be? 8:04 - QLoRA and Rank Continued 11:17 - Alpha Hyperparameter 13:20 - Dropout Hyperparameter Ready to put it into practice? Try LoRA fine-tuning at www.entrypointai.com
Fine-tuning 101 | Prompt Engineering Conference
👉 Start fine-tuning at www.entrypointai.com Intro to fine-tuning LLMs (large language models0 from the Prompt Engineering Conference (2023) Presented by Mark Hennings, founder of Entry Point AI. 00:13 - Part 1: Background Info -How a foundation model is born -Instruct tuning and safety tuning -Unpredictability of raw LLM behavior -Showing LLMs how to apply knowledge -Characteristics of fine-tuning 06:25 - Part 2: When to use it -Examples of specialized tasks that fine-tuning benefits -Reasons to fine-tune a model -Speed and cost benefits -Prompt length before and after fine-tuning -Fine-tuning in the team environment -LLM workflow from prompt engineering and fine-tuning to production -Size of dataset for fine-tuning 11:27 - Part 3: No-code Demo -Demo of no-code fine-tuning on Entry Point AI Learn more at https://www.entrypointai.com
Fine-tuning Datasets with Synthetic Inputs
👉 Start building your dataset at www.entrypointai.com There are virtually unlimited ways to fine-tune LLMs to improve performance at specific tasks... but where do you get the data from? In this video, I demonstrate one way that you can fine-tune without much data to start with — and use what little data you have to reverse-engineer the inputs required! I show step-by-step how to take a small set of data (for my example I use 20 press releases I pulled from the internet), use LLMs to generate the missing inputs, run a real fine-tuning job, and play with the model to see how it behaves. The actual fine-tuning cost $0.35. Turns out, fine-tuning can be pretty affordable! Creating a dataset is half art and half science, but there is nothing particularly hard about it once you understand the core concepts. You don't need an ML degree to build your own fine-tuning dataset and train a custom LLM. You just need to be able to think critically about what you want the model to do and how you want to steer the model through inputs. Follow along as I share my thought process and tools to create fine-tuning datasets and see how decisions along the way affect our interaction with the model and its outputs. Remember to press subscribe! ✅ You can try fine-tuning for yourself at: https://www.entrypointai.com My LinkedIn: https://www.linkedin.com/in/markhennings/ Entry Point AI Discord: https://discord.gg/7AjKSRd3hK
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