Max of THE DECODER is an editor specializing in exploring the depths of consciousness, artificial intelligence, and their implications- pondering whether machines can possess or simulate cognition.
ChatGPT’s training data has enabled the recent emergence of numerous AI models, Stanford’s Alpaca being the pioneering one.
Stanford researchers recently unveiled Alpaca, a variant of LLaMA 7B that has been fine-tuned with AI-generated data. This model was trained with the help of 52,000 example statements from OpenAI’s GPT-3.5 (text-davinci-003).
Eliezer Yudkowsky, an alignment researcher, perceives Alpaca as a rival for organizations such as OpenAI despite its low cost of $600. It registered scores nearly analogous to those obtained in the team’s tests.
What You Need To Know About ALPACA FORMULA Open Source Licensing
Stanford has not yet released the LLaMA model used for Alpacas since the OpenAI GPT-3.5 terms of use restrict its commercial use. However, they have made the training data and code available to generate and fine-tune the model.
Using the resource-efficient low-rank adaptation (LoRA) method in Meta’s LLaMA, Alpaca-LoRA was released shortly after Alpaca and is heavily inspired by this work. It has been shown to achieve results comparable to Alpaca.
Nomic AI has unveiled GPT4All – a LLaMA version trained using 430,000 outcomes from a dataset of one million total outcomes pulled from GPT-3.5-turbo.
ChatDoctor is a LLaMA model that has been specially designed for medical conversations. To train the ChatDoctor framework, first, 52,000 Alpaca examples were used, followed by 5,000 real chats between healthcare experts and patients.
Databricks, instead of relying on LLaMA for its chatbot Dolly, chose EleutherAI’s GPT-J-6B and the Alpaca training dataset to power it.
Databricks say:
“We find that even years-old open source models with much earlier architectures exhibit striking behaviors when fine tuned on a small corpus of instruction training data,”
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Alpaca, Stanford’s open-source model recipe, leverages the powerful LLaMA models with specially generated ChatGPT datasets. With the recent leak of larger LLaMA models, further improvements to these models are expected; however, they will not be available commercially.
Google employees wanted to use dialogs created by ChatGPT as training data for Bard, but the plan was stopped when a staff member pointed it out to the administration. This shows that ChatGPT output is sufficiently suitable for first-class training data.
High-quality, human-generated data is still important for high-performance models, as OpenAI employs several human experts to create specialized data. This is particularly necessary for OpenAI to strengthen its models and increase their accuracy.
For those in the open-source community looking to create a free and effective alternative to ChatGPT, the AI results may suffice in its current state.
Stanford’s Alpaca has sparked a movement within the AI space: leveraging ChatGPT-generated data of exceptional caliber to train open-source models.
Alpaca-LoRaA, GPT4All, ChatDoctor, and Dolly – all of which use EleutherAI’s model – are examples of cutting-edge technology solutions. These innovative solutions help people in diverse areas, such as communication, data analysis, and automatic machine learning.
AI-generated data, particularly when it comes to fine-tuning and open-source models, has much potential; however, its commercial use is inhibited by the licenses of LLaMA and policies from OpenAI.
Max, the managing editor at THE DECODER, is a philosopher. His knowledge and research focus is on consciousness, artificial intelligence, and the debate around whether machines can think or simulate doing so.
As the field of AI continues to grow and evolve, it’s clear that open-source solutions like ChatGPT will play an increasingly important role in driving innovation and creating new possibilities. By leveraging the power of open-source technology and collaboration, we can continue to push the boundaries of what’s possible with AI and create a brighter future for all.
Source: THE DECODER
