Remove writing finetuning
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Saurabh Vij, CEO & Co-Founder of MonsterAPI – Interview Series

Unite.AI

MonsterAPI leverages lower cost commodity GPUs from crypto mining farms to smaller idle data centres to provide scalable, affordable GPU infrastructure for machine learning, allowing developers to access, fine-tune, and deploy AI models at significantly reduced costs without writing a single line of code.

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Researchers from UC Berkeley Introduce Gorilla: A Finetuned LLaMA-based Model that Surpasses GPT-4 on Writing API Calls

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A recent breakthrough in the field of Artificial Intelligence is the introduction of Large Language Models (LLMs). These models enable us to understand language more concisely and, thus, make the best use of Natural Language Processing (NLP) and Natural Language Understanding (NLU).

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Researchers from UC Berkeley Introduce Gorilla: A Finetuned LLaMA-based Model that Surpasses GPT-4 on Writing API Calls

Flipboard

A recent breakthrough in the field of Artificial Intelligence is the introduction of Large Language Models (LLMs). These models enable us to …

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MPT-30B: MosaicML Outshines GPT-3 With A New LLM To Push The Boundaries of NLP

Unite.AI

MPT-30B uses an Attention with Linear Biases (ALiBi) technique to understand longer sequences and extend the context window beyond 8k tokens during finetuning or inference. Using a technique called tiling, FlashAttention reduces the number of times the model needs to read from or write to memory, speeding up the processing.

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Latest Modern Advances in Prompt Engineering: A Comprehensive Guide

Unite.AI

GPT4Tools GPT4Tools finetunes open-source LLMs to use multimodal tools via a self-instruct approach, demonstrating that even non-proprietary models can effectively leverage external tools for improved performance. Gorilla and HuggingGPT Both Gorilla and HuggingGPT integrate LLMs with specialized deep learning models available online.

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Transforming Specialized AI Training- Meet LMFlow: A Promising Toolkit to Efficiently Fine-Tune and Personalize Large Foundation Models for Superior Performance

Marktechpost

However, more finetuning of such LLMs is required to increase performance on specialized domains or jobs. Common procedures for finetuning such big models include: Ongoing pretraining in niche areas, allowing a broad base model to pick up expertise in such areas. Individualized model training is now accessible to everyone with LMFlow.

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RAG vs Finetuning — Which Is the Best Tool to Boost Your LLM Application?

Topbots

And eventually we get to the point of where we ask ourselves: Should we use Retrieval-Augmented Generation (RAG) or model finetuning to improve the results? By finetuning, we are adjusting the model’s weights based on our data, making it more tailored to our application’s unique needs. However, my perspective has since evolved.

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