Preliminary testing on my agent benchmark (based on https://
github.com/aymeric-rouche
r/benchmark_agents
…): Llama3-70B-Instruct is on par with GPT4! cc @lvwerra
MACHINE LEARNING
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Llama3-70B-Instruct matches GPT4 in agent benchmark test
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Python Machine Learning AI Engineering Tutorials Daily
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If you interested in: – Python – Machine Learning – AI Engineering Find me → @akshay_pachaar Everyday, I share tutorials on above topics! Cheers!
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AI Engineer Journey: From Basics to Deep Learning Expertise
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Your journey to becoming an AI Engineer mapped out! From AI basics to deep learning and beyond. Keep pace with the evolving tech landscape by following @ingliguori
. Boost your expertise with insights from 'The Digital Edge' https://
bit.ly/3u4pILl #AI #MachineLearning -
Jason Exploits Variant of Shunyua Yao’s Koala Architecture
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and jason is exploiting a variant of the @ShunyuYao12 Koala architecture
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Serverless Fine-tuning and Serving for Llama3 on Predibase
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Check out #serverless fine-tuning and serving for #Llama3 on @Predibase with our free trial! 🦙
— Predibase by Rubrik (@predibase) 18 avril 2024
🔥 Blazing fast serverless inference – #8b & #70b variants
🖌️ #Finetune in the UI or SDK via config
🌳 Start prompting your fine-tune instantly with #LoRAXhttps://t.co/Rq8fsQwaZl pic.twitter.com/CYON9Wx25ZCheck out #serverless fine-tuning and serving for #Llama3 on @Predibase with our free trial! Blazing fast serverless inference – #8b & #70b variants #Finetune in the UI or SDK via config Start prompting your fine-tune instantly with #LoRAX https://
predibase.com/free-trial -
Fine-tuning Models Before Release for Better Self-Description
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I agree with Jeremy here: we know that people are going to ask models about themselves here whether or not that actually makes sense to do so, so we should be able to fine-tune the model after initial training but before release to help it provide more useful answers there
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RLHF Annotation Bias and Model Vocabulary Development
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I don't know that RLHF would bias that kind of thing – my mental model is that annotators are shown two answers to the same prompt and asked which is "best", so if none of the test prompts happened to touch on the concept of a roadside kiosk that vocabulary wouldn't be affected
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Pre-training and Instruction-Tuning in Large Language Models
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The pre-training stage is where the billions of words of training data come into play – the instruction-tuning / RLHF stage is where human labelers are asked to vote on which generations are "best" – that's the bit that might influence things like "delve" https://
openai.com/research/instr
uction-following
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RLHF and Instruction Tuning: Understanding Model Training Mechanisms
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Yeah, what did it get wrong? It fitted my mental model of how the RLHF/instruction tuning stage works pretty closely
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SQuAD Explorer: Benchmark for Machine Reading Comprehension
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Oh yeah, this looks good https://
rajpurkar.github.io/SQuAD-explorer/