a list of 6 GPT-3 tips for getting the desired output
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post by Travis Tang from DataDrivenInvestor
OpenAI's document with strategies and tactics for getting better results from large language models
a post about the DSPy, a framework developed by the Stanford NLP group aimed at algorithmically optimizing language model prompts
AI Engineer Summit workshop
by Dr Alan D. Thompson. The following references came from this video description
Awesome Gen AI Tools: DeepMind says its new language model can beat others 25 times its size | MIT Technology Review
Awesome Gen AI Tools: Inside language models (from GPT-3 to PaLM) – Dr Alan D. Thompson – Life Architect
list with large language models from diverse companies
strategies for testing LLMs against jailbreaks and attacks
post that discusses the impact of LLaMa and Alpaca in popularizing LLMs and even using them in small hardware devices
open source, instruction-following LLM, fine-tuned on a human-generated instruction dataset licensed for research and commercial use
Awesome Gen AI Tools: Stability AI Launches the First of its StableLM Suite of Language Models — Stability AI
a foundational large language model (LLM) with 40 billion parameters trained on one trillion tokens shared by Technology Innovation Institute from Abu Dhabi
Awesome Gen AI Tools: A simple guide to fine-tuning Llama 2 | Brev docs
an experiment about management theories with ChatGPT by Harvard Business Review Italia
tools and papers for improving outputs from GPT
Awesome Gen AI Tools: Practical Tips for Finetuning LLMs Using LoRA (Low-Rank Adaptation)
giving language models unlimited context
Awesome Gen AI Tools: Automatic Hallucination detection with SelfCheckGPT NLI