Train a Reasoning-Capable LLM in One Weekend with NVIDIA NeMo - NVIDIA Developer
<a href="https://news.google.com/rss/articles/CBMinAFBVV95cUxQM2Vha244bG5yZ3Njbm1MUlk4dndsQzdyd2c0OHZ0a01HcVZLQUtsdGlfa1ZnLVRQTWJfOXJVRndpNUlmdU9QRUNlbXhqYkZKUDd1Tnk3RVhSWnlLMWlQdUFiUF8zY2h5WWZYNjQzbjh1YThkLWx2MVA1amt5WGpFeHZxWmJseEs2bVhSRjNkS2theUVYN1BBTzVrSGo?oc=5" target="_blank">Train a Reasoning-Capable LLM in One Weekend with NVIDIA NeMo</a> <font color="#6f6f6f">NVIDIA Developer</font>
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Wanted to share one of my personal projects, since similar work has been shared here. TLDR is that I trained an LLM from scratch on pre-1900 text to see if it could come up with quantum mechanics and relativity. The model was too small to do meaningful reasoning, but it has glimpses of intuition. When given observations from past landmark experiments, the model can declare that “light is made up of definite quantities of energy” and even suggest that gravity and acceleration are locally equivalent. I’m releasing the dataset + models and leave this as an open problem. You can play with one of the early instruction tuned models here (not physics post trained): gpt1900.com Blog post: https://michaelhla.com/blog/machina-mirabilis.html GitHub: https://github.com/michaelhla/gpt1900 submitted by
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Quoting Chengpeng Mou
From anonymized U.S. ChatGPT data, we are seeing: ~2M weekly messages on health insurance ~600K weekly messages [classified as healthcare] from people living in “hospital deserts” (30 min drive to nearest hospital) 7 out of 10 msgs happen outside clinic hours Chengpeng Mou , Head of Business Finance, OpenAI Tags: ai-ethics , generative-ai , openai , chatgpt , ai , llms
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