{"version":1,"type":"rich","provider_name":"Libsyn","provider_url":"https:\/\/www.libsyn.com","height":90,"width":600,"title":"Finely Tuned Models","description":"I have no idea if this is true, but this post on X says that Thomson Reuters used information they've collected for decades to fine tune and train a model. They started with one of the qwen models and then spent $40 million to add their knowledge to the model. The post says this model is comparable to the Gpt5.5 and Sonnet 5 models. They have used 10% of their data, which spans over 100 years. I have many questions. But first, $40mm? How many tokens is USD$40mm? A quick calculation is trillions of tokens, and while I'm sure there is some payback if lots of their customers use the model for work, there's also a compute cost every time they use the model. Perhaps they can fine-tune and train the model more efficiently over time, but I can't see many individual companies spending this effort on training their own model. Read the rest of Finely Tuned Models ","author_name":"Voice of the DBA","author_url":"http:\/\/www.voiceofthedba.com","html":"