| Forecasting Granite Final is a fine tuned version of Granite 3.3 2b Instruct. | |
| It was fine tuned on a one gigabyte dataset compiled from five gigabytes of internet scraped data on the topic of event forecasting and problem solving using LlamaFactory over three days on a single RX 7900 XTX gpu. | |
| Using the method of freeze training, the first five layers were unfrozen and trained at a learning rate of 1e-4 with two epochs. | |
| This model was designed to run as the main forecasting LLM for the Metaculs AI Forecasting competition to replace the big, inefficient, non-specialized LLMs provided by Metaclus (or self provided for that matter) as there are very few LLms out there designed specifically for event forecasting. | |
| This model's accuracy hasn't been tested, and whether or not the fine tuning improved its forecasting capabilities is not known. | |
| An interesting trait of this model is that it "talks" in dot-point explanations, even when explicitly told not to. For the use-case of the model, this shouldn't pose any issues. | |
| TEMPERATURE MUST BE SET TO 0 FOR IT TO FUNCTION. | |
| The model devolves into loops when given complex and long tasks, a temperature of 0 or close to 0 seems to fix this. |