Screenshot of this question was making the rounds last week. But this article covers testing against all the well-known models out there.
Also includes outtakes on the ‘reasoning’ models.
Screenshot of this question was making the rounds last week. But this article covers testing against all the well-known models out there.
Also includes outtakes on the ‘reasoning’ models.
Gemini 3 (Fast) got it right for me; it said that unless I wanna carry my car there it’s better to drive, and it suggested that I could use the car to carry cleaning supplies, too.
Edit: A locally run instance of Gemma 2 9B fails spectacularly; it completely disregards the first sentece and recommends that I walk.
You never know. The car wash may be out of order and you might need to wash your car by hand.
Well it is a 9B model after all. Self hosted models become a minimum “intelligent” at 16B parameters. For context the models ran in Google servers are close to 300B parameters models
Any source for that info? Seems important to know and assert the quality, no?
Here:
https://www.sitepoint.com/local-llms-complete-guide/
https://www.hardware-corner.net/running-llms-locally-introduction/
https://travis.media/blog/ai-model-parameters-explained/
https://claude.ai/public/artifacts/0ecdfb83-807b-4481-8456-8605d48a356c
https://labelyourdata.com/articles/llm-fine-tuning/llm-model-size
https://medium.com/@prashantramnyc/understanding-parameters-context-size-tokens-temperature-shots-cot-prompts-gsm8k-mmlu-4bafa9566652
To find them it only required a web search using the query local llm parameters and number of params of cloud models on DuckDuckGo.
Edit: formatting
Appreciated. Very much appreciated!