Dear friends,
I decided to write because many are active on HuggieFace with their AI models.
I have been continuously testing AI Models 8/10 hours a day for a year now. And when I say that I test the models, I don't mean like many do on YouTube to get likes, with type tests. Tell me what the capital of Australia is or tell me who the tenth president of the United States is. Because these tests depress me as well as making me smile. Already 40 years ago my Commodore Vic 20 answered these questions in BASIC language!
I test models very seriously. Being a history buff, my questions are very oriented towards history, culture, geography, literature. So my tests are to try in every way to extrapolate answers and summaries to the AI models.
Now I note with great sadness that models are trained with a lot of data, but there is not enough focus on ensuring that the algorithm is able to extrapolate the data and return it to the user in a faithful and coherent manner.
Now if we want to use templates just to play with creative invented stories like poetry everything can be fine, but when we get serious Open Source templates to be installed locally seem very insufficient to me.
Furthermore, I note that the models are never accompanied in an excellent manner by configuration or preset data which the user often has to try to understand by making various calibrations.
Another issue, the models are always generic, there is no table of models with their actual capabilities.
More guidance would be needed. example This is a model that is good for Medicine, this has been trained with History data etc.
While we find ourselves researching Huggingface in an almost haphazard manner, not to mention total disarray
In Pavero words I want to tell you, since you work hard, you too should ensure that the models, in addition to being filled with data, can then be able to use them and give them to the user.
So let's take a step forward and improve the progress.
Claudio from Italy
I recommend following some fine-tune tutorials to train a history oriented model yourself. You can get decent result with a few megabytes of good quality dataset about the history content you are interested in. It should be a much more interesting activity than testing models all day! If you want the model to recall intricate details, use higher rank LoRAs or try full fine tune rather than parameter efficient fine tunes.
But like others said, open source models we have today are still far from GPT-4. Fine tuning a small model also barely add any new capability to the model, it is only "tuning" it to be knowledgeful in something else. These LLMs are pre-trained with trillions of tokens, a few tens of thousands more will not make it any smarter.