Those values are most of what I was looking for. An LLM is just predicting the next token (for simplicity, a word). It does so by generating every possible word with a probability associated with it, and then picking a random word from this list, influenced by its probability. So for the sentence “a cat sat” it might generate “on: 0.6”, “down: 0.2”, and so on. 0.6 just means 60%, and all the values add up to 1 (100%). Now, the number of tokens generated can be as big as the context, so you might want to pick randomly from the top 10, you control this with the parameter top_k, or you might want to discard all the words below 20%, you control this with min_p. And finally, in cases where you have a token with a big probability followed by tokens with very low probability, you might want to squash these probabilities to be closer together, by decreasing the higher tokens and increasing the lower tokens. You control this with the temperature parameter where 0.1 is very little squashing, and 1 a lot of it. In layman terms this is the amount of creativity of your model. 0 is none, 1 is a lot, 2 is mentally insane.

Now, without knowing your hardware or why you need docker, it is hard to suggest a tool to run LLMs. I am not familiar with what you are using, but it seems to not be maintained, and likely lacks the features needed for a modern LLM to work properly. For consumer grade hardware and personal use, the best tool these days is llamacpp, usually through a newbie friendly wrapper like LMStudio which support other backends as well and provide so much more than just a UI to download and run models. My advice is to download it and start there (it will download the right backend for you, so no need to install anything else manually).