[ot][spam][log] attempt bits: talk to a language model on list

[mailbombbin]

> Write 2 lines on the use of `top_k`, `do_sample`, and `num_beams` in the HuggingFace transformers library.

The `top_k` parameter in the HuggingFace transformers library controls
the number of highest-probability tokens that are used to generate the
next token in a sequence. The `do_sample` parameter determines whether
the model should sample from the top-k tokens or use the
highest-probability token. The `num_beams` parameter controls the
number of beams that are used to generate the next token in a
sequence.

...> Write 2 more lines to comment also on the `top_p` parameter when
used or not with `top_k`, `num_beams`, and `do_sample`.

The `top_p` parameter is used to control the diversity of the
generated text. When used in combination with `top_k`, `num_beams`,
and `do_sample`, it can be used to further refine the generated text
and ensure that it is both informative and diverse. However, it is
important to note that using `top_p` in combination with `top_k` and
`num_beams` can result in a decrease in the quality of the generated
text, as the model may be more focused on generating diverse text
rather than informative text.

...> What are typical values for `top_p`?

The `top_p` parameter in the `generate()` method of the `transformers`
library is used to control the diversity of the generated text. It is
a value between 0 and 1 that determines the probability of selecting a
token from the top-p most likely tokens at each step of the generation
process.

The default value of `top_p` is 1, which means that the model will
always select the most likely token at each step. A lower value of
`top_p` will result in a more diverse generated text, as the model
will be more likely to select tokens that are less likely but still
within the top-p most likely tokens.

Here are some typical values for `top_p` that you can use:

* `top_p=0.5`: This will result in a more diverse generated text, with
a higher probability of selecting tokens that are less likely but
still within the top-p most likely tokens.
* `top_p=0.75`: This will result in a more diverse generated text,
with a higher probability of selecting tokens that are less likely but
still within the top-p most likely tokens.
* `top_p=0.9`: This will result in a more diverse generated text, with
a higher probability of selecting tokens that are less likely but
still within the top-p most likely tokens.

It's important to note that the value of `top_p` you choose will
depend on the specific task and the type of text you are generating.
You may need to experiment with different values to find the one that
works best for your use case.