Casper da Costa-Luis

I can't reproduce your error @VikNeu ... can run `echo $HOME` and `ls $HOME/.niftypet` in the container?

Based on `$HOME` being `/home/BRAIN/mthalhammer/` in your container, you seem to be doing something messing with home dirs. Make sure you install after such changes (or copy the old home...

try now?

> [...] > +> stream[2]: 3 chunks of data are DONE. > [...] > ic> maximum prompt sino value: 13 looks unusually small. Also: > invalid argument in /tmp/pip-install-r1u6rc7y/nipet_f727f69030e8494d9faf2f743df5c3d7/niftypet/nipet/prj/src/prjf.cu at...

for OSS services we control, I very very strongly suggest using URLs with built-in checksums instead, e.g. https://github.com/premAI-io/prem-services/releases/download/v1/cht-llama-cpp-mistral-1.1.2-aarch64-apple-darwin instead of https://github.com/premAI-io/prem-services/releases/download/v1/cht-llama-cpp-mistral-1-aarch64-apple-darwin becuase - they're OSS and `https:` and made by...

See also [Meaning of "Open"](https://book.premai.io/state-of-open-source-ai/licences/#meaning-of-open) - I agree it's deliberately confusing. Open source weights doesn't have to mean open source training data or permissive/OSI-approved licence terms.

Good idea; added a link to [Meaning of "Open"](https://book.premai.io/state-of-open-source-ai/licences/#meaning-of-open) in #97 Also note that [OSI's "open source definition" (OSD)](https://opensource.org/osd) is mentioned in the link above, but I completely disagree with...

Hey @mzattera! What made you develop `predictive-powers`? What was lacking in the alternatives? Was it purely a language preference?

```python #from tqdm.auto import tqdm from tqdm import tqdm import pandas as pd df = pd.DataFrame(...) tqdm.pandas(desc="customised", postfix={"hello": -1}) df.progress_apply(...) tqdm.pandas(desc="remove postfix", postfix=None) df.progress_apply(...) ``` I agree this is clunky...

That's not exactly currently supported, and it wouldn't be easy to add without breaking backward compatibility. The easiest approach would be explicitly defining a `bar_format`, e.g: ```python tqdm.pandas( bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt}...