finetuner VS django-ninja

And all of that has something to do with finetuners. It basically fine-tunes AI models for specific use cases. With it can create a custom search experience that is tailored to their specific needs. I also wonder how this is going to be integrated into SEO tools soon since those tools are catered to traditional search engines.

Combining multiple lists into one is tough, but it's doable if you have the right approach. Fine-tuning GPT-3 might help, but finding enough examples is tough. You could use existing text data or manually label a set of training examples. A finetuner could be help too. It's a platform-agnostic toolkit that can fine-tune pre-trained models and it's customizable to do lots of tasks.

You can adjust the pause threshold a little longer for pauses between and phrases. You can also use the phrase detection mode, which sets a time limit for the entire phrase instead of ending the transcription prematurely. If your microphone sensitivity is low, you can also try adjusting the energy threshold. If you want, you can use finetuners.

It's possible that completion results may be identical to fine-tuned examples, but not guaranteed. Even with the same prompt, slight variations in output are expected due to the nature of probabilistic language models. You can experiment with different settings and parameters, including those with finetuners like these.

You can try creating your own dataset. Get some audio data that you want, preprocess it, and then create a custom dataset you can use to fine tune. You could use finetuners like these if you want as well.

We derived the dataset by pre-processing the deepfashion dataset using Finetuner. The image label generated by Finetuner is extracted and formatted to produce the text attribute of each product.

You can either use Hugging Face Transformers as they have a lot of pre-trained models that you can customize. Or Finetuners like this one: which is a toolkit for fine-tuning multiple models.

Very recently, a few non-English and multilingual CLIP models have appeared, using various sources of training data. In this article, we’ll evaluate a multilingual CLIP model’s performance in a language other than English, and show how you can improve it even further using Jina AI’s Finetuner.

I think you can convert your data into text and fine-tune the model on it. But that might not be the ideal way to go since you kind of base that on the model. Try transfer learning or finetuning with a finetuner.

you can try prompt-based classification or fine-tuning with a Finetuner. Prompts work well for simple tasks but fine-tuning may give better results for complex ones. Althouigh it's going to need more resources, but try both and see what works best for you.

Django Ninja [1], it forever changed how I write Django project, in a way so elegant and productive.

[1]: https://django-ninja.dev/

Django Ninja is a RESTful wrapper for Django, while UtilMeta Python Framework uses a more concise declarative ORM Schema for Django and other future-supporting ORMs like sqlachemy and Peewee to build RESTful APIs more efficiently, and supports not only Django but all Python mainstream frameworks like Django, Flask, Starlette, FastAPI, Sanic, Tornado, etc.

I can't recommend [django-ninja](https://github.com/vitalik/django-ninja) enough. It's an easy to use, extremely fast, typed API for django. I've found it to be better in almost all aspects when compared to djangorestframework.

It's gaining popularity but is still widely unknown.

> The only place I really see Django at large companies is as an api using DRF or something.

This is not a bad thing. Using Django as an API backend is amazingly fast in terms of development time, especially with modern frameworks such as django-ninja [1].

Just use the built-in ORM to create models, write your endpoints, and use the built-in admin interface to play with the database if you don't have endpoints for everything.

There is also a less known feature of Django called admindocs [2], which automatically generates a human readable, hyperlinked documentation for your models and relations between them.

[1] https://django-ninja.rest-framework.com/

[2] https://docs.djangoproject.com/en/4.2/ref/contrib/admin/admi...

Personally, I also prefer django-ninja to DRF.

Actually that's not fully true. If you mix async and sync codes in django-ninja there will be some errors. Where's the proof ? django-ninja doesn't support async auth

Subscribe to this issue :D

Also recommend Django-Ninja. It basically reimplements fastapi's type and decorator-based API construction, but embedded directly in django so you have access to django's ORM and middleware library.