LLMs are not inherently non-deterministic during inference. I don't believe non-determinism implies lack of abstraction. Abstraction is simply hiding detail to manage complexity.
pypdfium2 is a great choice and a solid piece of software!
You might want to look into https://github.com/VikParuchuri/surya as an alternative to tesseract. Yes, it's associated with a commercial company, but as you long as you aren't a company with 5M in ARR or $5M in funding it's free to use.
This is an excellent use case for LLM fine-tuning, purely because of the ease of generating a massive dataset of input / output pairs from public C code
Coding aid for unittests. Debugging aid for languages / frameworks I'm not particularly familiar with. Work that requires reformatting. Translating from rough drafts to more polished / professional language. Learning more about domains I don't have much expertise in where I need specific conceptual questions answered.
Whether or not to split is more a measure of whether or not these two concepts are likely to split down the road than whether or not share similarity today.
Interestingly it sounds like offloading could be made quite efficient in a batch setting if you primarily care about throughput rather than latency. Though I guess for most current LLM applications latency is quite important.
Often it might be viable to implement prediction w/o necessarily implementing training (especially if there are published weights or a reference implementation). Not viable for papers where the key contribution is a change to the pre-training objective / training methodology / optimizer, but useful for papers where the key contribution is architectural.
I'm getting a variety of CORS errors in console. Maybe this helps:
```
Access to XMLHttpRequest at 'https://api.doqs.dev/v1/organization' from origin 'https://app.doqs.dev' has been blocked by CORS policy: Response to preflight request doesn't pass access control check: No 'Access-Control-Allow-Origin' header is present on the requested resource.
```