5/ Each of the tech mentioned above has its own pros and cons. The processor that you are running in your system (a phone, a laptop, etc) will have a weighted sum of all the above.

It baffles me to think about all of this. 🤗

4/N Multi Threading in Single Core

In a core, we can have multiple register blocks (context blocks) to access different instructions. This way if a process is stalled, the processor quickly jumps to another.

3/N SIMD paradigm

In a single core if we have duplicate ALUs we can operate of a bunch of data in a single clock tick. The catch? Each operation should be the same.

Single instruction Multiple Data

2/N Multi Core Processor:

A single processor consists of a control unit, arithmetic unit and some registers. How about we duplicate this block into multiple blocks? This is the multi-core architecture. As a programmer you would need to explicitly mention which code runs where.

1/N Superscalar processors:

Your program is a list of instructions. This list almost always has independent instructions. A superscalar processor would identify them and execute seperately in the same clock tick.

HF model collection for transformers:
huggingface.co/collections/...

HF model collection for OpenCLIP and timm:
huggingface.co/collections/...

And of course big_vision checkpoints:
github.com/google-resea...

Paper:
arxiv.org/abs/2502.14786

HF blog post from @arig23498.bsky.social et al. with a gentle intro to the training recipe and a demo:
huggingface.co/blog/siglip2

Thread with results overview from Xiaohua (only on X, sorry - these are all in the paper):
x.com/XiaohuaZhai/...

I forgot to mention that you can use the same code to access any `warm` model on the Hub.

Here is a list of all the `warm` models: huggingface.co/models?infer...

Happy vibe checking 😇

[N/N]

I have created a simple and quick notebook to access this inference api and use `huggingface_hub` to access the model through it.

huggingface.co/datasets/ari...

[4/N]

But today it was my lucky day. I noticed that the model was already loaded on the Serverless Inference API and was ready to be used.

No more spinning up my GPUs and stress testing them (happy GPU noises)

[3/N]

My usual workflow is to visit the Hugging Face Hub model card (here that was hf[dot]co[dot]Qwen/QwQ-32B-Preview) and copy the working code sample.

I am sure this is how most of you work with a new model as well (if not, I would love to hear from you)

[2/N]

I like the evaluation part. Is there some evals you particularly like?

🙋‍♂️ ariG23498

awesome, thanks a lot for sharing 🙌