https://alvarobartt.me/how-to-read-and-parse-json-with-zig-0-13

Right, the point is that on Rust you end up "refactoring" a lot (at least I do), but seems easier to handle, whilst on Zig I don't feel is as easy, not especially complex either, just more cumbersome

Check DeepSeek-R1 collection on the Hugging Face Hub, with not just DeepSeek-R1 and DeepSeek-R1-Zero, but also distilled their reasoning patterns to fine-tune smaller models!

huggingface.co/collections/...

because it's my native language, anyway it was just an idea, not sure I'll do it anyway 🤗

awesome 🤗

how do I get in there? 🤗

Read more about the Serverless Inference API in the documentation!

https://huggingface.co/docs/api-inference

🔥 Finally, if you are willing to get started quickly and experiment with LLMs feel free to give the recently released Inference Playground a try!

https://huggingface.co/playground

👨‍💻 Alternatively, you can also use the Serverless Inference API programmatically via cURL, the huggingface_hub Python SDK, the openai SDK for chat completion, and much more!

Find all the alternatives at https://huggingface.co/docs/api-inference

🔎 Now let's explore some of the different alternatives to run inference via the Serverless API!

The most straightforward one is via the Hugging Face Hub available on the model card of the Serverless API supported models!

🔒 Before going on, you will first need to generate a Hugging Face fine-grained token with access to the Serverless API, as the requests need to be authenticated so keep the token safe and avoid exposing it!