Alvaro Bartolome

🧨 I built something with #Zig!

`tokeni.zig` is a std-only implementation of the Byte Pair Encoding (BPE) algorithm in Zig for tokenizing sequences of text, used by OpenAI (among many others) to tokenize the text when pretraining their large language models!

github.com/alvarobartt/...

How to read and parse JSON with Zig 0.13 How to read and parse JSON with Zig 0.13 by alvarobartt

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

For anyone interested in Zig I wrote a small post titled "How to read and parse JSON with Zig 0.13" that explains how to read JSON from a file with keys with different value types and how to access those values.

love this quote "working smarter helps, but the real superpower is resting smarter"

a highly recommended read!

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

🤗 Here's a simple script that calculates the required VRAM for serving DeepSeek R1 from @huggingface Hub safetensor's metadata!

P.S. The result of the script above is: "model_id='deepseek-ai/DeepSeek-R1' requires memory=756.716GB"

hmm refactoring in zig is not as easy as it's in rust, even though seems fairly common too, right? or is it just me? 🤔

stuff that matters takes time

GitHub - huggingface/open-r1: Fully open reproduction of DeepSeek-R1 Fully open reproduction of DeepSeek-R1. Contribute to huggingface/open-r1 development by creating an account on GitHub.

Last moments of closed-source AI 🪦 :
Hugging Face is openly reproducing the pipeline of 🐳 DeepSeek-R1. Open data, open training. open models, open collaboration.

🫵 Let's go!
github.com/huggingface/...

DeepSeek-R1 - a deepseek-ai Collection We’re on a journey to advance and democratize artificial intelligence through open source and open science.

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/...

🐐 DeepSeek is not on the @hf.co Hub to take part, they are there to take over!

Amazing stuff from the DeepSeek team, ICYMI they recently released some reasoning models (DeepSeek-R1 and DeepSeek-R1-Zero), fully open-source, their performance is on par with OpenAI-o1 and it's MIT licensed!

you can find so much gold in github gists wow, i was not a big fan because the discoverability doesn't seem great, but been exploring gists lately and so much gold stuff in there!

in case anyone missed it, we're running a certified course on ai agents at hugging face starting on feb 2nd; the course is on how to build you own ai agents for different cool use cases built on top of open source!

👇 you can sign up in the link below, don't miss it!

bit.ly/hf-learn-age...

ok, here we go again 😅

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

Not quite sure yet about how's following me here, but I may consider not just x-posting but also eventually post more random thoughts + content in Spanish, is that something you'd be interested in?

awesome 🤗

how do I get in there? 🤗

here we go again!

i work at hugging face and here you can expect posts about machine learning (llms mainly), some rust, some nvim nerdy stuff and anything related to hugging face 🤗

posting is not easy for me, but i’ll try to do better from now on, support is highly appreciated!

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!

❄️ Additionally, there are a bunch of models (around 1000) that are "Cold", meaning that those are not loaded in the Serverless API, but can be loaded when sending a request to them, also meaning that the first request may take a while until the model is loaded!

You may be wondering how do you know what models out of the over a million publicly available on the Hub can be used via the Serverless API?

Well, we have the "Warm" tag that indicates that a model is loaded in the Serverless API and ready to be used 🔥

💡 Did you know that you can use over 13700 public open models and adapters on the @huggingface Hub for FREE?

You just need a free account on the Hugging Face Hub (you can also subscribe to PRO to increase the requests per hour)

More details on the thread 🧵

oh great, just updated mine, thanks for sharing!

Bluesky pro-tip: you can set your domain as a handle:

bsky.social/about/blog/4...

Bonus: you can keep your handle if you ever want to move to another server in the future.