Yes, DeepSeek R1's release is impressive. But the real story is what happened in just 7 days after:
- Original release: 8 models, 540K downloads. Just the beginning...
- The community turned those open-weight models into +550 NEW models on Hugging Face. Total downloads? 2.5M—nearly 5X the originals.
The reason? DeepSeek models are open-weight, letting anyone build on top of them. Interesting to note that the community focused on quantized versions for better efficiency & accessibility. They want models that use less memory, run faster, and are more energy-efficient.
When you empower builders, innovation explodes. For everyone. 🚀
The most popular community model? @bartowski's DeepSeek-R1-Distill-Qwen-32B-GGUF version — 1M downloads alone.
The Hugging Face community has rated educational content in languages spoken by 1.6 billion people! New additions: • Japanese • Italian • Old High German
There's so much you could do with these developments. Especially combining them together into agentic applications or fine-tuning them on your use case.
I'm helping out on some community research to learn about the AI community. If you want to join in the conversation, head over here where I started a community discussion on the most influential model since BERT.
📣 Teachers and Students! Here's a handy quiz app if you're preparing your own study material.
TLDR, It's a quiz that uses a dataset to make questions and save answers
Here's how it works:
- make a dataset of multiple choice questions - duplicate the space add set the dataset repo - log in and do the quiz - submit the questions to create a new dataset
I made this to get ready for the agents course, but I hope it's useful for you projects too!
You can now use the Synthetic Data Generator with your own domain-specific seed data to generate a dataset for fine-tuning retrieval or reranking models.
Reminder: Don’t. Use. ChatGPT. As. A. Calculator. Seriously. 🤖
Loved listening to @sasha on Hard Fork—it really made me think.
A few takeaways that hit home: - Individual culpability only gets you so far. The real priority: demanding accountability and transparency from companies. - Evaluate if generative AI is the right tool for certain tasks (like search) before using it.
You can now use the "Synthetic Data Generator" at a much larger scale with your preferred inference engine: Ollama, vLLM, TGI, and serverless inference! 🔥
We've added a new chapter about the very basics of Argilla to the Hugging Face NLP course. Learn how to set up an Argilla instance, load & annotate datasets, and export them to the Hub.
On-demand audio transcription is an often-requested service without many good options on the market.
Using Hugging Face Spaces with Gradio SDK and the OpenAI Whisper model, I've put together a simple interface that supports the transcription and summarisation of audio files up to five minutes in length, completely open source and running on CPU upgrade. The cool thing is that it's built without a dedicated inference endpoint, completely on public infrastructure.
