Alfitaria/Q25-1.5B-VeoLu Q2.5-1.5-VeoLu is a 1.5 billion parameter General Purpose Creative model trained on Qwen2.5-1.5B-Instruct. Intended mostly as an educational process for myself, Veo Lu nevertheless manages to be usable most of the time, while also being light enough to potentially run on a smartphone.
Just tested Argilla's new data annotation feature - it's a game changer for AI project quality.
Upload CSVs, work with published datasets, or improve existing ones directly on HuggingFace Hub. Setup took < 2 minutes, no code needed (see example below where I selected a dataset to classify tweets in categories).
Real world impact: Missing in Chicago won a Pulitzer using a similar approach - 200 volunteers labeled police misconduct files to train their model. That's the power of good data annotation.
Three immediate use cases I see: - Build collaborative training sets with your community (surprisingly underused in AI journalism) - Turn your website chatbot logs into high-quality fine-tuning data - Compare generated vs published content (great for SEO headlines)
Works for solo projects or teams up to 100 people. All integrated with HuggingFace Hub for immediate model training.
Interesting to see tools like this making data quality more accessible. Data quality is the hidden driver of AI success that we don't talk about enough.
Introducing the 435M model that outperforms Llama-Guard-3-8B while slashing 75% of the computation cost! 💻💥 👉 Check it out: hbseong/HarmAug-Guard (Yes, INFERENCE CODE INCLUDED! 💡)