It's just a matter of time before all the data leakage and data scraping associated with building, training, and using AI results in some kind of major scandal.
Fine-tune Gemma3n on videos with audios inside with Colab A100 🔥 Just dropped the notebook where you can learn how to fine-tune Gemma3n on images+audio+text at the same time!
keep in mind, it's made for educational purposes 🫡 we do LoRA, audio resampling & video downsampling to be able to train <40GB VRAM stretch modalities and unfreeze layers as you wish! 🙏🏻 merve/smol-vision
Because hackathons are often the starting point for many AI projects, I've created a Python-backend template incorporating my feedback to streamline collaboration and urgent deployments 🏎️
Within a year, I had the opportunity to participate in hackathons organized by Mistral, OpenAI, and DeepMind and this GitHub template is structured around several fundamental building blocks and recommendations I offer developers eager to participate in their first hackathon, whether as part of a team or individually. Its emphasis is on rapid setup and deployment through: - uv as a package manager, simplifying usage via a series of pre-configured make commands. - FastAPI for API management, structured in a modular architecture designed to minimize branch conflicts during merges to main branches (using minimal health-check and ping routes to verify Docker’s proper execution and backend accessibility on the local network). - Pydantic for validation and type handling, which simplifies debugging and enhances understanding of data objects. - A set of custom instructions tailored for agents (Cline and GitHub Copilot), aimed at improving overall comprehension of the application and optimizing the vibe-coding experience.
This template includes unit tests with a 100% success rate and test coverage, as well as a minimal CI file ensuring that the FastAPI application runs correctly. Thus, merging code that breaks the server into production becomes impossible ⛔️
In general, I would reiterate an essential piece of advice: your two main adversaries are branch conflicts—particularly when the same file is modified concurrently within a brief period, especially if your architecture isn’t built for scalability—and deployment issues under urgent circumstances ⏱️
They have an image tokenizer unified with text, and they de-tokenize using either of two models (LLM and diffusion) The model is actually a full LLM (Qwen2), the tokenizer converts image tokens 🤯
Dataset Viewer for PDFs just landed on Hugging Face 📖🤗 you can now preview all the PDFs easier than before!
on top of this, there's PdfFolder format to load the PDF datasets quicker 💨 > to use it, your dataset should follow a directory format like folder/train/doc1.pdf, folder/train/doc1.pdf > if you want to include bounding boxes, labels etc. you can keep them in a metadata.csv file in the same folder 🤝
