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reacted to oldhag88's post with šŸ‘€ about 5 hours ago
Try Meze-pic TRAIN for organizing images for machine learning: https://train.mezepic.com Features: => Edit captions, tags, and ratings directly in the website, or use API access for programmatic access. => Batch import images and captions, in multiple versions, with unlimited storage in the cloud. => 5,000 images free storage (0.5 GB free storage @ 100 KB/image) => Generate DINOv3 image embeddings with one click in the cloud => Attach custom metadata to your images. Use image embeddings to visualize and predict tags and scores across your datasets https://train.mezepic.com
reacted to anakin87's post with šŸ˜Ž about 5 hours ago
šŸŒ€ Let LLMs wander - Engineering RL Environments Reinforcement Learning Environments are little worlds where models can act, get rewards, and learn. I've been exploring how to design them, figuring out what works and what doesn't. If you want to learn how to build them, I recorded a practical intro video. You'll also see how to turn Liquid AI LFM2-2.6B into a Tic-tac-toe master šŸ™‚ šŸŽ„ Engineering RL Environments video: https://www.youtube.com/watch?v=71V3fTaUp2Q --- šŸŒ± LLM RL Environments Lil Course: https://github.com/anakin87/llm-rl-environments-lil-course šŸ¤—šŸ•¹ļø Play against the trained model: https://huggingface.co/spaces/anakin87/LFM2-2.6B-mr-tictactoe šŸ“š HF collection (datasets + models): https://huggingface.co/collections/anakin87/lfm2-26b-mr-tic-tac-toe
upvoted an article about 5 hours ago
"Darwin-27B-Opus: Surpassing the Foundation Model Without Training"
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