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reacted to sergiopaniego's post with 🤗 12 days ago
So you can now SFT a model with hf jobs + TRL in ONE command lol 🏎️💨 Without worrying about infrastructure since it runs entirely on HF! docs: https://huggingface.co/docs/huggingface_hub/main/en/guides/jobs blog: https://huggingface.co/blog/hf-cli
reacted to prithivMLmods's post with 👍 15 days ago
On the verge of releasing Poseidon-Reasoning-5M, a dataset built to excel in general thought processes, mathematics, and science across a diverse mixture of domains, I’m also dropping the Gargantua-R1-Compact dataset, a collection of over six million high-quality reasoning QA pair traces. 🤗🚀 ✦ Gargantua-R1-Compact : https://huggingface.co/datasets/prithivMLmods/Gargantua-R1-Compact ``` from datasets import load_dataset dataset = load_dataset("prithivMLmods/Gargantua-R1-Compact", split="train") ``` Additionally, I’m adding the mini version of Gargantua — the Gargantua-R1-Wee : https://huggingface.co/datasets/prithivMLmods/Gargantua-R1-Wee ``` from datasets import load_dataset dataset = load_dataset("prithivMLmods/Gargantua-R1-Wee", split="train") ``` The composition spans 73.93% core mathematical reasoning involving problems, proofs, and computational challenges, 12.11% across diverse scientific domains such as physics, chemistry, biology, and interdisciplinary topics, 11.35% in competitive coding covering algorithms and data structures, 1.37% in academic science focusing on research-level methodology, 0.95% in creative and analytical reasoning through logic puzzles and problem-solving tasks, 0.25% in specialized technical areas like MLOps, LLMs, diffusion models, and CUDA, and 0.06% involving data from graphs and charts converted into structured JSON formats. Designed with both rich contextual depth and formal structural clarity, Gargantua-R1-Compact is an optimal resource for advancing research in symbolic reasoning, interpretability, and high-precision question answering in mathematical domains. ✦ Collection : https://huggingface.co/collections/prithivMLmods/gargantua-r1-mod-6896bfd7834e82b89ad2b38b To know more about it, visit the dataset card of the respective dataset. !!
reacted to codelion's post with 🔥 15 days ago
Released 17 production-ready adaptive text classifiers that learn from just 100 examples per class and continuously improve without retraining. These models achieve 93% average accuracy across enterprise use cases like email routing, fraud detection, document classification, and support ticket categorization. Built on ModernBERT with prototype memory and elastic weight consolidation. Key benefits: 90% cost reduction vs API solutions, 90-120ms local inference, dynamic class addition, and zero vendor lock-in. All models available under adaptive-classifier organization. Install with pip install adaptive-classifier. Full technical details: https://huggingface.co/blog/codelion/enterprise-ready-classifiers Code: https://github.com/codelion/adaptive-classifier
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