Open-weight on-device AI models for private, low-latency edge intelligence
Best for: Developers building on-device copilots and local assistants needing private, low-latency AI, Enterprises deploying AI on edge hardware for privacy-sensitive workflows
Open-source instruction-following LLM you can fine-tune in 30 minutes on a single A100 GPU
Best for: Researchers experimenting with LLM fine-tuning on limited compute, Developers needing a commercially safe open-source LLM for demos and prototypes
Open-source bilingual Chinese-English 7B LLM for text generation on Hugging Face
Best for: Researchers needing a lightweight bilingual baseline for Chinese-English NLP experiments, Developers prototyping bilingual text generation on consumer GPUs (e.g., RTX 3090)
Free Korean BERT for noisy comment text and informal language
Best for: Korean NLP researchers studying noisy comment-level text, Developers building sentiment analysis or toxicity detection for Korean social media
Ultra-fast open-source small language models for edge inference
Best for: Developers building lightweight local models for Raspberry Pi or thin laptops, Researchers exploring dynamic algorithms for small language model training
Open-source YAML-based deep learning framework for building, fine-tuning, and deploying multi-modal AI models.
Best for: ML engineers who want to quickly prototype and deploy multi-modal models without writing training loops, Data scientists needing a no-boilerplate framework for LLM fine-tuning and alignment (SFT, DPO, GRPO)
Train custom AI models on your proprietary data, keep learning in production, and own the weights.
Best for: Organizations with substantial proprietary data needing custom models they fully own, Teams that need a model to keep improving in production as their product changes
