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AI モデル 84759
- TabPFN-v2-reg↗
Prior-Labs
TabPFN is a transformer-based foundation model for tabular data that leverages prior-data based learning to achieve strong performance on small tabular regression tasks without requiring task-specific training. - **Developed by:** Prior Labs - **Model type:** Transformer-based foundation model for tabular data - **License:** Prior Labs License (Apache 2.0 with additional attribution requirement) - **Paper:** Published in Nature (January 2025) - **Repository:** GitHub - priorlabs/tabpfn 📚 For detailed usage examples and best practices, check out: - Interactive Colab Tutorial
- Carbon-X-GGUF↗
prithivMLmods
- WAI-illustrious-HSWQ_v17.0_base_checkpoint↗
Quiho
- LLaDa-Image-Turbo_ComfyUI↗
realrebelai
This associates the repository with the main LLaDA-Image-Turbo model as a quantized derivative in the Hugging Face model relationship/quantization tree. The custom ComfyUI runtime loads the quantized transformer while preserving the LLaDA-Image transformer architecture. The LLaDA2-MoE text encoder is provided as Q4_K_M GGUF for substantially lower storage/runtime memory requirements than the original full text encoder.
- ideogram-4-gguf↗
rectangleworm
To balance generation quality, memory usage, and inference speed, we recommend the following quantization choices for each component: If you are optimizing for inference speed or trying to fit a specific model entirely into VRAM/RAM, keep these rules of thumb in mind:
- JackChew_-_Qwen2-VL-2B-OCR-gguf↗
RichardErkhov
- lamm-mit_-_PRefLexOR_ORPO_DPO_EXO_REFLECT_10222024-gguf↗
RichardErkhov
- DeepSeek-V4.1-Flash-Abliterated↗
securepeak
- average_word_embeddings_komninos↗
sentence-transformers
- Asclepius-Llama2-7B↗
starmpcc