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AI 模型 84760

• supergemma4-e4b-abliterated-GGUF↗

  Abiray

• pii-detection↗

  adaptive-classifier

• KIMODO-Meta3_llm2vec_NF4↗

  Aero-Ex

• TinyLlama-1.1B-Chat-v1.0-x2-MoE↗

  AIChenKai

• Swift-1.5-Qwen3.8-27B-Uncensored-NVFP4-NInfer↗

  ajgazin

• Bolmo-7B↗

  allenai

• OLMo-2-0325-32B↗

  allenai

  We introduce OLMo 2 32B, the largest model in the OLMo 2 family. OLMo 2 was pre-trained on OLMo-mix-1124 and uses Dolmino-mix-1124 for mid-training. OLMo 2 is the latest in a series of **O**pen **L**anguage **Mo**dels designed to enable the science of language models. We have released all code, checkpoints, logs, and associated training details on GitHub. The quantized model is more sensitive to data types and CUDA operations. To avoid potential issues, it's recommended to pass the inputs directly to CUDA using:

• chronos-t5-base↗

  amazon

  🚀 **Update Feb 14, 2025**: Chronos-Bolt & original Chronos models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. 🚀 **Update Nov 27, 2024**: We have released Chronos-Bolt⚡️ models that are more accurate (5% lower error), up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. Check out the new models here. Chronos is a family of **pretrained time series forecasting models** based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes.

• Qwen3.8-27B-ABLITERATED-GGUF↗

  asdasdadadsss

• G2-9B-Sugarquill-v0-GGUF↗

  bartowski