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AI モデル 84759

• ZeroSearch_google_v1_Llama_3.2_3B_Instruct↗

  Alibaba-NLP

• Wan2.2-Fun-A14B-Control↗

  alibaba-pai

  Windows 的详细信息： - 操作系统 Windows 10 - python: python3.10 & python3.11 - pytorch: torch2.2.0 - CUDA: 11.8 & 12.1 - CUDNN: 8+ - GPU： Nvidia-3060 12G & Nvidia-3090 24G Linux 的详细信息： - 操作系统 Ubuntu 20.04, CentOS - python: python3.10 & python3.11 - pytorch: torch2.2.0 - CUDA: 11.8 & 12.1 - CUDNN: 8+ - GPU：Nvidia-V100 16G & Nvidia-A10 24G & Nvidia-A100 40G & Nvidia-A100 80G 由于Wan2.2的参数非常大，我们需要考虑显存节省方案，以节省显存适应消费级显卡。我们给每个预测文件都提供了GPU_memory_mode，可以在model_cpu_offload，model_cpu_offload_and_qfloat8，sequential_cpu_offload中进行选择。该方案同样适用于CogVideoX-Fun的生成。

• OlmoEarth-v1_1-Nano↗

  allenai

• Infinity-Instruct-7M-Gen-Llama3_1-70B↗

  BAAI

• finance-sentiment-zh-base↗

  bardsai

• L3.1-MS-Astoria-70b-v2-GGUF↗

  bartowski

• mlabonne_Qwen3-1.7B-abliterated-GGUF↗

  bartowski

  Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: You can either specify a new local-dir (mlabonne_Qwen3-1.7B-abliterated-Q8_0) or download them all in place (./)

• Rombo-Org_Rombo-LLM-V3.1-QWQ-32b-GGUF↗

  bartowski

• Tesslate_Tessa-Rust-T1-7B-GGUF↗

  bartowski

• politicalBiasBERT↗

  bucketresearch