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AI モデル 85003

• v6-mixed-25k-lower-shared-deu-ell-sequential_interleaved↗

  nikitastheo

• gpt2-greek↗

  nikokons

• Cosmos-Embed1-224p↗

  NVIDIA

  **Cosmos-Embed1** is a joint video-text embedder tailored for physical AI. It can be used for text-to-video retrieval, inverse video search, semantic deduplication, zero-shot and k-nearest-neighbors (kNN) classification, and as a base model for video curation tasks. It has state-of-the-art (SOTA) performance on autonomous vehicle (AV) and robotics datasets, while maintaining competitive performance in general domains. This model is ready for commercial use. The Cosmos-Embed1 release includes the following embedders: * **Cosmos-Embed1** * Cosmos-Embed1-224p (optimized with 8 frames and 224x224 input resolution, 256-dim output text and video embeddings) * Cosmos-Embed1-336p (optimized with 8 frames and 336x336 input resolution, 768-dim output text and video embeddings) * Cosmos-Embed1-448p (optimized with 8 frames and 448x448 input resolution, 768-dim output text and video embeddings) Note, while each checkpoint was optimized at a specific fixed resolution (and default to these), they all support arbitrary non-square resolutions.

• PMRF_blind_face_image_restoration↗

  ohayonguy

• grounding-dino-tiny-ONNX↗

  onnx-community

  https://huggingface.co/IDEA-Research/grounding-dino-tiny with ONNX weights to be compatible with Transformers.js. **Example:** Zero-shot object detection with `onnx-community/grounding-dino-tiny-ONNX` using the `pipeline` API. **Example:** Zero-shot object detection with `onnx-community/grounding-dino-tiny-ONNX` using the `AutoModel` API.

• tiny-mixtral-AWQ-4bit↗

  optimum-intel-internal-testing

• tiny-random-LlamaForCausalLM↗

  optimum-intel-internal-testing

• controlnet-union-promax-sdxl-1.0↗

  OzzyGT

• mdeberta-v3-ud-thai-pud-upos↗

  Pavarissy

• revanimated↗

  philz1337x