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AI 모델 84760

• T5-large-spell↗

  ai-forever

• MultiIndicQuestionGenerationSS↗

  ai4bharat

• Swift-1.5-Qwen3.8-27B-Uncensored-MTP↗

  ajgazin

• Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF↗

  Alexey777999

• Nova 2.0 Pro Preview (Non-reasoning)↗

  Amazon

• Qwen3.8-2.4T-A95B-Quark-MXFP4↗

  amd

  - **Model Architecture:** Qwen3_5MoeForCausalLM - **Input:** Text - **Output:** Text - **Supported Hardware Microarchitecture:** AMD MI350 / MI355 - **ROCm**: 7.2.0 - **PyTorch**: 2.12.0 - **Transformers**: 5.14.1 - **Operating System(s):** Linux - **Inference Engine:** SGLang/vLLM - **Model Optimizer:** AMD-Quark (v0.12) - **Quantized layers:** router experts - **Weight quantization:** OCP MXFP4, Static - **Activation quantization:** OCP MXFP4, Dynamic The model was quantized from Qwen/Qwen3.8-2.4T-A95B-FP8 using AMD-Quark. The weights and activations of the routed experts are quantized to MXFP4. For further details or issues, please refer to the AMD-Quark documentation or contact the respective developers.

• Qwen2.5-7B-Instruct-Uncensored_GGUF↗

  Andycurrent

• stanford_cars-ConvNeXt-base↗

  anonauthors

  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed]

• french-bge-m3↗

  antoinelouis

  --- pipeline_tag: sentence-similarity language: fr license: mit tags: - passage-retrieval - sentence-similarity - pruned library_name: sentence-transformers base_model: BAAI/bge-m3 base_model_relation: quantized --- This model is a 38.4% smaller version of BAAI/bge-m3 for the French language, created using the mtem-pruner space. This pruned model should perform similarly to the original model for French language tasks with a much smaller memory footprint. However, it may not perform well for other languages present in the original multilingual model as tokens not commonly used in French were removed from the original multilingual model's vocabulary.

• aimv2-large-patch14-224-lit↗

  apple

  We introduce the AIMv2 family of vision models pre-trained with a multimodal autoregressive objective. AIMv2 pre-training is simple and straightforward to train and to scale effectively. Some AIMv2 highlights include: 1. Outperforms OAI CLIP and SigLIP on the majority of multimodal understanding benchmarks. 2. Outperforms DINOv2 on open-vocabulary object detection and referring expression comprehension. 3. Exhibits strong recognition performance with AIMv2-3B achieving *89.5% on ImageNet using a frozen trunk*.