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AI 模型 84759

• Iambe-RP-DARE-20B-DENSE-GGUF↗

  TheBloke

• convnext_base.fb_in1k↗

  timm

• samvit_large_patch16.sa1b↗

  timm

• swin_tiny_patch4_window7_224.ms_in1k↗

  timm

  A Swin Transformer image classification model. Pretrained on ImageNet-1k by paper authors. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 28.3 - GMACs: 4.5 - Activations (M): 17.1 - Image size: 224 x 224 - **Papers:** - Swin Transformer: Hierarchical Vision Transformer using Shifted Windows: https://arxiv.org/abs/2103.14030 - **Original:** https://github.com/microsoft/Swin-Transformer - **Dataset:** ImageNet-1k Explore the dataset and runtime metrics of this model in timm model results.

• distilbert-base-uncased-mnli↗

  typeform

  - Model Details - How to Get Started With the Model - Uses - Risks, Limitations and Biases - Training - Evaluation - Environmental Impact **Model Description:** This is the uncased DistilBERT model fine-tuned on Multi-Genre Natural Language Inference (MNLI) dataset for the zero-shot classification task. - **Developed by:** The Typeform team. - **Model Type:** Zero-Shot Classification - **Language(s):** English - **License:** Unknown - **Parent Model:** See the distilbert base uncased model for more information about the Distilled-BERT base model. **CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**

• UI-TARS-2B-SFT↗

  字节跳动

• Qwen2.5-Coder-7B-Instruct-bnb-4bit↗

  unsloth

  We have a Qwen 2.5 (all model sizes) free Google Colab Tesla T4 notebook. Also a Qwen 2.5 conversational style notebook. All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. - This conversational notebook is useful for ShareGPT ChatML / Vicuna templates. - This text completion notebook is for raw text. This DPO notebook replicates Zephyr. - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.

• Xing4.0-29B-A4B-Q4_K_M↗

  WearItAIteam

• 12bgguf↗

  Abc7347

• colSmol-256M-merged↗

  akshayballal