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AI モデル 84760

• SeaLLMs_-_SeaLLMs-v3-7B-gguf↗

  RichardErkhov

• 38-2012-11↗

  rose33300

• Juggernaut-X-v10↗

  RunDiffusion

  The ground-up retrain — GPT-4V captions, sharper prompt adherence, native text rendering. > Juggernaut X (v10) is a **ground-up retrain** of the Juggernaut SDXL line. Not a fine-tune of v9 — the entire dataset was re-captioned with **GPT-4 Vision**, image classifications were rebuilt from scratch, and the model was trained fresh against an expanded, higher-quality image set. The result: noticeably better prompt adherence, cleaner shot-type control, and native text-rendering capability. - 🧠 **Retrained from the ground up** — not fine-tuned on top of a previous Juggernaut. - 👁️ **GPT-4 Vision captioning** — dataset re-captioned with detailed, structured language descriptions. - 🎯 **Stronger prompt adherence** — does what you ask more often, with less wrestling. - 📸 **Cleaner shot-type classification** — Full Body, Mid Shot, Portrait, etc., now distinct in the model's understanding. - 🔤 **Native text rendering** — handles legible signage, labels, and short phrases directly. - 🖼️ **RunDiffusion Photo** refinement — same photographic backbone that made v9 popular.

• tmax-4b-gguf↗

  samuelchristlie

• EXAONE-3.5-32B-Instruct-GGUF↗

  second-state

• sentence-t5-large↗

  sentence-transformers

  This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks. This model was converted from the Tensorflow model st5-large-1 to PyTorch. When using this model, have a look at the publication: Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models. The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results. The model uses only the encoder from a T5-large model. The weights are stored in FP16.

• FLUX.1-dev-LoRA-AntiBlur↗

  Shakker-Labs

• UL_base_classification↗

  sharmajai901

  This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3125 - Accuracy: 0.8921 The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7

• mit_b3.imagenet↗

  smp-hub

  --- library_name: segmentation-models-pytorch license: other pipeline_tag: image-classification tags: - segmentation-models-pytorch - image-classification - pytorch - mit languages: - python --- This repository contains the `imagenet` pre-trained weights for the `mit_b3` model used as encoder in the segmentation-models-pytorch library.

• FCN-ExecuTorch↗

  software-mansion