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AI モデル 84755
- FLUX.1-schnell-GGUF↗
unsloth
`FLUX.1 [schnell]` is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. For more information, please read our blog post. 1. Cutting-edge output quality and competitive prompt following, matching the performance of closed source alternatives. 2. Trained using latent adversarial diffusion distillation, `FLUX.1 [schnell]` can generate high-quality images in only 1 to 4 steps. 3. Released under the `apache-2.0` licence, the model can be used for personal, scientific, and commercial purposes. We provide a reference implementation of `FLUX.1 [schnell]`, as well as sampling code, in a dedicated github repository. Developers and creatives looking to build on top of `FLUX.1 [schnell]` are encouraged to use this as a starting point. The FLUX.1 models are also available via API from the following sources - bfl.ml (currently `FLUX.1 [pro]`) - replicate.com - fal.ai - mystic.ai `FLUX.1 [schnell]` is also available in Comfy UI for local inference with a node-based workflow. To use `FLUX.1 [schnell]` with the 🧨 diffusers python library, first install or upgrade diffusers To learn more check out the diffusers documentation --- - This model is not intended or able to provide factual information. - As a statistical model this checkpoint might amplify existing societal biases. - The model may fail to generate output that matches the prompts. - Prompt following is heavily influenced by the prompting-style.
- Phi-4-reasoning-GGUF↗
unsloth
- VersaViT↗
腾讯
- Sakura-Qwen-Image-2.1-Turbo-GGUF↗
webmp3
- toxic-bert↗
Xenova
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
- MiMo-Embodied-7B↗
XiaomiMiMo
- Zarya-0.6B↗
ai-forever
- flux-ghibsky-illustration↗
aleksa-codes
- longformer-scico↗
allenai
This model is the `unified` model discussed in the paper SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts (AKBC 2021) that formulates the task of hierarchical cross-document coreference resolution (H-CDCR) as a multiclass problem. The model takes as input two mentions `m1` and `m2` with their corresponding context and outputs 4 scores: * 0: not related * 1: `m1` and `m2` corefer * 2: `m1` is a parent of `m2` * 3: `m1` is a child of `m2`. We provide the following code as an example to set the global attention on the special tokens: ` `, ` ` and ` `.
- Molmo2-8B↗
allenai
Molmo2 is a family of open vision-language models developed by the Allen Institute for AI (Ai2) that support image, video and multi-image understanding and grounding. Molmo2 models are trained on publicly available third party datasets as referenced in our technical report and Molmo2 data, a collection of datasets with highly-curated image-text and video-text pairs. It has state-of-the-art performance among multimodal models with a similar size. You can find all models in the Molmo2 family here. **Learn more** about the Molmo2 family in our announcement blog post. Molmo2-8B is based on Qwen3-8B and uses SigLIP 2 as vision backbone. It outperforms others in the class of open weight and data models on short videos, counting, and captioning, and is competitive on long-videos.