全站搜索
AI 模型 84759
- cats↗
nroggendorff
- GR00T-N1.5-3B↗
NVIDIA
- Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct↗
NVIDIA
- stt_en_fastconformer_transducer_large↗
NVIDIA
- dreamshaper-xl-lightning-dpmsde↗
oguzm
- Minimax-h3_Singularity-x-10Eros-Max_PRISM↗
OliviaRossi
Merge of two Minimax-H3 Ref2VA finetunes with a custom algorithm (**PRISM**, no mergekit): * A: `WarmBloodAban/Minimax-h3_Singularity` / `Minimax-h3_Singularity_ref2va_v2_bf16.safetensors` * B: `TenStrip/10Eros-Max` / `10Eros_Max_h3_8stepTURBO_beta6/7.safetensors` (8-step turbo) Weights: `Minimax-h3_Singularity_x_10Eros-Max_PRISM_b_fp16.safetensors` (fp16). Licence follows the source models - check them before use.
- OpenMed-PII-BigMed-Large-278M-v1↗
OpenMed
- OpenMed-ZeroShot-NER-Organism-Tiny-60M↗
OpenMed
**Specialized model for Species Entity Recognition - Species names from the Species-800 dataset** Optimized for **species identification** in scientific text, covering a wide range of taxa and naming variants.Useful for **ecology studies**, **organism tagging**, and **biocuration**. OpenMed ZeroShot NER is an advanced, domain-adapted Named Entity Recognition (NER) model designed specifically for medical, biomedical, and clinical text mining. Leveraging state-of-the-art zero-shot learning, this model empowers researchers, clinicians, and data scientists to extract expert-level biomedical entities—such as diseases, chemicals, genes, species, and clinical findings—directly from unstructured text, without the need for task-specific retraining.
- Ornith-1.5-35B-A3B-MLX-8bit↗
ornith-ai
Chirp Chirp! 🐦 We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement. Ornith-1.5 extends Ornith-1.0 (which was developed on top of Qwen3.5 and Gemma4 with additional continued pretraining, mid-training, and post-training) by expanding the self-improvement loop from scaffold and rollout optimization to jointly optimizing task generation, scaffold construction, and solution rollouts. Rather than relying on a fixed set of human-curated tasks and manually designed harnesses, Ornith-1.5 continuously generates new training tasks, discovers effective strategies for solving them, and improves the policy through reinforcement learning. For more details on the task, harness, and rollout reward design, please refer to our blog. This model card documents **Ornith-1.5-35B-A3B**, the mid-size mixture-of-experts member of the Ornith-1.5 family. It activates only ~3B parameters per token, yet significantly outperforms its similar-sized peer Qwen 3.6-35B across all coding and agentic benchmarks, and outperforms dense models such as Gemma 4-31B and Muse Glimmer-30B by wide margins on agentic coding.
- PixArt-Sigma-XL-2-1024-MS↗
PixArt-alpha
PixArt-Σ consists of pure transformer blocks for latent diffusion: It can directly generate 1024px, 2K and 4K images from text prompts within a single sampling process. For research purposes, we recommend our `generative-models` Github repository (https://github.com/PixArt-alpha/PixArt-sigma), which is more suitable for both training and inference and for which most advanced diffusion sampler like SA-Solver will be added over time. Hugging Face provides free PixArt-Σ inference. - **Repository:** https://github.com/PixArt-alpha/PixArt-sigma - **Demo:** https://huggingface.co/spaces/PixArt-alpha/PixArt-Sigma > [!IMPORTANT] > Make sure to upgrade diffusers to >= 0.28.0: > > In addition make sure to install `transformers`, `safetensors`, `sentencepiece`, and `accelerate`: > > For `diffusers = 2.0`, you can improve the inference speed by 20-30% with torch.compile. Simple wrap the unet with torch compile before running the pipeline: