サイト内検索
AI モデル 84760
- NVILA-8B-HD-Video↗
NVIDIA
NVILA-HD-Video is a Multi-modal Large Language Model with 8B parameters that understands and answers questions about videos with up to 4K resolution and 1K frames. Specifically, NVILA-HD-Video uses AutoGaze to reduce redundant patches in a video before running the ViT or LLM. Empirically, AutoGaze can reduce #tokens in in a video by up to 100x, reducing the latency of ViT/LLM by up to 19x/10x. This enables NVILA-HD-Video to efficiently scale to 4K-resolution, 1K-frame videos and achieve improved performance on benchmarks such as VideoMME and state-of-the-art performance on HLVid, a high-resolution long-form video benchmark proposed in this work as well. For more details, see the VILA github repo.
- whisper-small.en_timestamped↗
onnx-community
https://huggingface.co/openai/whisper-small.en with ONNX weights to be compatible with Transformers.js. 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`).
- tiny-random-Qwen2ForCausalLM↗
onnx-internal-testing
- tofu_Llama-2-7b-chat-hf_full↗
open-unlearning
- gpt-oss-safeguard-120b↗
OpenAI
`gpt-oss-safeguard-120b` and `gpt-oss-safeguard-20b` are safety reasoning models built-upon gpt-oss. With these models, you can classify text content based on safety policies that you provide and perform a suite of foundational safety tasks. These models are intended for safety use cases. For other applications, we recommend using gpt-oss models. This model `gpt-oss-safeguard-120b` fits into a single H100 GPU (117B parameters with 5.1B active parameters). Check out `gpt-oss-safeguard-20b` for lower latency (21B parameters with 3.6B active parameters). Both models were trained on our harmony response format and should only be used with the harmony format as it will not work correctly otherwise.
- OpenMed-NER-BloodCancerDetect-SuperClinical-434M↗
OpenMed
**Specialized model for Clinical Entity Recognition - Clinical entities related to Chronic Lymphocytic Leukemia** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for clinical entity recognition - clinical entities related to chronic lymphocytic leukemia. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as **drug interaction detection**, **medication extraction from patient records**, **adverse event monitoring**, **literature mining for drug discovery**, and **biomedical knowledge graph construction** with **production-ready reliability** for clinical and research applications. - **High Precision**: Optimized for biomedical entity recognition - **Domain-Specific**: Trained on curated CLL dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem
- tiny-random-OPTForCausalLM-extended-vocab↗
peft-internal-testing
- **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] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
- unsup-simcse-roberta-base↗
princeton-nlp
- OpenCaption-2B-VL-SFT-v1.0-GGUF↗
prithivMLmods
- Qwen3.8-27B-Uncensored-Q4_K_M-GGUF↗
qaqab