tencent/WeVisOne-VL-2B开源了新模型

tencent/WeVisOne-VL-2B · Hugging Face Hugging Face (https://huggingface.co/) Models (https://huggingface.co/models) Datasets (https://huggingface.co/datasets) Spaces (https://huggingface.co/spaces) Buckets new (https://huggingface.co/storage) Docs (https://huggingface.co/docs) Enterprise (https://huggingface.co/enterprise) Pricing (https://huggingface.co/pricing) Website Tasks (https://huggingface.co/tasks) HuggingChat (https://huggingface.co/chat) Collections (https://huggingface.co/collections) Languages (https://huggingface.co/languages) Organizations (https://huggingface.co/organizations) Community Blog (https://huggingface.co/blog) Posts (https://huggingface.co/posts) Daily Papers (https://huggingface.co/papers) Hardware (https://huggingface.co/hardware) Learn (https://huggingface.co/learn) Discord (https://huggingface.co/join/discord) Forum (https://discuss.huggingface.co/) GitHub (https://github.com/huggingface) Solutions Team & Enterprise (https://huggingface.co/enterprise) Hugging Face PRO (https://huggingface.co/pro) Enterprise Support (https://huggingface.co/support) Inference Providers (https://huggingface.co/inference/models) Inference Endpoints (https://huggingface.co/inference-endpoints) Storage Buckets (https://huggingface.co/storage) Log In (https://huggingface.co/login) Sign Up (https://huggingface.co/join) (https://huggingface.co/tencent) tencent (https://huggingface.co/tencent) / WeVisOne-VL-2B (https://huggingface.co/tencent/WeVisOne-VL-2B) Like 22 Follow Tencent 12.7k Image-Text-to-Text (https://huggingface.co/models?pipeline_tag=image-text-to-text) Transformers (https://huggingface.co/models?library=transformers) Safetensors (https://huggingface.co/models?library=safetensors) qwen3_vl (https://huggingface.co/models?other=qwen3_vl) vision-language (https://huggingface.co/models?other=vision-language) object-detection (https://huggingface.co/models?other=object-detection) referring-expression-comprehension (https://huggingface.co/models?other=referring-expression-comprehension) object-counting (https://huggingface.co/models?other=object-counting) point-grounding (https://huggingface.co/models?other=point-grounding) ocr (https://huggingface.co/models?other=ocr) referring-expression-segmentation (https://huggingface.co/models?other=referring-expression-segmentation) image-matting (https://huggingface.co/models?other=image-matting) conversational (https://huggingface.co/models?other=conversational) License: apache-2.0 Model card (https://huggingface.co/tencent/WeVisOne-VL-2B) Files Files and versions xet (https://huggingface.co/tencent/WeVisOne-VL-2B/tree/main) Community 1 (https://huggingface.co/tencent/WeVisOne-VL-2B/discussions) Deploy Copy to bucket new Use this model Instructions to use tencent/WeVisOne-VL-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started. Libraries Transformers (https://huggingface.co/tencent/WeVisOne-VL-2B?library=transformers) How to use tencent/WeVisOne-VL-2B with Transformers: # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="tencent/WeVisOne-VL-2B") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages) # pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("tencent/WeVisOne-VL-2B") model = AutoModelForMultimodalLM.from_pretrained("tencent/WeVisOne-VL-2B", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the

Tencent tencent/WeVisOne-VL-2B tencent/WeVisOne-VL-2B https://huggingface.co/tencent/WeVisOne-VL-2B