Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
Chinese
qwen2_vl
image-text-to-text
mteb
Qwen2-VL
vidore
custom_code
Eval Results (legacy)
Instructions to use Alibaba-NLP/gme-Qwen2-VL-2B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Alibaba-NLP/gme-Qwen2-VL-2B-Instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Alibaba-NLP/gme-Qwen2-VL-2B-Instruct", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use Alibaba-NLP/gme-Qwen2-VL-2B-Instruct with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("Alibaba-NLP/gme-Qwen2-VL-2B-Instruct", trust_remote_code=True) model = AutoModelForImageTextToText.from_pretrained("Alibaba-NLP/gme-Qwen2-VL-2B-Instruct", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
| { | |
| "min_pixels": 3136, | |
| "max_pixels": 12845056, | |
| "patch_size": 14, | |
| "temporal_patch_size": 2, | |
| "merge_size": 2, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "image_processor_type": "Qwen2VLImageProcessor", | |
| "processor_class": "Qwen2VLProcessor" | |
| } |