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-7B-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-7B-Instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Alibaba-NLP/gme-Qwen2-VL-7B-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-7B-Instruct with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Alibaba-NLP/gme-Qwen2-VL-7B-Instruct", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("Alibaba-NLP/gme-Qwen2-VL-7B-Instruct", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d89a7e85abbec00d5aeb7899f4be12513bcde323439accba85aeec63e3e75f7
- Size of remote file:
- 4.05 GB
- SHA256:
- c494191f68029820aba82a39b60f027531e74ab4738229fdd1ca0d0721d6edbc
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