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
About the training set
#11
by hzf666 - opened
Thanks for great work. I'm confused about the training data. In the paper, it is said that train gme by 8m data. But the in the script, it seems like that you are using TextCaps(https://huggingface.co/datasets/HuggingFaceM4/TextCaps).
It seems they provide an example for fine-tuning the trained gme model on textcaps.