Sentence Similarity
sentence-transformers
Safetensors
Transformers
qwen2
text-generation
mteb
Qwen
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use Alibaba-NLP/gte-Qwen1.5-7B-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Alibaba-NLP/gte-Qwen1.5-7B-instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Alibaba-NLP/gte-Qwen1.5-7B-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/gte-Qwen1.5-7B-instruct with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Alibaba-NLP/gte-Qwen1.5-7B-instruct", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("Alibaba-NLP/gte-Qwen1.5-7B-instruct", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
how to load this model to multiple gpus?
#18
by yijiu - opened
I have 4 GPU A10G each one's memory is 24g, but this model needs 26g memory, so I need to load it into at least 2 GPUs at the same time, and I am a beginner at embedding, so I don't know how to do that, could anyone tell me? thank you!
You can use the below code:
model = AutoModel.from_pretrained('Alibaba-NLP/gte-Qwen1.5-7B-instruct', trust_remote_code=True, device_map='auto')