Sentence Similarity
sentence-transformers
Safetensors
Transformers
qwen2
text-generation
mteb
Qwen2
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use Alibaba-NLP/gte-Qwen2-7B-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Alibaba-NLP/gte-Qwen2-7B-instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Alibaba-NLP/gte-Qwen2-7B-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-Qwen2-7B-instruct with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Alibaba-NLP/gte-Qwen2-7B-instruct", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("Alibaba-NLP/gte-Qwen2-7B-instruct", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Training embadding Issues.
#8
by Imran1 - opened
Hello,
Its great work.
I am facing some issues.
I can train/fine tune embadding model using sentence transformer v3.
But this Alibaba-NLP/gte-Qwen2-7B-instruct are completely different.
If I want to use batch size 128 so it's crashed the GPU even with 4 batch size too.
Are you using peft and lora ?
Are you just training the embadding layer if using lora or peft?
Can you provide a basic code so it will help a lot. May be GitHub reproduce notebook.
Regard
Imran
Thank you for your interest in the GTE model. Yes, we have used peft and lora to train the model.
could you please provide your Lora configuration? Thanks!