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
question about quants
#12
by prudant - opened
this kind of "LLM" for embeddings can be quantized, by example to AWQ o GPTQ format?
regards!
Indeed, gte embedding models can be quantized to reduce their computational requirements and memory footprint.
can you give me a little info of how get started with that? wich format, library or useful starting poing please !
i am planning to quantize from 4bytes to 2 bytes so that it is under pgvector's 2k limits. https://jkatz05.com/post/postgres/pgvector-scalar-binary-quantization/
I can report back and see if that works