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
update readme for instructions for usage with infinity
#39
by michaelfeil - opened
Please merge this PR for documentation update.
Launched on A100-40G, 32GB usage, batch-size=16
INFO 2024-11-12 22:13:40,975 infinity_emb INFO: infinity_server.py:89
Creating 1engines:
engines=['Alibaba-NLP/gte-Qwen2-7B-instruct']
INFO 2024-11-12 22:13:40,979 infinity_emb INFO: Anonymized telemetry.py:30
telemetry can be disabled via environment variable
`DO_NOT_TRACK=1`.
INFO 2024-11-12 22:13:40,987 infinity_emb INFO: select_model.py:64
model=`Alibaba-NLP/gte-Qwen2-7B-instruct` selected,
using engine=`torch` and device=`cuda`
INFO 2024-11-12 22:13:41,188 SentenceTransformer.py:216
sentence_transformers.SentenceTransformer
INFO: Load pretrained SentenceTransformer:
Alibaba-NLP/gte-Qwen2-7B-instruct
INFO 2024-11-12 22:41:25,069 SentenceTransformer.py:355
sentence_transformers.SentenceTransformer
INFO: 1 prompts are loaded, with the keys:
['query']
INFO 2024-11-12 22:41:26,143 infinity_emb INFO: Getting select_model.py:97
timings for batch_size=16 and avg tokens per
sentence=2
2.64 ms tokenization
32.47 ms inference
0.25 ms post-processing
35.36 ms total
embeddings/sec: 452.54
INFO 2024-11-12 22:41:27,721 infinity_emb INFO: Getting select_model.py:103
timings for batch_size=16 and avg tokens per
sentence=513
7.76 ms tokenization
765.84 ms inference
0.53 ms post-processing
774.13 ms total
embeddings/sec: 20.67
thenlper changed pull request status to merged