Sentence Similarity
sentence-transformers
Safetensors
Transformers
qwen2
text-generation
mteb
Qwen2
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use starsy/gte-Qwen2-7B-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use starsy/gte-Qwen2-7B-instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("starsy/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 starsy/gte-Qwen2-7B-instruct with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("starsy/gte-Qwen2-7B-instruct", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("starsy/gte-Qwen2-7B-instruct", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- config.json +1 -0
- sentence_bert_config.json +1 -1
config.json
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"transformers_version": "4.41.2",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151646
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}
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"transformers_version": "4.41.2",
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"use_cache": true,
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"use_sliding_window": false,
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"is_causal": false,
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"vocab_size": 151646
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}
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sentence_bert_config.json
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{
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"max_seq_length": 32768,
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"do_lower_case": false
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}
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{
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"max_seq_length": 32768,
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"do_lower_case": false
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}
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