Feature Extraction
sentence-transformers
OpenVINO
Transformers
Chinese
bert
sentence-similarity
nncf
8-bit precision
text-embeddings-inference
Instructions to use turingevo/bge-base-zh-v1.5-openvino-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use turingevo/bge-base-zh-v1.5-openvino-8bit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("turingevo/bge-base-zh-v1.5-openvino-8bit") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use turingevo/bge-base-zh-v1.5-openvino-8bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="turingevo/bge-base-zh-v1.5-openvino-8bit")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("turingevo/bge-base-zh-v1.5-openvino-8bit") model = AutoModel.from_pretrained("turingevo/bge-base-zh-v1.5-openvino-8bit") - Notebooks
- Google Colab
- Kaggle
Upload openvino_config.json with huggingface_hub
Browse files- openvino_config.json +27 -0
openvino_config.json
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{
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"dtype": "int8",
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"input_info": null,
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"optimum_version": "1.25.2",
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"quantization_config": {
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"all_layers": null,
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"backup_precision": null,
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"bits": 8,
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"dataset": null,
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"dtype": "int8",
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"gptq": null,
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"group_size": -1,
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"ignored_scope": null,
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"lora_correction": null,
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"num_samples": null,
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"processor": null,
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"quant_method": "default",
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"ratio": 1.0,
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"scale_estimation": null,
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"sensitivity_metric": null,
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"sym": false,
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"tokenizer": null,
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"trust_remote_code": false
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},
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"save_onnx_model": false,
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"transformers_version": "4.46.3"
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}
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