Instructions to use BAAI/llm-embedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BAAI/llm-embedder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BAAI/llm-embedder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BAAI/llm-embedder") model = AutoModel.from_pretrained("BAAI/llm-embedder", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Add exported onnx model 'model_O2.onnx'
Verified signatureHello! *This pull request has been automatically generated from the [`export_optimized_onnx_model`](https://sbert.net/docs/package_reference/util.html#sentence_transformers.backend.export_optimized_onnx_model) function from the Sentence Transformers library.* ## Config ```python OptimizationConfig( optimization_level=2, optimize_for_gpu=False, fp16=False, optimize_with_onnxruntime_only=None, enable_transformers_specific_optimizations=True, disable_gelu=None, disable_gelu_fusion=False, disable_layer_norm=None, disable_layer_norm_fusion=False, disable_attention=None, disable_attention_fusion=False, disable_skip_layer_norm=None, disable_skip_layer_norm_fusion=False, disable_bias_skip_layer_norm=None, disable_bias_skip_layer_norm_fusion=False, disable_bias_gelu=None, disable_bias_gelu_fusion=False, disable_embed_layer_norm=True, disable_embed_layer_norm_fusion=True, enable_gelu_approximation=False, use_mask_index=False, no_attention_mask=False, disable_shape_infere…82e74ad