Instructions to use ai-forever/FRIDA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ai-forever/FRIDA with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ai-forever/FRIDA") 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 ai-forever/FRIDA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ai-forever/FRIDA")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ai-forever/FRIDA") model = AutoModel.from_pretrained("ai-forever/FRIDA", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Add exported onnx model 'model_qint8_avx512_vnni.onnx'
Verified signatureHello! *This pull request has been automatically generated from the [`export_dynamic_quantized_onnx_model`](https://sbert.net/docs/package_reference/util.html#sentence_transformers.backend.export_dynamic_quantized_onnx_model) function from the Sentence Transformers library.* ## Config ```python QuantizationConfig( is_static=False, format=<QuantFormat.QOperator: 0>, mode=<QuantizationMode.IntegerOps: 0>, activations_dtype=<QuantType.QUInt8: 1>, activations_symmetric=False, weights_dtype=<QuantType.QInt8: 0>, weights_symmetric=True, per_channel=True, reduce_range=False, nodes_to_quantize=[], nodes_to_exclude=[], operators_to_quantize=['Conv', 'MatMul', 'Attention', 'LSTM', 'Gather', 'Transpose', 'EmbedLayerNormalization'], qdq_add_pair_to_weight=False, qdq_dedicated_pair=False, qdq_op_type_per_channel_support_to_axis={'MatMul': 1} ) ``` ## Tip: Consider testing this pull request before merging by loading the model from this PR with the `revision` argument: ```pyth…a53d981
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