Tuan Tran

tuantm

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liked a dataset 6 days ago

microsoft/orca-agentinstruct-1M-v1

liked a model 13 days ago

tablegpt/TableGPT2-7B

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facebook/MobileLLM-1B

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liked a dataset 6 days ago

microsoft/orca-agentinstruct-1M-v1

Viewer • Updated 24 days ago • 1.05M • 2.68k • 349

liked a model 13 days ago

tablegpt/TableGPT2-7B

Updated 10 days ago • 1.83k • 108

liked 2 models 23 days ago

facebook/MobileLLM-1B

Text Generation • Updated 23 days ago • 9.64k • 108

facebook/MobileLLM-125M

Text Generation • Updated 23 days ago • 7.62k • 85

liked a model 25 days ago

HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1

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Marqo/marqo-ecommerce-embeddings-L

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Reacted to tomaarsen's post with 🔥 about 1 month ago

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📣 Sentence Transformers v3.2.0 is out, marking the biggest release for inference in 2 years! 2 new backends for embedding models: ONNX (+ optimization & quantization) and OpenVINO, allowing for speedups up to 2x-3x AND Static Embeddings for 500x speedups at 10-20% accuracy cost.

1️⃣ ONNX Backend: This backend uses the ONNX Runtime to accelerate model inference on both CPU and GPU, reaching up to 1.4x-3x speedup depending on the precision. We also introduce 2 helper methods for optimizing and quantizing models for (much) faster inference.
2️⃣ OpenVINO Backend: This backend uses Intel their OpenVINO instead, outperforming ONNX in some situations on CPU.

Usage is as simple as SentenceTransformer("all-MiniLM-L6-v2", backend="onnx"). Does your model not have an ONNX or OpenVINO file yet? No worries - it'll be autoexported for you. Thank me later 😉

🔒 Another major new feature is Static Embeddings: think word embeddings like GLoVe and word2vec, but modernized. Static Embeddings are bags of token embeddings that are summed together to create text embeddings, allowing for lightning-fast embeddings that don't require any neural networks. They're initialized in one of 2 ways:

1️⃣ via Model2Vec, a new technique for distilling any Sentence Transformer models into static embeddings. Either via a pre-distilled model with from_model2vec or with from_distillation where you do the distillation yourself. It'll only take 5 seconds on GPU & 2 minutes on CPU, no dataset needed.
2️⃣ Random initialization. This requires finetuning, but finetuning is extremely quick (e.g. I trained with 3 million pairs in 7 minutes). My final model was 6.6% worse than bge-base-en-v1.5, but 500x faster on CPU.

Full release notes: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.2.0
Documentation on Speeding up Inference: https://sbert.net/docs/sentence_transformer/usage/efficiency.html