capleaf/viXTTS
Text-to-Speech
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Sinisa Stanivuk
Stopwolf
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Multilingual LLMs, STT and TTS models
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Adding `safetensors` variant of this model
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πDeepSeek π is the real OpenAI π―
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Adding `safetensors` variant of this model
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Train 400x faster Static Embedding Models with Sentence Transformers
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Adding `safetensors` variant of this model
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Would you like to get a high-quality dataset to pre-train LLMs in your language? π
At Hugging Face we're preparing a collaborative annotation effort to build an open-source multilingual dataset as part of the Data is Better Together initiative.
Follow the link below, check if your language is listed and sign up to be a Language Lead!
https://forms.gle/s9nGajBh6Pb9G72J6
At Hugging Face we're preparing a collaborative annotation effort to build an open-source multilingual dataset as part of the Data is Better Together initiative.
Follow the link below, check if your language is listed and sign up to be a Language Lead!
https://forms.gle/s9nGajBh6Pb9G72J6
Adding `safetensors` variant of this model
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Iβm recently experimenting with the Flux-Ultra Realism and Real Anime LoRA models, using the Flux.1-dev model as the base. The model and its demo example are provided in the Flux LoRA DLC collections.π
π₯³Demo : π prithivMLmods/FLUX-LoRA-DLC
π₯³Model:
- prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0
- prithivMLmods/Flux-Dev-Real-Anime-LoRA
π₯³For more details, please visit the README.md of the Flux LoRA DLC Space & prithivMLmods/lora-space-collections-6714b72e0d49e1c97fbd6a32
π₯³Demo : π prithivMLmods/FLUX-LoRA-DLC
π₯³Model:
- prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0
- prithivMLmods/Flux-Dev-Real-Anime-LoRA
π₯³For more details, please visit the README.md of the Flux LoRA DLC Space & prithivMLmods/lora-space-collections-6714b72e0d49e1c97fbd6a32
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π£ 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.
1οΈβ£ 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.
2οΈβ£ OpenVINO Backend: This backend uses Intel their OpenVINO instead, outperforming ONNX in some situations on CPU.
Usage is as simple as
π 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:
1οΈβ£ via Model2Vec, a new technique for distilling any Sentence Transformer models into static embeddings. Either via a pre-distilled model with
2οΈβ£ 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
1οΈβ£ 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.
2οΈβ£ 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 ππ 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:
1οΈβ£ 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.2οΈβ£ 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
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