Text Classification
Transformers
ONNX
Safetensors
modernbert
semantic-router
vela
text-embeddings-inference
Instructions to use vllm-sr/Vela-1.0-Encoder-307M-Modality with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/Vela-1.0-Encoder-307M-Modality with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vllm-sr/Vela-1.0-Encoder-307M-Modality")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vllm-sr/Vela-1.0-Encoder-307M-Modality") model = AutoModelForSequenceClassification.from_pretrained("vllm-sr/Vela-1.0-Encoder-307M-Modality", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Release Vela on the unified Encoder base
Verified signature87db865
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Release Vela-1.0-Encoder-307M-Modality with frozen task and native engine evidence
Verified signatureNew text-model release with immutable task weights, original-parent lineage where retained, independent final evaluation, source-pinned training reproduction, frozen LoRA, and FP32 ONNX GPU qualification through 32K. Model card reports source, class and long-context limitations; existing repositories remain unchanged.f4ee346