Text Classification
Transformers
Safetensors
Laya
English
modernbert
feature-extraction
llm-router
model-routing
agents
ai-agents
tool-risk
guardrails
zero-shot-classification
multiple-choice
calibrated-probabilities
jev
decision-model
calibration
mcp
mcp-server
distillation
text-embeddings-inference
Instructions to use gparitosh007/Open-Decision-Model-150M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gparitosh007/Open-Decision-Model-150M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gparitosh007/Open-Decision-Model-150M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gparitosh007/Open-Decision-Model-150M") model = AutoModel.from_pretrained("gparitosh007/Open-Decision-Model-150M", device_map="auto") - Laya
How to use gparitosh007/Open-Decision-Model-150M with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 598 Bytes
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"padding_side": "right",
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"tokenizer_class": "TokenizersBackend",
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