Text Classification
Transformers
Safetensors
multilingual
modernbert
routing
llm-routing
prompt-classification
complexity-estimation
text-embeddings-inference
Instructions to use scombat/s1d1-router with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scombat/s1d1-router with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="scombat/s1d1-router")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("scombat/s1d1-router") model = AutoModelForSequenceClassification.from_pretrained("scombat/s1d1-router", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
3 days ago
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LiteLLM Auto Router classifier server (serve_litellm.py), launchers, tutorial update
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6 days ago
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Loader from the Hub (tokenizer config, vocab map, MLX/ONNX), four tiers, train.py, judge/, card fixes (docs/16)
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Loader from the Hub (tokenizer config, vocab map, MLX/ONNX), four tiers, train.py, judge/, card fixes (docs/16)
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config.json: problem_type regression (raw outputs in the pipeline), task_names, output_layout
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Fill the card with the current model's figures; readable output names; template family keys
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Add the full model weights (PyTorch fp16)
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Card: move the variants section above Quick start
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Card: link the six-source development variants
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Remove compiled Python files
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serve_variant.sh, vocab map support
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Router backends (torch, MLX, ONNX), tokenizers-only tokenization
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serve.py decision log, review_log.py
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Hero image
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Integrations: policy, server, LiteLLM, tutorials (untested parts labelled)
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7 days ago
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Card: four-source data, filled known values
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NOTICE and citation author
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Draft model card, license, notice, usage example (private draft)
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initial commit
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