Text Classification
Transformers
Safetensors
English
arbit
feature-extraction
decision-model
decisionbench
typed-decisions
calibrated
unsloth
lora
qwen3
custom_code
Eval Results (legacy)
Instructions to use litillabs/arbit-1.7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use litillabs/arbit-1.7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="litillabs/arbit-1.7b", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("litillabs/arbit-1.7b", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Commit History
19 days ago
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Model card: question-type chart split by suite
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Model card: rewrite
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Model card: review corrections; root LICENSE
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Model card: results from the Transformers-adapter run; charts
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20 days ago
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Loader: count_tokens for over-length detection
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Stock-Transformers loader (trust_remote_code); trained files moved to checkpoint/; card updated
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Model card: attribution, adapter status, dataset licenses; relabeled charts
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Arbit 1.7B: weights, decision head, tokenizer, model card
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initial commit
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