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
Stock-Transformers loader (trust_remote_code); trained files moved to checkpoint/; card updated
133b941 verified Download checkpoint/tokenizer.json from litillabs/arbit-1.7b: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/litillabs/arbit-1.7b/resolve/main/checkpoint/tokenizer.json
- Command line
-
hf download hf://litillabs/arbit-1.7b/checkpoint/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/litillabs/arbit-1.7b/resolve/main/checkpoint/tokenizer.json
11.4 MB
- Xet hash:
- dde3c5a4d468da6fe61f11ffa8f04af2d101f8f4a0fe118c37bd31bb8c3017b6
- Size of remote file:
- 11.4 MB
- SHA256:
- be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
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