Text Classification
Transformers
Safetensors
English
mistral3
image-text-to-text
decision-model
typed-decisions
schema-head
jev
calibration
decode-free
Instructions to use StandardThinking/StandardOne-3B-SH with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StandardThinking/StandardOne-3B-SH with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="StandardThinking/StandardOne-3B-SH")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("StandardThinking/StandardOne-3B-SH") model = AutoModelForMultimodalLM.from_pretrained("StandardThinking/StandardOne-3B-SH", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
18 days ago
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Update model card (v4)
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Update weights (v4)
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23 days ago
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docs: replace examples with inventory and verification workflows
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24 days ago
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docs: download snapshot before installing its requirements
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docs: add operational examples and link llama.cpp SH runner
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27 days ago
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v3 card: benchmark charts from the Decision Index guide
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v3 card: correct the long-document and JevBench hard numbers (clean re-run of the released weights)
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v3 card: FP8 and GGUF repos now hold v3
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v3 card: Results (Decision Index 0.3, HoVer, BRIGHT, long-document set, JevBench public)
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v3: further training on synthetic decision data (Decision Index 0.3 public index, self-scored)
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30 days ago
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Model card: fast serving with the Standard One engine (open-source SGLang fork)
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Standard One 3B SH v2: hard decision tasks (JevBench hard 54.05 -> 63.96)
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Standard One 3B SH v1
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initial commit
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