Text Classification
Transformers
Safetensors
qwen3_5
feature-extraction
multilingual
system-one
typed-decisions
decision-model
calibration
probabilistic-classification
vision
multimodal
long-context
african-languages
nigerian-languages
qwen3.5
Eval Results
Instructions to use olaverse/PurpleMIST-Mini-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use olaverse/PurpleMIST-Mini-2.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="olaverse/PurpleMIST-Mini-2.0")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("olaverse/PurpleMIST-Mini-2.0") model = AutoModel.from_pretrained("olaverse/PurpleMIST-Mini-2.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
3 days ago
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Card: multilingual bullet and tag, torch>=2.12
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5 days ago
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Card: languages, harness numbers, sidebar eval results
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Model card; multi-image support in purplemist.py and serve.py
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mini 2.0: full fine-tune of Qwen/Qwen3.5-4B-Base, two-block attention, no truncation (98,304 tokens), vision
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initial commit
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