Text Classification
Transformers
Safetensors
PEFT
branchone
feature-extraction
decision-model
multimodal
custom-code
custom_code
Instructions to use ndemirdemir/BranchOne with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ndemirdemir/BranchOne with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ndemirdemir/BranchOne", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ndemirdemir/BranchOne", trust_remote_code=True, device_map="auto") - PEFT
How to use ndemirdemir/BranchOne with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Commit History
16 days ago
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Add Colab example timing ten questions with one model load
Verified signatureSeparate model loading and warm-up from synchronized prediction timings. Explain process lifetime and the scope of the short-text example.d3ce9ae
18 days ago
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Accept matching adapter transport metadata from Transformers AutoModel
Verified signatureHandle forwarded revision and token metadata while preserving rejection of external adapter selection. Model weights, processor and calibration are unchanged.4f7f1e5 -
Support anonymous and cached AutoModel loading
Verified signatureAccept the matching authentication option Transformers forwards through adapter kwargs. Continue rejecting external adapter overrides. Model weights and inference computations are unchanged.cf5df64 -
Release BranchOne FP16 text and image decision model
Verified signatureApache-2.0 inference package with trained adapters, shared scorer, processor, calibrated probabilities, and standalone usage examples.f78d5a7 -
initial commit
Verified signature13acc74