Text Classification
PEFT
Safetensors
English
stance-detection
stance-classification
argument-mining
computational-social-science
llama
lora
wiba
Instructions to use armaniii/WIBA-Stance-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use armaniii/WIBA-Stance-V1 with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("meta-llama/Llama-2-7b-hf") model = PeftModel.from_pretrained(base_model, "armaniii/WIBA-Stance-V1") - Notebooks
- Google Colab
- Kaggle
Commit History
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Model card v3: step-by-step gated-access walkthrough, separate GPU/CPU quickstarts with hardware requirements, batch processing with tqdm progress bar
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Model card v2: empirically verified instructions (tested on transformers 5.12/peft 0.19 and 4.38/0.7.1), corrected storage-format description, verified example outputs, dependency matrix
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Update tokenizer.json (modern PEFT format conversion)
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Convert adapter to modern PEFT format (merged trained score head). Verified logit-equivalent to the PEFT 0.7.1 original on both transformers 5.12/peft 0.19 and transformers 4.38/peft 0.7.1 stacks. Original-format files remain in git history.
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Update adapter_config.json (modern PEFT format conversion)
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Add complete model card: pipeline overview, adapter/full-model explanation, download and usage instructions with exact prompt formats and label mappings
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Upload complete local adapter: fp32 weights incl. trained score base_layer, tokenizer files, and PEFT model card
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Upload LlamaForSequenceClassification
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initial commit
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