Text Classification
Transformers
lora
fine-tuning
adaptive
research
nested-lora
synaptic-plasticity
rank-adaptation
Instructions to use Simo76/Unified-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Simo76/Unified-LoRA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Simo76/Unified-LoRA")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Simo76/Unified-LoRA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update unified_lora.py
Browse files- unified_lora.py +17 -0
unified_lora.py
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Unified-LoRA Controller
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========================
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Adaptive per-layer rank controller for LoRA fine-tuning.
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"""
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Legacy Adaptive LoRA (Gradient-based)
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====================================
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Early experimental version of adaptive LoRA using gradient-based rank updates.
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This approach adjusts rank per-layer based on gradient norm dynamics.
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However, it suffers from instability and does not provide consistent benefits.
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Replaced by:
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- NestedLoRA (shared orbital architecture)
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- OrbitalController (stress-based closed-loop control)
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This file is kept for reference only.
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Status: deprecated / legacy
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"""
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Unified-LoRA Controller
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========================
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Adaptive per-layer rank controller for LoRA fine-tuning.
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