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
Revise README with Tinker run results and observations
Browse filesUpdated validation results and key observations for the Tinker run with Llama-3.2-1B. Improved clarity on stress recovery cycle and added details on reversibility.
README.md
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@@ -17,26 +17,24 @@ The system uses a synaptic control parameter **φ(t)** derived from:
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- **E**: Multi-task error
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- **S**: Memory stability
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```
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φ(t) = f(C, E, S, ΔC, ΔE, ΔS)
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```
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## Validation Results
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### 🧪
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[550] Mode=1 φ=0.371 E_s=0.521 lr=5e-4 (Multi return)
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[700] Mode=1 φ=0.333 E_s=0.500 lr=5e-4 (baseline restored)
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```
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**Key
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### 📊 Standard Benchmark (GLUE MRPC + DistilBERT)
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- **E**: Multi-task error
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- **S**: Memory stability
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φ(t) = f(C, E, S, ΔC, ΔE, ΔS)
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## Validation Results
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### 🧪 Tinker run (Llama-3.2-1B, cloud GPU)
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A controlled run demonstrating a full **stress → recovery cycle** and adaptive mode switching.
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This experiment illustrates the behavior of **φ(t)** under induced instability; it is **not a production deployment claim**.
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[250] Mode=1 φ=0.333 E_s=0.500 lr=5e-4 (Multi stable)
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>>> SHOCK @ step 300
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[350] Mode=2 φ=0.827 E_s=4.979 lr=1e-4 (Mirror activated)
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>>> RECOVERY @ step 500
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[550] Mode=1 φ=0.371 E_s=0.521 lr=5e-4 (Multi return)
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[700] Mode=1 φ=0.333 E_s=0.500 lr=5e-4 (baseline restored)
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**Key observation (this run):**
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φ returns close to its pre-shock regime after recovery (example: 0.33 → 0.83 → 0.33), indicating reversible stress handling within this setup.
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### 📊 Standard Benchmark (GLUE MRPC + DistilBERT)
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