AI Training
9 instruments · LoRA fine-tuning · Training dynamics · Catastrophic forgetting detection
9 Tools
LoRA Adapters
Optimizer 3070
Model Diff
TRAINING LOSS
FINDINGS
Final train loss: 0.2636
Final val loss: 0.3721
Train/val gap: 0.1084
Convergence: Mild overfitting risk
Training converged to 0.264 loss over 100 steps. Small train/val divergence. Consider early stopping or dropout regularisation.
All Instruments
LoRA Fine-Tune
Low-rank adaptation fine-tuning with custom dataset
8
164
32
1128
3
120
Training Dynamics
Loss landscape, gradient norm, and learning rate analysis
0.0001
0.0000010.01
Catastrophic Forgetting
EWC-based detection of forgetting on original task distribution
0.4
01
Gradient Checkpoint Profiler
Measures memory savings from gradient checkpointing
4
132
Learning Rate Finder
Smith cyclical LR range test with loss curve
100
20500
Dataset Quality Audit
Perplexity, deduplication, and contamination checks
Checkpoint Delta
Parameter diff between two training checkpoints
Optimizer State Inspector
Inspect AdamW moment estimates and effective learning rates
No configuration required — ready to run.
Training Replay
Replay saved training logs with interactive loss visualization