Failure Modes and Edge Cases in PEFT Systems

Underfitting from Insufficient Capacity: The most common failure mode in LoRA is setting rank too low or targeting too few layers. When you constrain the adapter to rank 4 and only apply it to key and value projections in attention, you might have only 10 to 20 million trainable parameters for a 7B base model. This can be insufficient for complex tasks. Symptom: training loss plateaus early, validation metrics stall at mediocre levels even with more data or longer training. A production team might see their customer support model stuck at 70% intent classification accuracy when full fine tuning achieves 85%. Solution: first, increase rank from 4 to 16 or 32. Second, expand target modules to include all linear layers: query, key, value, output projections in attention, plus up, gate, and down projections in feed forward blocks. This can increase trainable parameters from 20M to 100M, often unlocking 10 to 15 percentage point improvements. If metrics still don't improve, the task may require full fine tuning or the base model may be fundamentally mismatched. Extreme Domain Shift: PEFT assumes the pre trained model already has strong representations and you're making small adjustments. If the domain gap is too large, small adapters fail catastrophically. Example: adapting an English centric model like LLaMA to a low resource language with different script (Thai, Amharic) or grammar structure. You might see the model produce gibberish or fall back to English even after fine tuning. The frozen base embeddings and early layers are tuned for Latin script and English syntax. A 50M parameter LoRA adapter can't overcome this fundamental mismatch. In such cases, either pre train a model on the target language from scratch, or use a multilingual base model that already has some capacity for that language. QLoRA Quantization Brittleness: QLoRA's 4 bit quantization can introduce gradient spikes and training divergence on certain models and tasks. This happens when the weight distribution doesn't match the NormalFloat 4 assumptions, or when task loss landscapes are steep and sensitive to small parameter changes. Real example: a team fine tuning a 30B model for SQL generation with QLoRA sees loss suddenly spike to infinity at step 500, gradients explode, and training crashes. Switching to 8 bit quantization or 16 bit LoRA resolves it, at the cost of requiring a larger GPU or reducing batch size. Monitoring: always track gradient norms during training. If you see norm values suddenly jump from 1.0 to 10.0 or higher, that's a warning sign. Implement gradient clipping (max norm 1.0 to 2.0) as a safety measure. Multi Adapter System Failures: In production systems serving thousands of adapters, operational complexity creates new failure modes. Adapter routing mistakes can send the wrong specialization to users: imagine a customer support request hitting the sales adapter, producing completely irrelevant responses. Cache thrashing becomes a problem when adapter access patterns are unpredictable. If you have 10 GB of GPU memory for adapter cache and 1000 adapters of 50 MB each, you can only keep 200 hot. If traffic patterns cause frequent cache evictions, you spend more time loading adapters from disk (20 to 50 ms per load) than running inference, degrading p99 latency from 300 ms to over 1 second. Version management is tricky: updating an adapter during live traffic can cause race conditions where some requests use version N and others use version N+1, leading to inconsistent user experience. Teams usually implement blue green deployment at the adapter level, routing a percentage of traffic to the new version before full cutover. Overfitting on Tiny Datasets: Because adapters are small and fast to train (hours instead of days), teams sometimes fine tune on datasets with only 100 to 1000 examples. This leads to severe overfitting: training accuracy 98%, but validation accuracy 60%. The model memorizes training examples but doesn't generalize. Mitigation: use strong regularization (weight decay 0.01 to 0.1), aggressive dropout (0.1 to 0.3 on adapter layers), and early stopping based on validation loss. If your dataset is under 1000 examples, consider data augmentation, synthetic data generation, or few shot prompting instead of fine tuning.