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Why does my trained PyTorch model give different predictions every time even when I use the same input?
This happens because your model is still running in training mode, which keeps randomness active inside layers like dropout and batch normalization. PyTorch layers behave differently depending on whether the model is in training or evaluation mode. If model.eval() is not called before inference, droRead more
This happens because your model is still running in training mode, which keeps randomness active inside layers like dropout and batch normalization.
PyTorch layers behave differently depending on whether the model is in training or evaluation mode. If
model.eval()is not called before inference, dropout will randomly disable neurons and batch normalization will update running statistics, which makes predictions change on every run even with identical input.The fix is simply to switch the model to evaluation mode before inference:
model.eval()
with torch.no_grad():
output = model(input_tensor)
See lesstorch.no_grad()is important because it prevents PyTorch from tracking gradients, which also reduces memory usage and avoids subtle state changes during inference.Why does my model behave correctly in training but fail after deployment?
This almost always indicates an environment or preprocessing mismatch. Training pipelines often include steps—normalization, tokenization, feature encoding—that are not replicated exactly in production. Even small differences in default parameters can cause large output changes. Verify that the sameRead more
How do I know if my production model is suffering from data drift?
You’ll usually see a gradual drop in real-world accuracy without any changes to the model itself. Data drift occurs when the statistical properties of incoming data change over time. This is common in user behavior models, recommendation systems, and NLP pipelines where language evolves. Start by moRead more
You’ll usually see a gradual drop in real-world accuracy without any changes to the model itself.
Data drift occurs when the statistical properties of incoming data change over time. This is common in user behavior models, recommendation systems, and NLP pipelines where language evolves.
Start by monitoring feature distributions and comparing them to training-time baselines. Sudden shifts in mean, variance, or category frequency are strong indicators. Prediction confidence trends are also useful—models often become less confident before accuracy drops.
If drift is detected, retraining with recent data or introducing adaptive thresholds often restores performance.
Common mistakes:
See lessMonitoring only accuracy, not input features
Using stale validation sets
Ignoring seasonal or regional variations
Why does my training suddenly diverge after increasing learning rate slightly?
Neural networks often have narrow stability windows for learning rates. A small increase can push updates beyond the region where gradients are meaningful, especially in deep or transformer-based models. This causes loss to explode or become NaN within a few steps. Rollback to the last stable rate aRead more
Neural networks often have narrow stability windows for learning rates.
A small increase can push updates beyond the region where gradients are meaningful, especially in deep or transformer-based models. This causes loss to explode or become NaN within a few steps.
Rollback to the last stable rate and introduce a scheduler instead of manual tuning. Warm-up schedules are especially important for large models.
Also verify that mixed-precision training isn’t amplifying numerical errors.
Common mistakes:
Using the same learning rate across architectures
Disabling gradient clipping
Increasing rate without adjusting batch size
When in doubt, stability beats speed.
See lessHow can prompt engineering cause silent failures in LLM applications?
Prompt changes can unintentionally alter task framing, leading to valid but incorrect outputs. LLMs are highly sensitive to instruction wording, ordering, and context length. A prompt that works during testing may fail once additional system messages or user inputs are added. To prevent this, versioRead more
Prompt changes can unintentionally alter task framing, leading to valid but incorrect outputs.
LLMs are highly sensitive to instruction wording, ordering, and context length. A prompt that works during testing may fail once additional system messages or user inputs are added.
To prevent this, version-control prompts and test them with adversarial and edge-case inputs. Keep instructions explicit and avoid mixing multiple objectives in a single prompt.
If outputs suddenly degrade, diff the prompt text before blaming the model.
Common mistakes:
Relying on implicit instructions
Appending user input without separators
Assuming prompts are stable across model versions
Treat prompts as code, not static text.
See lessWhy does my fine-tuned LLM perform worse than the base model?
This happens when fine-tuning introduces noise or bias that overwrites useful pretrained knowledge. The most frequent cause is low-quality or inconsistent fine-tuning data. If your dataset is small, poorly labeled, or stylistically narrow, the model may over-specialize and lose general reasoning abiRead more
This happens when fine-tuning introduces noise or bias that overwrites useful pretrained knowledge.
The most frequent cause is low-quality or inconsistent fine-tuning data. If your dataset is small, poorly labeled, or stylistically narrow, the model may over-specialize and lose general reasoning ability.
Another common issue is using an aggressive learning rate. Large updates can destroy pretrained representations in just a few steps.
To fix this, reduce the learning rate significantly and limit the number of trainable parameters using techniques like LoRA or partial layer freezing. Always evaluate against a held-out baseline prompt set to detect regression early.
Common mistakes:
Fine-tuning on fewer than a few thousand high-quality samples
Not validating against base model outputs
Training for too many epochs
Fine-tuning should nudge behavior, not replace core knowledge.
See lessWhy does my retrained model perform worse on old data?
This is a classic case of catastrophic forgetting. When retraining only on recent data, the model adapts to new patterns while losing performance on older distributions. This is common in incremental learning setups. To fix it, mix a representative sample of historical data into retraining or use reRead more
This is a classic case of catastrophic forgetting.
When retraining only on recent data, the model adapts to new patterns while losing performance on older distributions. This is common in incremental learning setups.
To fix it, mix a representative sample of historical data into retraining or use rehearsal techniques. Regularization toward previous weights can also help.
Common mistakes:
Training only on the latest data window
Assuming more recent data is always better
Dropping legacy edge cases
Retraining should expand knowledge, not replace it.
See lessWhat causes NaN losses during model training?
NaNs usually come from invalid numerical operations. Common sources include division by zero, log of zero, exploding gradients, or invalid input values. In deep models, this often appears after a few unstable updates. Start by enabling gradient clipping and lowering the learning rate. Then check youRead more
NaNs usually come from invalid numerical operations.
Common sources include division by zero, log of zero, exploding gradients, or invalid input values. In deep models, this often appears after a few unstable updates.
Start by enabling gradient clipping and lowering the learning rate. Then check your input data for NaNs or infinities before it enters the model.
If using mixed precision, confirm loss scaling is enabled correctly.
Common mistakes:
Normalizing with zero variance features
Ignoring data validation
Training with unchecked custom loss functions
NaNs are symptoms—fix the instability, not the symptom.
See lessWhy does my model pass offline tests but fail A/B experiments?
Offline metrics often fail to capture real user behavior. In production, user interactions introduce feedback loops, latency constraints, and distribution shifts that static datasets don’t reflect. A model may optimize for offline accuracy but degrade user experience. Instrument live metrics and anaRead more
Offline metrics often fail to capture real user behavior.
In production, user interactions introduce feedback loops, latency constraints, and distribution shifts that static datasets don’t reflect. A model may optimize for offline accuracy but degrade user experience.
Instrument live metrics and analyze segment-level performance. Often the failure is localized to specific cohorts or edge cases.
Common mistakes:
Relying on a single offline metric
Ignoring latency and timeouts
Deploying without gradual rollout
Offline success is necessary but never sufficient.
See lessHow can prompt length cause unexpected truncation?
LLMs have strict context length limits. If system messages, instructions, and user input exceed this limit, earlier tokens are dropped silently. This often removes critical instructions. Always calculate token usage explicitly and reserve space for the response. Truncate user input, not system prompRead more
LLMs have strict context length limits.
If system messages, instructions, and user input exceed this limit, earlier tokens are dropped silently. This often removes critical instructions.
Always calculate token usage explicitly and reserve space for the response. Truncate user input, not system prompts.
Common mistakes:
Assuming character count equals token count
Appending logs or history blindly
Ignoring model-specific context limits
Context budgeting is essential for reliable prompting.
See less