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Why does my inference latency increase after model optimization?
Some optimizations improve throughput but hurt single-request latency. Batching, quantization, or graph compilation can introduce overhead that only pays off at scale. In low-traffic scenarios, this overhead dominates. Profile latency at realistic request rates and choose optimizations accordingly.Read more
Some optimizations improve throughput but hurt single-request latency.
Batching, quantization, or graph compilation can introduce overhead that only pays off at scale. In low-traffic scenarios, this overhead dominates. Profile latency at realistic request rates and choose optimizations accordingly.
Common mistakes:
Optimizing without workload profiling
Using batch inference for real-time APIs
Ignoring cold-start costs
Optimize for your actual deployment context.
See lessHow do I debug incorrect token alignment in transformer outputs?
Token misalignment usually comes from mismatched tokenizers or improper handling of special tokens. This happens when training and inference use different tokenizer versions or settings. Even a changed vocabulary order can shift outputs. Always load the tokenizer from the same checkpoint as the modeRead more
Token misalignment usually comes from mismatched tokenizers or improper handling of special tokens.
This happens when training and inference use different tokenizer versions or settings. Even a changed vocabulary order can shift outputs.
Always load the tokenizer from the same checkpoint as the model. When post-processing outputs, account for padding, start, and end tokens explicitly.
Common mistakes:
Rebuilding tokenizers manually
Ignoring attention masks
Mixing fast and slow tokenizer variants
Tokenizer consistency is non-negotiable in transformer pipelines.
See lessHow do I detect silent label leakage during training?
Label leakage occurs when future or target information sneaks into input features. This often happens through timestamp misuse, aggregated features, or improperly joined datasets. The model appears highly accurate but fails in production. Audit features for causal validity and simulate prediction usRead more
Label leakage occurs when future or target information sneaks into input features.
This often happens through timestamp misuse, aggregated features, or improperly joined datasets. The model appears highly accurate but fails in production. Audit features for causal validity and simulate prediction using only information available at inference time.
Common mistakes:
Using post-event aggregates
Joining tables without time constraints
Trusting unusually high validation scores
If performance seems too good, investigate.
See lessWhy does my model’s accuracy fluctuate wildly between training runs?
Non-determinism is the usual culprit. Random initialization, data shuffling, parallelism, and GPU kernels all introduce variance. Without controlled seeds, results will differ. Set seeds across libraries and disable non-deterministic operations where possible. Expect some variance, but large swingsRead more
Non-determinism is the usual culprit.
Random initialization, data shuffling, parallelism, and GPU kernels all introduce variance. Without controlled seeds, results will differ.
Set seeds across libraries and disable non-deterministic operations where possible. Expect some variance, but large swings indicate instability.
Common mistakes:
Setting only one random seed
Comparing single-run results
Ignoring hardware differences
Reproducibility requires deliberate configuration
See lessWhy does my fine-tuning job overfit within minutes?
Fast convergence isn’t always a good sign. this usually means the dataset is too small or too repetitive.Large pretrained models can memorize tiny datasets extremely fast. Once memorized, generalization collapses. Reduce epochs, add regularization, or increase dataset diversity. Parameter-efficientRead more
Fast convergence isn’t always a good sign.
this usually means the dataset is too small or too repetitive.Large pretrained models can memorize tiny datasets extremely fast. Once memorized, generalization collapses.
Reduce epochs, add regularization, or increase dataset diversity. Parameter-efficient tuning methods help limit overfitting.
Common mistakes:
See lessTraining full model on small data
Reusing near-duplicate samples
Ignoring validation signals
How do I safely roll out a new model version?
Gradual rollout is the safest approach. Deploy the new model alongside the old one and route a small percentage of traffic to it. Monitor key metrics before increasing exposure. Fallback mechanisms are essential—rollback should be instant and automated. Common mistakes: Full replacement deploymentsRead more
Gradual rollout is the safest approach. Deploy the new model alongside the old one and route a small percentage of traffic to it. Monitor key metrics before increasing exposure.
Fallback mechanisms are essential—rollback should be instant and automated.
Common mistakes:
Full replacement deployments
Missing rollback plans
Monitoring only aggregate metrics
Production models should evolve cautiously
See lessHow can batch size changes affect model convergence?
Batch size directly influences gradient noise and optimization dynamics. Smaller batches introduce stochasticity that can help generalization, while larger batches provide stable but potentially brittle updates. Changing batch size without adjusting learning rate often breaks convergence. If you incRead more
Batch size directly influences gradient noise and optimization dynamics.
Smaller batches introduce stochasticity that can help generalization, while larger batches provide stable but potentially brittle updates.
Changing batch size without adjusting learning rate often breaks convergence. If you increase batch size, scale the learning rate proportionally or use adaptive optimizers.
Common mistakes:
Changing batch size mid-training
Comparing results across different batch regimes
Assuming larger batches are always better
Batch size is a training hyperparameter, not just a performance knob.
See lessWhat causes “CUDA out of memory” errors even with a small batch size?
This usually happens because memory is being accumulated across iterations rather than freed correctly. The most common cause is storing computation graphs unintentionally, often by appending loss tensors or model outputs to a list without detaching them. Over time, GPU memory fills up regardless ofRead more
This usually happens because memory is being accumulated across iterations rather than freed correctly.
The most common cause is storing computation graphs unintentionally, often by appending loss tensors or model outputs to a list without detaching them. Over time, GPU memory fills up regardless of batch size.
Make sure you call
optimizer.zero_grad()every iteration and avoid saving tensors that require gradients. If you need to log values, convert them to scalars using.item().In transformer workloads, sequence length matters more than batch size. A batch of 2 with long sequences can exceed memory limits faster than a batch of 16 with shorter inputs.
Common mistakes:
Forgetting
torch.no_grad()during evaluationLogging full tensors instead of scalars
Increasing max token length without adjusting batch size
Monitoring GPU memory with a profiler will usually reveal the leak within a few iterations.
See lessWhy does my model fail only on edge cases?
Edge cases are often underrepresented during training. The model optimizes for majority patterns and lacks exposure to rare scenarios. This is common in NLP, fraud detection, and vision tasks. Augment training data with targeted edge examples and weight them appropriately. Common mistakes: AssumingRead more
Edge cases are often underrepresented during training. The model optimizes for majority patterns and lacks exposure to rare scenarios. This is common in NLP, fraud detection, and vision tasks. Augment training data with targeted edge examples and weight them appropriately.
Common mistakes:
Assuming edge cases don’t matter
Treating all samples equally
Not logging failure cases
Production failures usually live at the edges.
See lessWhy does my model’s confidence increase while accuracy decreases?
The model is becoming more certain about wrong predictions, often due to overfitting or distribution shift. This is especially common after retraining or fine-tuning on narrow datasets. Measure calibration metrics like expected calibration error (ECE) and inspect confidence histograms. Techniques suRead more
The model is becoming more certain about wrong predictions, often due to overfitting or distribution shift. This is especially common after retraining or fine-tuning on narrow datasets. Measure calibration metrics like expected calibration error (ECE) and inspect confidence histograms. Techniques such as temperature scaling or label smoothing can restore better alignment between confidence and correctness.
Common mistakes:
Equating confidence with correctness
Monitoring accuracy without calibration
Deploying fine-tuned models without recalibration
A trustworthy model knows when it might be wrong.
See less