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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