Trainable Parameters Calculator
Calculate the number of trainable parameters in a model with frozen layers.
About Trainable Parameters Calculator
When fine-tuning, you often freeze most layers and only train a small subset. LoRA typically trains <1% of total parameters. This calculator shows how many parameters are actually updated during training based on the frozen percentage.
Formula
Trainable Params
trainable = total_params × (1 - frozen_pct/100)
- frozen_pct
- Percentage of parameters that are frozen (not updated)
Worked example
With the default values below, Trainable Parameters Calculator returns Trainable Parameters of 350,000,000. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Total Parametersminimum 1
- 7,000,000,000
- Frozen Percentageaccepts 0–100 %
- 95 %
Results
- Trainable Parameters
- 350,000,000
- Frozen Parameters
- 6,650,000,000
- Trainable %
- 5.00%
Step by step
- 1Frozen params= 6,650,000,000
- 2Trainable params= 350,000,000
Assumptions and limits
- The result is derived only from the 2 inputs listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
- Figures are rounded for display to 2 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
Frequently Asked Questions
Why freeze layers?
Freezing pretrained layers reduces compute, memory, and risk of catastrophic forgetting. Only training top layers or adapter modules (LoRA) achieves good results with far less resources.
How does LoRA reduce trainable parameters?
LoRA adds small low-rank matrices alongside frozen weight matrices. With rank=8 on a 7B model, you might only train 0.1% of total parameters while achieving near-full fine-tuning quality.