Observability
MLflow compression tracking
MLflow tracks ML experiments and model versions. Log SuperCompress metrics alongside your model performance metrics.
MLflow integration
import mlflow
from supercompress import Compressor
with mlflow.start_run():
comp = Compressor()
result = comp.compress(context, query)
mlflow.log_metrics({
"original_tokens": result.original_tokens,
"kept_tokens": result.kept_tokens,
"compression_ratio": result.tokens_removed /
max(result.original_tokens, 1),
})Frequently asked questions
Can I track compression in MLflow pipelines?
Yes. Add compression metrics to any pipeline step that calls an LLM.
Does MLflow support compression dashboards?
Yes. Use MLflow's metric comparison UI to track compression over time.
Try it yourself
Paste your long prompt into the playground, ask a question, and see what SuperCompress keeps and removes. Free, no signup needed.