hsy23 / CLIF

What is CLIF?

CLIF is a Python framework for continuous learning during PEFT serving of LLMs. It runs inference replicas that detect idle GPU slack under load, then kicks off federated adapter fine-tuning without dropping requests. Developers get a production-ready setup with SLO-aware dispatching, dynamic batch sizing, and Excel exports for metrics like throughput, goodput, and GPU usage.

Why is it gaining traction?

Unlike static serving tools, CLIF proactively routes requests to maintain inference bounds while squeezing in PEFT updates via IDLE or COMBINED replicas. Dual-adapter mode trains shadows in parallel to the active one, enabling seamless swaps. The CLI-driven smoke tests and detailed pressure monitoring make tuning straightforward for variable loads.

Who should use this?

ML engineers on GPU clusters serving chat or code LLMs who need online adaptation to drifting data. Teams handling bursty inference—like at cliffhanger-scale apps—want zero-downtime fine-tuning without pausing service. Ideal for continuous learning setups mimicking cliff young's endurance in production.

Verdict

Worth a smoke test for PEFT serving experiments; the 1.0% credibility score and 17 stars signal early maturity, but solid README and metrics output lower the risk. Fork and extend if your cluster idles often—docs guide multi-GPU setup well.

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