// use cases
Where the data pipeline is the bottleneck
fast-axolotl is one idea — accelerate the data pipeline — but it shows up differently depending on where your wall-clock goes. Here are the four situations where teams feel it.
// out-of-memory
Fix Axolotl RAM OOM on large datasets
→Stock Axolotl preloads the whole dataset into RAM. Swap the reader for fast-axolotl streaming and bound peak memory by batch size, not dataset size.
// data-bound throughput
Speed up the Axolotl data pipeline
→When wall-clock is bound by reading and deduplicating data, fast-axolotl swaps two hot paths for Rust: 77x Parquet streaming and 1.9x parallel SHA256 dedup.
// preprocessing
High-throughput fine-tuning data prep
→Prepare large fine-tuning corpora faster: stream reads and deduplicate in Rust so the preprocessing pass keeps up with your training schedule.
// continuous integration
Fast dataset validation in CI
→Validate and deduplicate training data in CI without loading it all into RAM. Stream large files and hash rows in parallel on the runner.