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LoRA Speedrun – a public wall-clock leaderboard for fine-tuning techniques

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Why This Matters

The LoRA Speedrun introduces a public leaderboard that tracks the fastest wall-clock times for fine-tuning language models using LoRA techniques on standardized tasks. By providing a transparent, verifiable, and accessible platform, it encourages innovation and competitive optimization in model fine-tuning, benefiting both researchers and developers. This initiative highlights the importance of efficiency in AI training, making advanced fine-tuning more accessible and fostering rapid progress in the industry.

Key Takeaways

LoRA Speedrun 🏁

How fast can you LoRA-fine-tune Qwen2.5-1.5B to β‰₯ 57% on GSM8K β€” on a single L40S?

This is modded-nanogpt for fine-tuning: a frozen task, frozen hardware, and a public leaderboard of wall-clock records. Every record is independently re-run 3Γ— with fresh seeds on identical hardware before it counts.

Attempting and verifying are free: official timing runs on a Modal L40S sandbox, and Modal's free monthly compute credits cover full runs β€” so anyone can compete, and anyone can re-verify any record with one command.

Leaderboard

Track 1 β€” GSM8K Β· Qwen2.5-1.5B Β· target β‰₯ 57.0% Β· 1Γ— L40S

Current record: 6m 05s by @Saivineeth147 β€” Sequence packing + completion-only loss masking, 2 epochs. Same LoRA config as #0; ~2x faster at higher accuracy.

# Date Author Train time GSM8K/EM Ξ” Technique 0 2026-07-18 @Saivineeth147 11m 57s 59.4% β€” Baseline: plain LoRA r=16 on all linear layers, 3 epochs, cosine LR. No tricks. (report) 1 2026-07-18 @Saivineeth147 6m 05s 61.1% βˆ’49% Sequence packing + completion-only loss masking, 2 epochs. Same LoRA config as #0; ~2x faster at higher accuracy. (report)

Track 2 β€” SQuAD v1.1 Β· SmolLM2-1.7B Β· target β‰₯ 75.5% Β· 1Γ— L40S

Current record: none yet β€” this track is open for its first record.

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