Sylva: Sparse Embedded Adapters via Hierarchical Approximate Second-Order Information
Bibliographic record
Abstract
Fine-tuning is the gateway to transferring learned knowledge in a pre-trained Large Language Model (LLM) on many downstream applications. To make LLM fine-tuning more affordable, prior works follow two paths: i) adapters freeze the pre-trained LLM weights and inject a small number of trainable weights during fine-tuning, and ii) pruners remove the less important weights in pre-trained LLMs and train the remaining sparse weights during fine-tuning. We find that the former introduces computation overheads due to the injected trainable parameters, while the latter introduces an expensive pre-processing step to identify the important weights and degrades model quality. To get the best of both worlds, we propose Sylva, a novel LLM fine-tuning procedure that provides high system performance during fine-tuning and attains state-of-the-art model quality on downstream applications. Sylva identifies the most important LLM weights via second-order information in a pre-processing step, and significantly reduces the computation and storage costs of the pre-processing step via i) a hierarchical approximation of second-order information, and ii) an online projection and rediagonalization algorithm. Sylva trains only the sparse important weights and embeds these sparse weights into the pre-trained LLM during fine-tuning to provide high system performance. We show that end-to-end fine-tuning with Sylva is, on average, 5.1 × faster than ZeRO and 1.2 × faster than LoRA, the state-of-the-art adapter approach. Sylva’s hierarchical approximation reduces the peak GPU memory in the pre-processing step by 2.3 × compared to K-FAC, the most widely used approximation to second-order information. The source code of Sylva is publicly available at https://github.com/CentML/Sylva.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".