{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/svft-parameter-efficient-fine-tuning-with","title":"SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors","arxiv_id":"2405.19597","date":"2024-05-30","proceeding":null,"authors":["Vijay Lingam","Atula Tejaswi","Aditya Vavre","Aneesh Shetty","Gautham Krishna Gudur","Joydeep Ghosh","Alex Dimakis","Eunsol Choi","Aleksandar Bojchevski","Sujay Sanghavi"],"abstract":"Popular parameter-efficient fine-tuning (PEFT) methods, such as LoRA and its variants, freeze pre-trained model weights \\(W\\) and inject learnable matrices \\(\\Delta W\\). These \\(\\Delta W\\) matrices are structured for efficient parameterization, often using techniques like low-rank approximations or scaling vectors. However, these methods typically show a performance gap compared to full fine-tuning. Although recent PEFT methods have narrowed this gap, they do so at the cost of additional learnable parameters. We propose SVFT, a simple approach that fundamentally differs from existing methods: the structure imposed on \\(\\Delta W\\) depends on the specific weight matrix \\(W\\). Specifically, SVFT updates \\(W\\) as a sparse combination of outer products of its singular vectors, training only the coefficients (scales) of these sparse combinations. This approach allows fine-grained control over expressivity through the number of coefficients. Extensive experiments on language and vision benchmarks show that SVFT recovers up to 96% of full fine-tuning performance while training only 0.006 to 0.25% of parameters, outperforming existing methods that only recover up to 85% performance using 0.03 to 0.8% of the trainable parameter budget.","url_abs":"https://arxiv.org/abs/2405.19597v1","url_pdf":"https://arxiv.org/pdf/2405.19597v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"svft-parameter-efficient-fine-tuning-with","repo_url":"https://github.com/vijaylingam95/svft","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"parameter-efficient-fine-tuning","task_name":"parameter-efficient fine-tuning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2405.19597","atlas_url":"https://app.syntology.ai/?focus=2405.19597","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.19597"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/vijaylingam95/svft","reach":null}],"summary":{"ran_honours":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"aa4c428037088270","entry":"evaluate","repo":"vijaylingam95/svft","repo_kind":"official","path":"LLM-Adapters/multi_dataset_eval.py","file_url":"https://github.com/vijaylingam95/svft/blob/HEAD/LLM-Adapters/multi_dataset_eval.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"aa4c428037088270"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}