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Performance depends on network, server availability and workload.", "domain": "Collaborative language-model systems" }, "sources": [ { "citation": "Petals: Collaborative Inference and Fine-tuning of Large Models", "url": "https://arxiv.org/abs/2209.01188v1" } ], "statement": "# Petals: pipeline inference and adaptation across contributed servers\n\nSplit pretrained model blocks across volunteer server chains, with client-managed embeddings, session caches, routing/failover and parameter-efficient adaptation interfaces.\n\n## Scientific scope\n\nAssumes parties trusted with client data and inherits pipeline parallelism/quantization/adapters; not cryptographic privacy or Byzantine correctness. Performance depends on network, server availability and workload.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nAlexander Borzunov; Dmitry Baranchuk; Tim Dettmers; Max Ryabinin; Younes Belkada; Artem Chumachenko; Pavel Samygin; Colin Raffel\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Petals: Collaborative Inference and Fine-tuning of Large Models](https://arxiv.org/abs/2209.01188v1)\n", "summary": "Split pretrained model blocks across volunteer server chains, with client-managed embeddings, session caches, routing/failover and parameter-efficient adaptation interfaces.", "title": "Petals: pipeline inference and adaptation across contributed servers" }, "record_id": "urn:uuid:84942026-dfdc-4ef6-ae21-0ff8b2b697cf", "version": 1, "source_ref": { "record_id": "urn:uuid:84942026-dfdc-4ef6-ae21-0ff8b2b697cf", "version": 1, "content_hash": "sha256:d0b079d988a784c1c6013300ed74412eaf668712d3fb5867c3143ff3b09ef408" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:15f58db9ee4530905f0b03daba55d0067332a473211e450108c33f98da1795c8" }, "target": { "record_id": "urn:uuid:84942026-dfdc-4ef6-ae21-0ff8b2b697cf", "version": 1, "content_hash": "sha256:d0b079d988a784c1c6013300ed74412eaf668712d3fb5867c3143ff3b09ef408" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "borzunov-et-al-petals-collaborative-inference-2022", "entry_id": "borzunov-et-al-petals-collaborative-inference-2022", "revision": 1, "title": "Petals: pipeline inference and adaptation across contributed servers", "context": "Collaborative language-model systems", "track": "Human-designed research method or resource", "band": "B", "modifier": "+", "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-16", "evidence_cutoff": "2026-10-16", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Split pretrained model blocks across volunteer server chains, with client-managed embeddings, session caches, routing/failover and parameter-efficient adaptation interfaces.", "scope": "Split pretrained model blocks across volunteer server chains, with client-managed embeddings, session caches, routing/failover and parameter-efficient adaptation interfaces. Assumes parties trusted with client data and inherits pipeline parallelism/quantization/adapters; not cryptographic privacy or Byzantine correctness. Performance depends on network, server availability and workload.", "evidence_summary": "The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Assumes parties trusted with client data and inherits pipeline parallelism/quantization/adapters; not cryptographic privacy or Byzantine correctness. 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