[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-90985":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":9,"language":10,"languages":9,"totalLinesOfCode":9,"stars":11,"forks":12,"watchers":13,"openIssues":12,"contributorsCount":12,"subscribersCount":12,"size":12,"stars1d":12,"stars7d":12,"stars30d":12,"stars90d":12,"forks30d":12,"starsTrendScore":12,"compositeScore":14,"rankGlobal":9,"rankLanguage":9,"license":15,"archived":16,"fork":16,"defaultBranch":17,"hasWiki":18,"hasPages":16,"topics":19,"createdAt":9,"pushedAt":9,"updatedAt":20,"readmeContent":9,"aiSummary":9,"trendingCount":12,"starSnapshotCount":12,"syncStatus":21,"lastSyncTime":22,"discoverSource":23},90985,"embedding-vocab-trimmer","tardellirs\u002Fembedding-vocab-trimmer","tardellirs","How to trim an embedding model's vocabulary for a target language — training-free, no GPU. Example: EmbeddingGemma-300M → PT-BR at half the params.",null,"Python",46,0,80,36,"Apache License 2.0",false,"main",true,[],"2026-07-04 04:01:59",2,"2026-07-04 02:30:03","CREATED_QUERY"]