{"schema_version":"onlylabs.public_signal.v1","title":"Arcee AI Repo: arcee-ai/BlockPruner","description":"Arcee AI repo signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/7055fb42-6821-4f0d-bca8-018a3d784d9d","json_url":"https://onlylabs.fyi/signals/7055fb42-6821-4f0d-bca8-018a3d784d9d/signal.json","generated_at":"2026-06-11T03:17:55.692443+00:00","org":{"slug":"arcee","name":"Arcee AI","category":"neolab","category_label":"Neolab","dossier_url":"https://onlylabs.fyi/labs/arcee","dossier_json_url":"https://onlylabs.fyi/labs/arcee/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/7055fb42-6821-4f0d-bca8-018a3d784d9d","signal_json":"https://onlylabs.fyi/signals/7055fb42-6821-4f0d-bca8-018a3d784d9d/signal.json","source":"https://github.com/arcee-ai/BlockPruner","lab_dossier":"https://onlylabs.fyi/labs/arcee","lab_dossier_json":"https://onlylabs.fyi/labs/arcee/dossier.json","analysis":"https://onlylabs.fyi/analysis/arcee","analysis_json":"https://onlylabs.fyi/analysis/arcee/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/arcee/evidence.json","category":"https://onlylabs.fyi/neolabs","category_json":"https://onlylabs.fyi/neolabs.json","category_feed":"https://onlylabs.fyi/neolabs/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json?category=neolab","topic":null,"topic_signals_json":null,"topic_feed":null,"data_business":null},"answer_pack":{"answer":"Arcee AI published arcee-ai/BlockPruner (Python). This repository signal exposes tooling, eval, infrastructure, or model-adjacent work before it may appear in a launch post. 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This repository signal exposes tooling, eval, infrastructure, or model-adjacent work before it may appear in a launch post. 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It first decomposes each Transformer layer into two minimal residual blocks (MHA or MLP). Then, we use our proposed block importance evaluation metric to assess the importance of each block. Finally, we iteratively prune the block with the lowest importance. <p align=\"center\"> <img src=\"./assets/overview.png\" width=\"80%\"> <br> </p> Evaluation Results we experiment with three series of models:..."},"evidence_pages":[{"url":"https://github.com/arcee-ai/BlockPruner","final_url":"https://github.com/arcee-ai/BlockPruner","title":"arcee-ai/BlockPruner repository metadata","http_status":200,"content_type":"application/json","capture_method":"plain","fetched_at":"2026-06-11T03:17:55.692443+00:00","bytes":11030,"raw_path":"02bd5f257c5a5d399e2cf8393088448fd89f8f3d657a5a164c05c971f9e69ab9.json","content_hash":"49aa0ef7eefcb6da66297d6e84f51736d15ac898fabb7e5318791eca73d80189","excerpt_chars":1200,"truncated":true,"excerpt":"arcee-ai/BlockPruner Description: A block pruning framework for LLMs. Stars: 2 Forks: 3 Open issues: 0 Created: 2024-07-24T17:58:51Z Pushed: 2024-06-20T05:43:28Z Default branch: main Fork: no Archived: no README: <p align=\"center\" width=\"100%\"> </p> <div id=\"top\" align=\"center\"> BlockPruner: Fine-grained Pruning for Large Language Models ----------------------------- <!-- **Authors:** --> _**Longguang Zhong, Fanqi Wan, Ruijun Chen, Xiaojun Quan, Liangzhi Li**_ </div> Contents - [Overview](#overview) - [Evaluation Results](#evaluation_results) - [Quick Start](#quick-start) - [Evaluation](#evaluation) - [Citation](#citation) Overview In this work, we explored the phenomenon of block redundancy in existing LLMs and proposed a general block pruning framework. It first decomposes each Transformer layer into two minimal residual blocks (MHA or MLP). Then, we use our proposed block importance evaluation metric to assess the importance of each block. 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