{"schema_version":"onlylabs.public_signal.v1","title":"Cerebras Model: cerebras/GLM-4.5-Air-REAP-82B-A12B","description":"Cerebras model signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/73c7d80f-06d2-42d9-bb0f-fffefb8b778a","json_url":"https://onlylabs.fyi/signals/73c7d80f-06d2-42d9-bb0f-fffefb8b778a/signal.json","generated_at":"2026-06-11T02:49:54.562955+00:00","org":{"slug":"cerebras","name":"Cerebras","category":"neocloud","category_label":"Neocloud","dossier_url":"https://onlylabs.fyi/labs/cerebras","dossier_json_url":"https://onlylabs.fyi/labs/cerebras/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/73c7d80f-06d2-42d9-bb0f-fffefb8b778a","signal_json":"https://onlylabs.fyi/signals/73c7d80f-06d2-42d9-bb0f-fffefb8b778a/signal.json","source":"https://huggingface.co/cerebras/GLM-4.5-Air-REAP-82B-A12B","lab_dossier":"https://onlylabs.fyi/labs/cerebras","lab_dossier_json":"https://onlylabs.fyi/labs/cerebras/dossier.json","analysis":"https://onlylabs.fyi/analysis/cerebras","analysis_json":"https://onlylabs.fyi/analysis/cerebras/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/cerebras/evidence.json","category":"https://onlylabs.fyi/neoclouds","category_json":"https://onlylabs.fyi/neoclouds.json","category_feed":"https://onlylabs.fyi/neoclouds/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json?category=neocloud","topic":"https://onlylabs.fyi/topics/releases","topic_signals_json":"https://onlylabs.fyi/topics/releases/signals.json?category=neocloud","topic_feed":"https://onlylabs.fyi/topics/releases/feed.xml?category=neocloud","data_business":null},"answer_pack":{"answer":"Cerebras published cerebras/GLM-4.5-Air-REAP-82B-A12B. 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This model was created using **REAP (Router-weighted Expert Activation Pruning)**, a novel expert pruning method that selectively removes redundant experts while preserving the router's independent control over remaining experts. Key features include: - **Near-Lossless Performance**: Maintains almost identical..."},"evidence_pages":[{"url":"https://huggingface.co/cerebras/GLM-4.5-Air-REAP-82B-A12B/raw/main/README.md","final_url":"https://huggingface.co/cerebras/GLM-4.5-Air-REAP-82B-A12B/raw/main/README.md","title":"cerebras/GLM-4.5-Air-REAP-82B-A12B model card","http_status":200,"content_type":"text/plain; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T02:49:54.562955+00:00","bytes":8351,"raw_path":"424b2d3ddf0231ecfade5edad724bc65e1068ae010ec27d23d734b4da8ea519d.md","content_hash":"04b0afb48a02f51e0d895bf2eaae9c259002c1c838adb522ad695b45b81131ea","excerpt_chars":1200,"truncated":true,"excerpt":"--- language: - en library_name: transformers tags: - glm - MOE - pruning - compression license: mit name: cerebras/GLM-4.5-Air-REAP-82B-A12B description: > This model was obtained by uniformly pruning 25% of experts in GLM-4.5-Air using the REAP method. readme: > https://huggingface.co/cerebras/GLM-4.5-Air-REAP-82B-A12B/main/README.md license_link: https://huggingface.co/zai-org/GLM-4.5-Air/blob/main/LICENSE pipeline_tag: text-generation base_model: - zai-org/GLM-4.5-Air --- <p align=\"center\"> <em>𓌳 <strong>REAP</strong>𓌳 the Experts: Why Pruning Prevails for One-Shot MoE Compression</em><br> <img src=\"https://i.imgur.com/rmzG3gg.png\" alt=\"REAP\" width=\"75%\"> </p> GLM-4.5-Air-REAP-82B-A12B ✨ Highlights Introducing **GLM-4.5-Air-REAP-82B-A12B**, a **memory-efficient compressed variant** of GLM-4.5-Air that maintains near-identical performance while being **25% lighter**. 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