{"schema_version":"onlylabs.public_signal.v1","title":"Cerebras Model: cerebras/Step-3.5-Flash-REAP-149B-A11B","description":"Cerebras model signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/8e156f68-4602-4639-a2fe-608bfd8b83af","json_url":"https://onlylabs.fyi/signals/8e156f68-4602-4639-a2fe-608bfd8b83af/signal.json","generated_at":"2026-06-11T02:49:43.025984+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/8e156f68-4602-4639-a2fe-608bfd8b83af","signal_json":"https://onlylabs.fyi/signals/8e156f68-4602-4639-a2fe-608bfd8b83af/signal.json","source":"https://huggingface.co/cerebras/Step-3.5-Flash-REAP-149B-A11B","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/Step-3.5-Flash-REAP-149B-A11B. 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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 accuracy on code generation, agentic..."},"evidence_pages":[{"url":"https://huggingface.co/cerebras/Step-3.5-Flash-REAP-149B-A11B/raw/main/README.md","final_url":"https://huggingface.co/cerebras/Step-3.5-Flash-REAP-149B-A11B/raw/main/README.md","title":"cerebras/Step-3.5-Flash-REAP-149B-A11B model card","http_status":200,"content_type":"text/plain; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T02:49:43.025984+00:00","bytes":6236,"raw_path":"2968c7503f91cc905c27c2edb5c39a0c6807cbd679d12f1733ce8298f3c527d5.md","content_hash":"373129cf6b45fa32eff72483f34e6d338c5b283a2ce155606f21a60a482ecb8b","excerpt_chars":1200,"truncated":true,"excerpt":"--- language: - en library_name: transformers tags: - stepfun - MOE - pruning - compression license: other name: cerebras/Step-3.5-Flash-REAP-149B-A11B description: > This model was obtained by uniformly pruning 25% of experts in Step-3.5-Flash using the REAP method. readme: > https://huggingface.co/cerebras/Step-3.5-Flash-REAP-149B-A11B/main/README.md pipeline_tag: text-generation base_model: - stepfun-ai/Step-3.5-Flash --- <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> Step-3.5-Flash-REAP-149B-A11B ✨ Highlights Introducing **Step-3.5-Flash-REAP-149B-A11B**, a **memory-efficient compressed variant** of Step-3.5-Flash that maintains near-identical performance while being **25% lighter**. 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. 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