{"schema_version":"onlylabs.public_signal.v1","title":"LG AI Research (EXAONE) Model: LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct","description":"LG AI Research (EXAONE) model signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/3bf22069-fc4c-4095-b128-1886114a227c","json_url":"https://onlylabs.fyi/signals/3bf22069-fc4c-4095-b128-1886114a227c/signal.json","generated_at":"2026-06-11T03:04:00.775031+00:00","org":{"slug":"lg-ai","name":"LG AI Research (EXAONE)","category":"neolab","category_label":"Neolab","dossier_url":"https://onlylabs.fyi/labs/lg-ai","dossier_json_url":"https://onlylabs.fyi/labs/lg-ai/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/3bf22069-fc4c-4095-b128-1886114a227c","signal_json":"https://onlylabs.fyi/signals/3bf22069-fc4c-4095-b128-1886114a227c/signal.json","source":"https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct","lab_dossier":"https://onlylabs.fyi/labs/lg-ai","lab_dossier_json":"https://onlylabs.fyi/labs/lg-ai/dossier.json","analysis":"https://onlylabs.fyi/analysis/lg-ai","analysis_json":"https://onlylabs.fyi/analysis/lg-ai/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/lg-ai/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":"https://onlylabs.fyi/topics/releases","topic_signals_json":"https://onlylabs.fyi/topics/releases/signals.json?category=neolab","topic_feed":"https://onlylabs.fyi/topics/releases/feed.xml?category=neolab","data_business":null},"answer_pack":{"answer":"LG AI Research (EXAONE) published LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct. 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EXAONE 3.5 language models include: 1) **2.4B model** optimized for deployment on small or resource-constrained devices, 2) **7.8B model** matching the size of its predecessor but offering improved performance, and 3) **32B model** delivering powerful performance. All models support long-context processing of up to 32K tokens. Each model demonstrates state-of-the-art performance in real-world use cases and long-context understanding, while remaining competitive in general domains compared to recently released models of similar sizes. For more details, please refer to our [technical report](https://arxiv.org/abs/2412.04862), [blog](https://www.lgresearch.ai/blog/view?seq=507) and..."},"evidence_pages":[{"url":"https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct/raw/main/README.md","final_url":"https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct/raw/main/README.md","title":"LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct model card","http_status":200,"content_type":"text/plain; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T03:04:00.775031+00:00","bytes":7499,"raw_path":"beeb1039a1eeccc49ac968a3abd39f15a8201a37d3119563821c17f2510745e0.md","content_hash":"2ec2f8c8e50890fa835d30be2b397c187963c2fbad69c9c48e57ebd12cca4d05","excerpt_chars":1200,"truncated":true,"excerpt":"--- license: other license_name: exaone license_link: LICENSE language: - en - ko tags: - lg-ai - exaone - exaone-3.5 pipeline_tag: text-generation library_name: transformers --- <p align=\"center\"> <img src=\"assets/EXAONE_Symbol+BI_3d.png\", width=\"300\", style=\"margin: 40 auto;\"> <br> EXAONE-3.5-2.4B-Instruct Introduction We introduce EXAONE 3.5, a collection of instruction-tuned bilingual (English and Korean) generative models ranging from 2.4B to 32B parameters, developed and released by LG AI Research. EXAONE 3.5 language models include: 1) **2.4B model** optimized for deployment on small or resource-constrained devices, 2) **7.8B model** matching the size of its predecessor but offering improved performance, and 3) **32B model** delivering powerful performance. All models support long-context processing of up to 32K tokens. Each model demonstrates state-of-the-art performance in real-world use cases and long-context understanding, while remaining competitive in general domains compared to recently released models of similar sizes. 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