{"schema_version":"onlylabs.public_signal.v1","title":"OpenBMB (MiniCPM) Model: openbmb/InfLLM-V2-Long-Sparse-Base","description":"OpenBMB (MiniCPM) model signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/96564eaf-81e3-49e5-a600-8a5727fb84e9","json_url":"https://onlylabs.fyi/signals/96564eaf-81e3-49e5-a600-8a5727fb84e9/signal.json","generated_at":"2026-06-11T02:49:54.147562+00:00","org":{"slug":"openbmb","name":"OpenBMB (MiniCPM)","category":"neolab","category_label":"Neolab","dossier_url":"https://onlylabs.fyi/labs/openbmb","dossier_json_url":"https://onlylabs.fyi/labs/openbmb/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/96564eaf-81e3-49e5-a600-8a5727fb84e9","signal_json":"https://onlylabs.fyi/signals/96564eaf-81e3-49e5-a600-8a5727fb84e9/signal.json","source":"https://huggingface.co/openbmb/InfLLM-V2-Long-Sparse-Base","lab_dossier":"https://onlylabs.fyi/labs/openbmb","lab_dossier_json":"https://onlylabs.fyi/labs/openbmb/dossier.json","analysis":"https://onlylabs.fyi/analysis/openbmb","analysis_json":"https://onlylabs.fyi/analysis/openbmb/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/openbmb/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":"OpenBMB (MiniCPM) published openbmb/InfLLM-V2-Long-Sparse-Base. 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This model is the result of continued training on the [**InfLLM-V2-Short-Dense-Base**](https://huggingface.co/openbmb/InfLLM-V2-Short-Dense-Base) model using just **5B tokens** of high-quality long-text data from the [**InfLLM-V2-data-5B**](https://huggingface.co/datasets/openbmb/InfLLM-V2-data-5B) dataset. Its core feature is the InfLLM-V2 architecture, which allows it to seamlessly switch between high-performance dense attention for short texts and highly efficient sparse attention for long texts. This enables the model to process extremely long sequences with significant end-to-end acceleration while preserving performance on standard tasks. 📌 The Training Journey This model is..."},"evidence_pages":[{"url":"https://huggingface.co/openbmb/InfLLM-V2-Long-Sparse-Base/raw/main/README.md","final_url":"https://huggingface.co/openbmb/InfLLM-V2-Long-Sparse-Base/raw/main/README.md","title":"openbmb/InfLLM-V2-Long-Sparse-Base model card","http_status":200,"content_type":"text/plain; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T02:49:54.147562+00:00","bytes":6344,"raw_path":"93e7e0b144df584eb2aa6492c67b2a3434eea95a05d8a9ec5f7d7fae827a4e70.md","content_hash":"851939fbc1b0b8bb7732de7760d913c2526707ffad87135068aeb3977d4375f7","excerpt_chars":1200,"truncated":true,"excerpt":"--- license: apache-2.0 language: - en - zh tags: - long-context - infllm - sparse-attention --- InfLLM-V2-Long-Sparse-Base **Project Links**: [[Paper](https://arxiv.org/abs/2509.24663)] [[InfLLM-V2 Models](https://huggingface.co/openbmb/InfLLM-V2-Long-Sparse-Base)] [[CUDA Kernel Code](https://github.com/OpenBMB/infllmv2_cuda_impl)] --- 🚀 Model Description `InfLLM-V2-Long-Sparse-Base` is the final, long-context capable model in the InfLLM-V2 series, featuring a native **sparse attention** mechanism. This model is the result of continued training on the [**InfLLM-V2-Short-Dense-Base**](https://huggingface.co/openbmb/InfLLM-V2-Short-Dense-Base) model using just **5B tokens** of high-quality long-text data from the [**InfLLM-V2-data-5B**](https://huggingface.co/datasets/openbmb/InfLLM-V2-data-5B) dataset. Its core feature is the InfLLM-V2 architecture, which allows it to seamlessly switch between high-performance dense attention for short texts and highly efficient sparse attention for long texts. 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