{"schema_version":"onlylabs.public_signal.v1","title":"OpenBMB (MiniCPM) Model: openbmb/InfLLM-V2-Short-Dense-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/3be030f1-a7e3-46ee-ba93-d341227e98f4","json_url":"https://onlylabs.fyi/signals/3be030f1-a7e3-46ee-ba93-d341227e98f4/signal.json","generated_at":"2026-06-11T02:49:53.989379+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/3be030f1-a7e3-46ee-ba93-d341227e98f4","signal_json":"https://onlylabs.fyi/signals/3be030f1-a7e3-46ee-ba93-d341227e98f4/signal.json","source":"https://huggingface.co/openbmb/InfLLM-V2-Short-Dense-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-Short-Dense-Base. 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This model is pre-trained on a large corpus of **short-text data** and utilizes a standard **dense attention** mechanism. It serves as the starting checkpoint for the continued training phase, which unlocks the advanced long-context capabilities seen in the final sparse model. It is highly performant on short-text tasks and provides a solid foundation for further fine-tuning or continued training. 📌 Role in the InfLLM-V2 Ecosystem This model is the crucial first step in the InfLLM-V2 training workflow. The entire process is designed to be transparent and reproducible: - **Step 1: Start from this base model.** - ➡️ [**InfLLM-V2-Short-Dense-Base**](https://huggingface.co/openbmb/InfLLM-V2-Short-Dense-Base) **(This Model)**: The base..."},"evidence_pages":[{"url":"https://huggingface.co/openbmb/InfLLM-V2-Short-Dense-Base/raw/main/README.md","final_url":"https://huggingface.co/openbmb/InfLLM-V2-Short-Dense-Base/raw/main/README.md","title":"openbmb/InfLLM-V2-Short-Dense-Base model card","http_status":200,"content_type":"text/plain; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T02:49:53.989379+00:00","bytes":3420,"raw_path":"e4d9ee60aa5f7e1d2d33552bd4969297ce349b840f5da12aa644244c380b2a06.md","content_hash":"dae299bb900cd9e0252ba97111a058cd7a6c7d9196aaca6c376341509df340f3","excerpt_chars":1200,"truncated":true,"excerpt":"--- license: apache-2.0 language: - en - zh tags: - infllm - dense-attention --- InfLLM-V2-Short-Dense-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-Short-Dense-Base` is the foundational base model for the InfLLM-V2 long-context training pipeline. This model is pre-trained on a large corpus of **short-text data** and utilizes a standard **dense attention** mechanism. It serves as the starting checkpoint for the continued training phase, which unlocks the advanced long-context capabilities seen in the final sparse model. It is highly performant on short-text tasks and provides a solid foundation for further fine-tuning or continued training. 📌 Role in the InfLLM-V2 Ecosystem This model is the crucial first step in the InfLLM-V2 training workflow. 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