SenseTime releases SenseNova-Loop-9B-Preview, a looped multimodal model under Apache 2.0
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabSenseTime released SenseNova-Loop-9B-Preview, a multimodal model accepting text and images and generating text, aimed at long-context and agentic workflows. It combines a looped decoder, in which a block of 24 layers runs twice with shared weights, with two-stream manifold-constrained hyper-connections (mHC). Training recipes document sequences up to 262,144 tokens. The model supports MTP acceleration, while Dspark and Dflash2 are described as in training. Weights are on Hugging Face with a Transformers example.
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SenseTime released SenseNova-Loop-9B-Preview, a multimodal model accepting text and images and generating text, aimed at long-context and agentic workflows. It combines a looped decoder, in which a block of 24 layers runs twice with shared weights, with two-stream manifold-constrained hyper-connections (mHC). Training recipes document sequences up to 262,144 tokens. The model supports MTP acceleration, while Dspark and Dflash2 are described as in training. Weights are on Hugging Face with a Transformers example.
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Key findings from the stored brief
- A shared 24-layer block runs twice, giving 56 decoder-layer executions from 32 distinct layers.
- The decoder interleaves three linear-attention layers with one full-attention layer in each four-layer group.
- Documented training uses sequences up to 262,144 tokens with text, images, code, and tool trajectories.
- MTP inference acceleration is available; Dspark and Dflash2 are listed as training in progress.
Source claims not independently checked; the technical report is listed as TBD and speedups depend on workload and hardware.
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Evidence & sources
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SenseTime released SenseNova-Loop-9B-Preview on Hugging Face under Apache 2.0. It accepts text and images and runs a shared block of 24 decoder layers twice, giving 56 layer executions from 32 distinct layers, with two-stream manifold-constrained hyper-connections; training recipes reach 256K-token sequences.
Source claims not independently checked; the technical report is listed as TBD and speedups depend on workload and hardware.
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- x post ↗ · 511 likes, 45 reposts, 24 replies, 18 quotes · observed 10/30/2026, 4:08:56 PM
- huggingface post ↗ · 29 likes, 12 downloads · observed 10/30/2026, 4:08:30 PM
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Why this may matter
Looped weight-sharing aims to add compute depth without duplicating parameters, a possible efficiency lever for long-context and agent workloads. The release is open-weight under Apache 2.0, so the architecture can be inspected and tested directly.
Limits and open questions
Source claims not independently checked; the technical report is listed as TBD and speedups depend on workload and hardware.
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