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model: Add support for SenseNova U1 model - #28919
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Add support for the dense SenseNova U1 understanding branch, including the U1.5-8B-MoT model. - Add HF-to-GGUF conversion for NEOChatModel, excluding MoT generation tensors and concatenating temporal and spatial Q/K normalization weights. - Register the sensenova_u1 GGUF architecture and custom spatial RoPE metadata. - Implement temporal/spatial Q/K normalization with 4-axis RoPE. - Keep U1-specific text position handling in the model implementation. - Add synthetic architecture coverage for sensenova_u1. Only the dense understanding branch is supported; generation branches are intentionally rejected during conversion.
Add conversion support for the SenseNova U1 vision encoder and implement its CPU mtmd projector graph. The graph applies patch embedding, GELU, adjacent-pair 2D RoPE, spatial downsampling, and dense projection for dynamic-resolution image inputs.
Maphist0
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CISC,
JohannesGaessler and
ggerganov
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September 15, 2026 00:13
ngxson
requested changes
Sep 21, 2026
ngxson
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for vision support: mirror what gemma4uv.cpp is doing. keep it simple.
Replace the hard-coded spatial RoPE key with shared GGUF and LLM_KV definitions used by conversion, model loading, and architecture tests. Clarify why SenseNova U1 retains its custom text-position input: U1 uses [t, 0, 0, 0], while the generic M-RoPE input uses [t, t, t, 0].
Map the SenseNova U1 dense projection to the standard mm.0 weight and bias tensors and reuse the shared projector fields in the loader and graph. Read vision parameters from the checkpoint configuration and remove the U1-specific tensor names and model fields while preserving its strided projection, bias, and row-major token layout.
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@ngxson Thank you for the constructive feedback. I’ve updated the patches to address the review comments and look forward to further discussion. |
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Many thanks! Would other reviewers (@CISC @ggerganov @JohannesGaessler) please also take a look and make some review comments when available? That would be very helpful! |
CISC
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Oct 20, 2026
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
Use standard backend precision handling for QKV, FFN, and output projections instead of casting inputs based on the output tensor type. Assisted-by: Codex
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@CISC Thank you very much for the constructive comments. I’ve updated the patches to address them and look forward to further discussion. |
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
CISC
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Oct 21, 2026
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auRose94
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Oct 28, 2026
* model : add SenseNova U1 understanding support Add support for the dense SenseNova U1 understanding branch, including the U1.5-8B-MoT model. - Add HF-to-GGUF conversion for NEOChatModel, excluding MoT generation tensors and concatenating temporal and spatial Q/K normalization weights. - Register the sensenova_u1 GGUF architecture and custom spatial RoPE metadata. - Implement temporal/spatial Q/K normalization with 4-axis RoPE. - Keep U1-specific text position handling in the model implementation. - Add synthetic architecture coverage for sensenova_u1. Only the dense understanding branch is supported; generation branches are intentionally rejected during conversion. * conversion : treat one-expert SenseNova U1 configs as dense * mtmd : add SenseNova U1 vision support Add conversion support for the SenseNova U1 vision encoder and implement its CPU mtmd projector graph. The graph applies patch embedding, GELU, adjacent-pair 2D RoPE, spatial downsampling, and dense projection for dynamic-resolution image inputs. * mtmd : fix SenseNova U1 vision token layout * model : clean up SenseNova U1 RoPE metadata Replace the hard-coded spatial RoPE key with shared GGUF and LLM_KV definitions used by conversion, model loading, and architecture tests. Clarify why SenseNova U1 retains its custom text-position input: U1 uses [t, 0, 0, 0], while the generic M-RoPE input uses [t, t, t, 0]. * mtmd : simplify SenseNova U1 projector integration Map the SenseNova U1 dense projection to the standard mm.0 weight and bias tensors and reuse the shared projector fields in the loader and graph. Read vision parameters from the checkpoint configuration and remove the U1-specific tensor names and model fields while preserving its strided projection, bias, and row-major token layout. * mtmd : fix SenseNova U1 image positions and attention * fix spacing * mtmd : restore generic test input chunks * mtmd : restore SenseNova U1 integration after upstream merge * Update conversion/sensenova_u1.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * Update src/models/sensenova-u1.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * model : remove SenseNova U1 BF16 activation casts Use standard backend precision handling for QKV, FFN, and output projections instead of casting inputs based on the output tensor type. Assisted-by: Codex * Update src/models/sensenova-u1.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
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Overview
This PR adds support for the SenseNova U1 text-generation branch in llama.cpp.
Supported use cases:
The image path supports SenseNova U1’s dynamic-resolution preprocessing, 2D RoPE, patch embedding, spatial merge, and image-token injection.
The implementation is intentionally limited to U1’s understanding/text-generation branch. Image generation branch is not included.
Additional information
Related to discussion #23529
Validation
Compile:
Convert ckpt:
Case 1: Text generation:
Case 2: Image-to-text generation:
Case 3: Image-text-to-text generation:
Requirements