Parameter Breakdown

SenseNova-U1-8B-MoT contains roughly 8B understanding parameters and 8B generation parameters. To avoid confusion caused by the naming and to present the architecture more accurately, we provide a small inspection script that parses parameter names of the loaded checkpoint and reports a detailed parameter breakdown.

Run the script

python scripts/inspect_model_params.py \
    --model_path sensenova/SenseNova-U1-8B-MoT

Useful argments:

• --dtype {float32,float16,bfloat16} (default: bfloat16) — load dtype. It does not affect parameter counts; it only affects the reported memory column, since each bf16/fp16 element occupies 2 bytes versus 4 bytes for fp32.
• --show_groups <name1,name2> (default: shared) — list member parameters of the specified groups. Use all for every group, or an empty string to disable.
• --custom_groups_json <path> — override the default grouping rules with a JSON file of the form {"group_name": ["prefix1", "prefix2"]}.

Example output

Model: sensenova/SenseNova-U1-8B-MoT
Load dtype:   bfloat16
Total params: 17.552B
Total memory: 35.105GB (bfloat16)
---------------------------------------------------------------------
group                              params memory (bfloat16)      ratio
---------------------------------------------------------------------
generation_transformer             8.186B         16.373GB     46.64%
understanding_transformer          8.121B         16.243GB     46.27%
shared                             1.245B          2.489GB      7.09%
---------------------------------------------------------------------
Pathway breakdown (shared counted in both):
---------------------------------------------------------------------
pathway                            params memory (bfloat16)      ratio
---------------------------------------------------------------------
understanding pathway              9.366B         18.732GB     53.36%
generation pathway                 9.431B         18.862GB     53.73%

---------------------------------------------------------------------
Members of group 'shared' (2 params, 1.245B total, 2.489GB @ bfloat16)
---------------------------------------------------------------------
param name                                                  numel    dtype
---------------------------------------------------------------------
language_model.model.embed_tokens.weight                 622.330M bfloat16
language_model.lm_head.weight                            622.330M bfloat16

How to read it

1. Parameters (mutually exclusive, sums to 100%)

Each parameter is counted exactly once and assigned to one of three groups based on its module path:

• understanding_transformer ≈ 8.12B (46%) — vision und. (vision_model.*) plus the LLM expert without _mot_gen suffix (language_model.* minus the generation expert and the shared text I/O).
• generation_transformer ≈ 8.19B (47%) — generation-side modules (fm_modules.*: vision gen., flow-matching head, timestep / noise embedders) plus the LLM expert with _mot_gen suffix (language_model.* containing _mot_gen).
• shared ≈ 1.25B (7%) — text-token I/O reused by both pathways: language_model.model.embed_tokens and language_model.lm_head.

2. Pathway coverage (forward activations, ratios sum to >100%)

A pathway sums the parameters that are actually traversed during the forward pass of one task. Because both tasks reuse the shared group, the ratios overlap and add up to more than 100%.

• Understanding pathway ≈ understanding_transformer + shared ≈ 9.37B (53%). Image goes through vision_model → tokens go through embed_tokens → the LLM runs on the non-_mot_gen expert → lm_head produces text logits.

• Generation pathway (single-turn thinking interleave) ≈ generation_transformer + shared ≈ 9.43B (54%). The condition image goes through fm_modules.vision_model_mot_gen, while the text prompt goes through embed_tokens → the LLM runs on the _mot_gen expert → text reasoning is produced via lm_head and the image is decoded via fm_modules.fm_head.

Why embed_tokens and lm_head are "shared", not "understanding-only"

embed_tokens is needed by every text token and is therefore obviously shared. lm_head is also exercised by the generation pathway in some scenarios, e.g., t2i-reasoning runs a thinking phase that emits text tokens before any image token is produced, so lm_head is on the critical path of both pathways — hence the "shared" label.