Repository Analysis

OpenSenseNova/SenseNova-U1

SenseNova-U series: Native Unified Paradigm with NEO-unify from the First Principles

27.6 Moderate AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of OpenSenseNova/SenseNova-U1, a Python project with 5,795 GitHub stars. SynthScan v2.0 examined 101,553 lines of code across 337 source files, recording 1809 pattern matches distributed across 21 syntactic categories. The overall adjusted score of 27.6 places this repository in the Moderate AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

27.6
Adjusted Score
27.6
Raw Score
100%
Time Factor
2026-08-28
Last Push
5.8K
Stars
Python
Language
101.6K
Lines of Code
337
Files
1.8K
Pattern Hits
2026-08-29
Scan Date
0.15
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

Longitudinal tracking requires multiple scan runs. Once this repository is re-scanned after new commits land, this chart will visualise how the synthetic code signal evolves over time — enabling you to detect whether AI authorship is growing, stabilising, or being actively corrected by human engineers.

No multi-scan history yet — run the scanner again to build trend data.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 1HIGH 49MEDIUM 538LOW 1221

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 1809 distinct pattern matches across 21 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Decorative Section Separators296 hits · 936 pts
SeverityFileLineSnippetContext
MEDIUMpyproject.toml99# -----------------------------------------------------------------------------COMMENT
MEDIUMpyproject.toml107# -----------------------------------------------------------------------------COMMENT
MEDIUM…sensenovavl_moe_chat/configuration_sensenovavl_chat.py1# --------------------------------------------------------COMMENT
MEDIUM…sensenovavl_moe_chat/configuration_sensenovavl_chat.py5# --------------------------------------------------------COMMENT
MEDIUM…enovavl/model/sensenovavl_moe_chat/modeling_neo_vit.py1# --------------------------------------------------------COMMENT
MEDIUM…enovavl/model/sensenovavl_moe_chat/modeling_neo_vit.py5# --------------------------------------------------------COMMENT
MEDIUM…/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py1# --------------------------------------------------------COMMENT
MEDIUM…/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py5# --------------------------------------------------------COMMENT
MEDIUM…ing/sensenovavl/model/sensenovavl_moe_chat/__init__.py1# --------------------------------------------------------COMMENT
MEDIUM…ing/sensenovavl/model/sensenovavl_moe_chat/__init__.py5# --------------------------------------------------------COMMENT
MEDIUM…vl/model/sensenovavl_moe_chat/configuration_neo_vit.py1# --------------------------------------------------------COMMENT
MEDIUM…vl/model/sensenovavl_moe_chat/configuration_neo_vit.py5# --------------------------------------------------------COMMENT
MEDIUM…senovavl/model/sensenovavl_moe_chat/flash_attention.py1# --------------------------------------------------------COMMENT
MEDIUM…senovavl/model/sensenovavl_moe_chat/flash_attention.py8# --------------------------------------------------------COMMENT
MEDIUMtraining/sensenovavl/data/cfg_cond_drop_utils.py58 # -------------------------------------------------COMMENT
MEDIUMtraining/sensenovavl/data/cfg_cond_drop_utils.py61 # -------------------------------------------------COMMENT
MEDIUMtraining/sensenovavl/data/cfg_cond_drop_utils.py77 # -------------------------------------------------COMMENT
MEDIUMtraining/sensenovavl/data/cfg_cond_drop_utils.py79 # -------------------------------------------------COMMENT
MEDIUMtraining/sensenovavl/data/cfg_cond_drop_utils.py93 # -------------------------------------------------COMMENT
MEDIUMtraining/sensenovavl/data/cfg_cond_drop_utils.py95 # -------------------------------------------------COMMENT
MEDIUMtraining/tools/revert2hf.py112# ---------------------------------------------------------------------------COMMENT
MEDIUMtraining/tools/revert2hf.py115# ---------------------------------------------------------------------------COMMENT
MEDIUMtraining/tools/revert2hf.py162# ---------------------------------------------------------------------------COMMENT
MEDIUMtraining/tools/revert2hf.py164# ---------------------------------------------------------------------------COMMENT
MEDIUMtraining/tools/revert2hf.py207# ---------------------------------------------------------------------------COMMENT
MEDIUMtraining/tools/revert2hf.py209# ---------------------------------------------------------------------------COMMENT
MEDIUMtraining/tools/revert2hf.py245# ---------------------------------------------------------------------------COMMENT
MEDIUMtraining/tools/revert2hf.py247# ---------------------------------------------------------------------------COMMENT
MEDIUMtraining/tools/revert2hf.py317# ---------------------------------------------------------------------------COMMENT
MEDIUMtraining/tools/revert2hf.py319# ---------------------------------------------------------------------------COMMENT
MEDIUMtraining/tools/revert2hf.py395# ---------------------------------------------------------------------------COMMENT
MEDIUMtraining/tools/revert2hf.py397# ---------------------------------------------------------------------------COMMENT
MEDIUMtraining/tools/revert2hf.py470# ---------------------------------------------------------------------------COMMENT
MEDIUMtraining/tools/revert2hf.py472# ---------------------------------------------------------------------------COMMENT
MEDIUMtraining/tools/revert2hf.py485# ---------------------------------------------------------------------------COMMENT
MEDIUMtraining/tools/revert2hf.py487# ---------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py10# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py12# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py17# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py19# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py36# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py38# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py45# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py47# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py142# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py144# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py152# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py154# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py159# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py161# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py406# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py408# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py417# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py419# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py63# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py65# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py101# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py103# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py114# -----------------------------------------------------------------------------COMMENT
MEDIUM…nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py116# -----------------------------------------------------------------------------COMMENT
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Hyper-Verbose Identifiers447 hits · 441 pts
SeverityFileLineSnippetContext
LOWtraining/train_sensenovau1.py23def patch_inductor_triton_max_block():CODE
LOWtraining/sensenovavl/utils/checkpoint.py327def _load_pretrained_internevo_llm(model, model_args, state_dict, has_moe=False):CODE
LOWtraining/sensenovavl/utils/checkpoint.py508def convert_q_k_v_to_wqkv_interleaved(CODE
LOWtraining/sensenovavl/utils/checkpoint.py1589def load_pretrained_fm_modules(model, model_path=None, state_dict=None):CODE
LOWtraining/sensenovavl/utils/checkpoint.py1623def load_pretrained_vit_moe_online(state_dict, model_args):CODE
LOWtraining/sensenovavl/utils/checkpoint.py1809def load_pretrained_llm_moe_offline(model, model_args, model_path):CODE
LOWtraining/sensenovavl/utils/checkpoint.py1884def load_pretrained_llm_moe_online(model, state_dict, model_args, from_hf: bool = True):CODE
LOWtraining/sensenovavl/utils/utils.py14def precompute_rope_freqs_sincos(CODE
LOWtraining/sensenovavl/utils/utils.py46def build_abs_positions_from_grid_hw(grid_hw: torch.Tensor, device=None):CODE
LOW…/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py66def _offsets_to_doc_ids_tensor(offsets, has_pad, split_size=1024):CODE
LOW…/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py140def create_flex_mask_padding_image_gen(document_ids, modality_indicators, image_gen_indicators, token_pos, dup_boundary,CODE
LOW…/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py235def slice_tensor_by_image_lens(tensor: Optional[torch.Tensor], full_image_seq_lens, kept_image_seq_lens, dim: int = 0):CODE
LOW…/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py255def align_selected_to_image_seq_lens(selected_mask: torch.Tensor, modality_indicators: torch.Tensor, image_seq_lens):CODE
LOW…/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py274def build_modality_indicators_from_context_runs(image_context_mask: torch.Tensor):CODE
LOW…/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py302def summarize_image_seq_mismatch(prompt_image_seq_lens, expected_image_seq_lens):CODE
LOW…/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py378def unpack_two_branch_sequence(CODE
LOW…/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py586 def prepare_image_gen_targets(self, pixel_values, image_for_gen_flags, grid_hw):CODE
LOW…/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py682 def build_image_gen_indicators(CODE
LOW…/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py1175 def _build_cu_seqlens_from_doc_ids(doc_ids: torch.Tensor) -> torch.Tensor:CODE
LOW…/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py1598def build_pipeline_partition_mot_model(**kwargs):CODE
LOW…aining/sensenovavl/model/sensenovavl_moe_chat/utils.py106def gather_forward_split_backward(input_, parallel_mode, dim, div=0, mod=0, split_size=None):CODE
LOW…aining/sensenovavl/model/sensenovavl_moe_chat/utils.py151def split_forward_gather_backward(input_, parallel_mode, dim, div=0, mod=0, split_size=None):CODE
LOWtraining/sensenovavl/model/vit_moe/gshard_layer.py666 def add_multiplicative_jitter(self, jitter_epsilon):CODE
LOWtraining/sensenovavl/train/record_metrics.py24def all_gather_moe_metrics_pp(CODE
LOWtraining/sensenovavl/train/record_metrics.py58def record_current_batch_training_metrics(CODE
LOWtraining/sensenovavl/data/build_dataloader.py70def maybe_set_torch_sharing_strategy(sharing_strategy):CODE
LOWtraining/sensenovavl/data/build_dataloader.py83def get_multimodal_streaming_train_loader_items(data_cfg): # NOTE:CODE
LOWtraining/sensenovavl/data/build_dataloader.py238def get_multimodal_packed_streaming_train_loader_items(data_cfg):CODE
LOWtraining/sensenovavl/data/build_dataloader.py432def _build_multimodal_dataloader_kwargs(data_cfg, train_collate_fn):CODE
LOWtraining/sensenovavl/data/build_dataloader.py465def build_train_loader_with_data_type():CODE
LOWtraining/sensenovavl/data/cfg_cond_drop_utils.py13def find_subsequence_positions(seq: torch.Tensor, pattern: torch.Tensor):CODE
LOWtraining/sensenovavl/data/cfg_cond_drop_utils.py29def build_text_drop_mask_ranges_singleturn(CODE
LOWtraining/sensenovavl/data/cfg_cond_drop_utils.py125def mask_text_for_current_image(image_i: int, user_pos: list, asst_pos: list, image_start_token_positions: List[int], imCODE
LOWtraining/sensenovavl/data/dataset.py315def simulate_jpeg_degradation(quality):CODE
LOWtraining/sensenovavl/data/dataset.py768def dynamic_preprocess_native_resolution(image, size_factor=32, min_pixels=4 * 32 * 32, max_pixels=16384 * 32 * 32, **kwCODE
LOWtraining/sensenovavl/data/dataset.py799def resize_crop_from_candidates(image: Image.Image, candidates, **kwargs):CODE
LOWtraining/sensenovavl/data/multimodal_dataset.py47def seconds_to_minutes_secondswithdot(seconds):CODE
LOWtraining/sensenovavl/data/multimodal_dataset.py53def seconds_to_minutes_seconds(seconds):CODE
LOWtraining/sensenovavl/data/multimodal_dataset.py226 def find_closest_aspect_ratio(self, aspect_ratio, target_ratios, width, height):CODE
LOWtraining/sensenovavl/data/multimodal_dataset.py631 def image_gen_append_image_size_info(CODE
LOWtraining/sensenovavl/data/multimodal_dataset.py1191 def _enable_worker_distributed(self):CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py1580 # def get_cu_seqlens_and_indexes(COMMENT
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py58def is_dist_avail_and_initialized():CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py1626 def get_cu_seqlens_and_indexes(CODE
LOWtraining/tools/revert2hf.py347def compute_dense_slice_layout(vit_num_layers: int, llm_num_layers: int):CODE
LOWtraining/sensenovalm/checkpoint/load_funcs.py19def load_hf_llama_pretrained_weights(folder, model):CODE
LOWtraining/sensenovalm/checkpoint/load_funcs.py234def obtain_spliting_parameters():CODE
LOWtraining/sensenovalm/checkpoint/utils.py42def obtain_not_saved_mtp_state(state, missing_keys: List[str]):CODE
LOWtraining/sensenovalm/checkpoint/components.py624def load_optimizer_checkpoint(folder, optim):CODE
LOWtraining/sensenovalm/checkpoint/components.py754def save_optimizer_checkpoint(optim, state_path):CODE
LOWtraining/sensenovalm/checkpoint/checkpoint_manager.py235def try_load_sensenovalm_ckpt_func(ckpt_mm, load_info, *args, func=None, **kwargs): # pylint: disable=W0613CODE
LOWtraining/sensenovalm/checkpoint/checkpoint_manager.py586 def query_latest_snapshot_step_local(self):CODE
LOWtraining/sensenovalm/checkpoint/checkpoint_manager.py669 def try_load_averaged_model_from_checkpoint(self):CODE
LOWtraining/sensenovalm/checkpoint/convert_to_hf.py779def convert_internevo_ckpt_to_hf(src: str, tgt: str):CODE
LOWtraining/sensenovalm/core/model_average.py30def _iter_named_params_and_buffers(model: Module, use_buffers: bool) -> Iterable[Tuple[str, Tensor]]:CODE
LOWtraining/sensenovalm/core/model_average.py219 def get_averaged_model_state_dict(self, model: Module) -> Dict[str, Tensor]:STRING
LOWtraining/sensenovalm/core/model_average.py236 def load_from_model_state_dict(self, model_state_dict: Dict[str, Tensor]) -> None:STRING
LOWtraining/sensenovalm/core/model_average.py251 def load_from_checkpoint_folder(STRING
LOWtraining/sensenovalm/core/model_average.py274 def save_to_averaged_model_info(self, folder: str):STRING
LOWtraining/sensenovalm/core/trainer_builder.py62def _is_dataloader_worker_failure(exc: BaseException) -> bool:CODE
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Unused Imports271 hits · 250 pts
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LOW…/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py22CODE
LOW…ing/sensenovavl/model/sensenovavl_moe_chat/__init__.py7CODE
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LOW…ing/sensenovavl/model/sensenovavl_moe_chat/__init__.py10CODE
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LOWtraining/sensenovavl/data/multimodal_dataset.py41CODE
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LOWtraining/sensenovalm/core/context/__init__.py1CODE
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LOWtraining/sensenovalm/core/context/__init__.py12CODE
LOWtraining/sensenovalm/core/context/__init__.py12CODE
LOWtraining/sensenovalm/core/context/__init__.py12CODE
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LOWtraining/sensenovalm/core/context/__init__.py12CODE
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LOWtraining/sensenovalm/core/context/__init__.py12CODE
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LOWtraining/sensenovalm/core/context/__init__.py22CODE
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Dead Code163 hits · 234 pts
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MEDIUMtraining/sensenovalm/model/modules/mha.py496CODE
MEDIUMtraining/sensenovalm/model/modules/mha.py507CODE
MEDIUMtraining/sensenovalm/model/modules/mha.py510CODE
MEDIUMtraining/sensenovalm/model/modules/mha.py514CODE
MEDIUMevaluation/interleave/Realunify/calculate_score_ueg.py202CODE
MEDIUMevaluation/interleave/Realunify/calculate_score_ueg.py203CODE
MEDIUMevaluation/interleave/Realunify/calculate_score_ueg.py211CODE
MEDIUMevaluation/interleave/Realunify/calculate_score_ueg.py212CODE
MEDIUMevaluation/interleave/Realunify/calculate_score_ueg.py213CODE
MEDIUMevaluation/interleave/Realunify/calculate_score_ueg.py216CODE
MEDIUMevaluation/interleave/Realunify/calculate_score_ueg.py217CODE
MEDIUMsrc/sensenova_u1/models/neo_unify/modeling_qwen3_moe.py296CODE
MEDIUMsrc/sensenova_u1/models/neo_unify/modeling_qwen3_moe.py298CODE
MEDIUMsrc/sensenova_u1/models/neo_unify/modeling_qwen3_moe.py299CODE
MEDIUMsrc/sensenova_u1/models/neo_unify/modeling_qwen3_moe.py303CODE
MEDIUMsrc/sensenova_u1/models/neo_unify/modeling_qwen3_moe.py307CODE
MEDIUMsrc/sensenova_u1/models/neo_unify/modeling_qwen3_moe.py309CODE
MEDIUMsrc/sensenova_u1/models/neo_unify/modeling_qwen3_moe.py322CODE
MEDIUMsrc/sensenova_u1/models/neo_unify/modeling_qwen3_moe.py324CODE
MEDIUMsrc/sensenova_u1/models/neo_unify/modeling_qwen3_moe.py326CODE
MEDIUMsrc/sensenova_u1/models/neo_unify/modeling_qwen3_moe.py327CODE
MEDIUMsrc/sensenova_u1/models/neo_unify/modeling_qwen3_moe.py337CODE
MEDIUMsrc/sensenova_u1/models/neo_unify/modeling_qwen3_moe.py342CODE
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Excessive Try-Catch Wrapping166 hits · 210 pts
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LOWtraining/train_sensenovau1.py36 except Exception:CODE
LOW…enovavl/model/sensenovavl_moe_chat/modeling_neo_vit.py64except Exception:CODE
LOWtraining/sensenovavl/data/build_dataloader.py79 except Exception as exc: # pragma: no cover - depends on runtime envCODE
LOWtraining/sensenovavl/data/build_dataloader.py394 except Exception as exc: # pragma: no cover - best effort cleanupCODE
LOWtraining/sensenovavl/data/dataset.py122 except Exception:CODE
MEDIUMtraining/sensenovavl/data/multimodal_dataset.py1204def get_sample(self, line) -> Dict[str, torch.Tensor]:CODE
LOWtraining/sensenovavl/data/multimodal_dataset.py751 except Exception as e:CODE
LOWtraining/sensenovavl/data/multimodal_dataset.py1209 except Exception as e:CODE
LOWtraining/sensenovavl/data/multimodal_dataset.py1258 except Exception as e:CODE
MEDIUM…ining/sensenovavl/data/dataset_interleaved_iterable.py247def load_image(self, image_path_or_url):CODE
MEDIUM…ining/sensenovavl/data/dataset_interleaved_iterable.py254def _get_mmap(self, data_path):CODE
MEDIUM…ining/sensenovavl/data/dataset_interleaved_iterable.py265def get_sample(self, line):CODE
MEDIUM…ining/sensenovavl/data/dataset_interleaved_iterable.py945def next_data(self, current_dataset_idx):CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py250 except Exception as e:CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py260 except Exception as e:CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py271 except Exception as e:CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py513 except Exception as e:CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py960 except Exception as e:CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py988 except Exception as e:CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py1350 except Exception as e:CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py1360 except Exception as e:CODE
LOWtraining/sensenovalm/checkpoint/components.py730 except Exception as e:CODE
LOWtraining/sensenovalm/core/trainer_builder.py474 except Exception as exc:CODE
LOWtraining/sensenovalm/initialize/launch.py913 except Exception:CODE
MEDIUMtraining/sensenovalm/initialize/launch.py869def get_config_value(config, key, defalut):CODE
MEDIUMtraining/sensenovalm/utils/storage_manager.py77 print(f"Error: {mesage}", flush=True)CODE
LOWtraining/sensenovalm/utils/storage_manager.py484 except Exception as e:CODE
LOWtraining/sensenovalm/utils/storage_manager.py669 except Exception as e:CODE
LOWtraining/sensenovalm/utils/storage_manager.py737 except Exception as e:CODE
LOWtraining/sensenovalm/utils/storage_manager.py750 except Exception as e:CODE
LOWtraining/sensenovalm/utils/storage_manager.py800 except Exception as e:CODE
LOWtraining/sensenovalm/utils/writer.py165 except Exception:CODE
LOWtraining/sensenovalm/utils/writer.py197 except Exception:CODE
LOWtraining/sensenovalm/utils/writer.py214 except Exception:CODE
LOWtraining/sensenovalm/utils/writer.py232 except Exception:CODE
LOWtraining/sensenovalm/utils/writer.py241 except Exception:CODE
MEDIUMtraining/sensenovalm/utils/writer.py237def add_text(self, key, value, step):CODE
LOWtraining/sensenovalm/data/tokenized/single_dataset.py131 except Exception as e:CODE
LOWtraining/sensenovalm/data/tokenized/single_dataset.py154 except Exception as err:CODE
LOWevaluation/gen/tiif/eval/eval_with_vlm_mp.py75 except Exception as e:CODE
LOWevaluation/gen/tiif/eval/eval_with_vlm_mp.py148 except Exception as e:CODE
LOWevaluation/gen/tiif/eval/eval_with_vlm_mp.py219 except Exception as e:CODE
MEDIUMevaluation/gen/tiif/eval/eval_with_vlm_mp.py185def process_task(task, args):CODE
LOWevaluation/gen/tiif/eval/summary_results.py102 except Exception as e:CODE
LOWevaluation/gen/bizgeneval/eval_images_bizgeneval.py77 except Exception:CODE
LOWevaluation/gen/bizgeneval/eval_images_bizgeneval.py84 except Exception:CODE
LOWevaluation/gen/bizgeneval/eval_images_bizgeneval.py91 except Exception:CODE
LOWevaluation/gen/bizgeneval/eval_images_bizgeneval.py203 except Exception:CODE
LOWevaluation/gen/bizgeneval/eval_images_bizgeneval.py253 except Exception:CODE
LOWevaluation/gen/bizgeneval/eval_images_bizgeneval.py354 except Exception as exc:CODE
LOWevaluation/gen/bizgeneval/gen_images_bizgeneval.py79 except Exception:CODE
LOWevaluation/gen/cvtg/unified_metrics_eval.py172 except Exception as e:CODE
LOWevaluation/gen/cvtg/unified_metrics_eval.py232 except Exception as e:CODE
LOWevaluation/gen/cvtg/unified_metrics_eval.py264 except Exception as e:CODE
LOWevaluation/gen/cvtg/unified_metrics_eval.py302 except Exception as e:CODE
LOWevaluation/gen/cvtg/unified_metrics_eval.py359 except Exception as e:CODE
LOWevaluation/gen/cvtg/unified_metrics_eval.py371 except Exception as e:CODE
LOWevaluation/gen/cvtg/unified_metrics_eval.py391 except Exception as e:CODE
LOWevaluation/gen/igenbench/eval_images_igenbench.py128 except Exception:CODE
LOWevaluation/gen/igenbench/eval_images_igenbench.py136 except Exception:CODE
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Deep Nesting182 hits · 162 pts
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LOWtraining/sensenovavl/utils/checkpoint.py40CODE
LOWtraining/sensenovavl/utils/checkpoint.py119CODE
LOWtraining/sensenovavl/utils/checkpoint.py385CODE
LOWtraining/sensenovavl/utils/checkpoint.py716CODE
LOWtraining/sensenovavl/utils/checkpoint.py836CODE
LOWtraining/sensenovavl/utils/checkpoint.py1623CODE
LOWtraining/sensenovavl/utils/checkpoint.py1884CODE
LOW…/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py566CODE
LOW…/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py706CODE
LOWtraining/sensenovavl/model/vit_moe/gshard_layer.py226CODE
LOWtraining/sensenovavl/train/record_metrics.py58CODE
LOWtraining/sensenovavl/data/build_dataloader.py360CODE
LOWtraining/sensenovavl/data/conversation.py68CODE
LOWtraining/sensenovavl/data/dataset.py111CODE
LOWtraining/sensenovavl/data/dataset.py510CODE
LOWtraining/sensenovavl/data/dataset.py681CODE
LOWtraining/sensenovavl/data/dataset.py280CODE
LOWtraining/sensenovavl/data/multimodal_dataset.py1373CODE
LOWtraining/sensenovavl/data/multimodal_dataset.py226CODE
LOWtraining/sensenovavl/data/multimodal_dataset.py326CODE
LOWtraining/sensenovavl/data/multimodal_dataset.py364CODE
LOWtraining/sensenovavl/data/multimodal_dataset.py643CODE
LOWtraining/sensenovavl/data/multimodal_dataset.py1204CODE
LOWtraining/sensenovavl/data/multimodal_dataset.py1301CODE
LOWtraining/sensenovavl/data/collators.py8CODE
LOWtraining/sensenovavl/data/collators.py52CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py279CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py490CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py723CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py945CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py1052CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py1089CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py1128CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py1329CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py1626CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py734CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py1138CODE
LOWtraining/tools/revert2hf.py407CODE
LOWtraining/sensenovalm/checkpoint/load_funcs.py135CODE
LOWtraining/sensenovalm/checkpoint/load_funcs.py194CODE
LOWtraining/sensenovalm/checkpoint/load_funcs.py263CODE
LOWtraining/sensenovalm/checkpoint/utils.py42CODE
LOWtraining/sensenovalm/checkpoint/components.py129CODE
LOWtraining/sensenovalm/checkpoint/components.py183CODE
LOWtraining/sensenovalm/checkpoint/components.py512CODE
LOWtraining/sensenovalm/checkpoint/components.py624CODE
LOWtraining/sensenovalm/checkpoint/components.py754CODE
LOWtraining/sensenovalm/checkpoint/checkpoint_manager.py95CODE
LOWtraining/sensenovalm/checkpoint/checkpoint_manager.py527CODE
LOWtraining/sensenovalm/checkpoint/checkpoint_manager.py586CODE
LOWtraining/sensenovalm/checkpoint/convert_to_hf.py114CODE
LOWtraining/sensenovalm/core/trainer_builder.py123CODE
LOWtraining/sensenovalm/core/gradient_handler.py49CODE
LOWtraining/sensenovalm/core/parallel/shard.py156CODE
LOWtraining/sensenovalm/core/parallel/comm/isp.py404CODE
LOWtraining/sensenovalm/core/parallel/comm/isp.py428CODE
LOWtraining/sensenovalm/core/parallel/comm/isp.py632CODE
LOWtraining/sensenovalm/core/parallel/comm/isp.py474CODE
LOWtraining/sensenovalm/core/parallel/comm/zero.py25CODE
LOW…/sensenovalm/core/context/process_group_initializer.py667CODE
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Cross-File Repetition18 hits · 90 pts
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HIGHtraining/sensenovavl/utils/utils.py0compute patch coordinates (x, y) args: grid_hw: (b, 2) tensor representing (h, w) per imageSTRING
HIGHsrc/sensenova_u1/models/neo_unify/modeling_neo_vit.py0compute patch coordinates (x, y) args: grid_hw: (b, 2) tensor representing (h, w) per imageSTRING
HIGHsrc/sensenova_u1/models/neo_unify/modeling_neo_chat.py0compute patch coordinates (x, y) args: grid_hw: (b, 2) tensor representing (h, w) per imageSTRING
HIGHtraining/sensenovavl/model/vit_moe/gshard_layer.py0the file has been adapted from the following files: https://github.com/microsoft/deepspeed/blob/master/deepspeed/moe/expSTRING
HIGHtraining/sensenovalm/model/moe/gshard_layer.py0the file has been adapted from the following files: https://github.com/microsoft/deepspeed/blob/master/deepspeed/moe/expSTRING
HIGHtraining/sensenovalm/model/moe/experts.py0the file has been adapted from the following files: https://github.com/microsoft/deepspeed/blob/master/deepspeed/moe/expSTRING
HIGHtraining/sensenovalm/model/moe/dropless_layer_qwen.py0the file has been adapted from the following files: https://github.com/microsoft/deepspeed/blob/master/deepspeed/moe/expSTRING
HIGHtraining/sensenovavl/model/vit_moe/gshard_layer.py0gate module which implements top2gating as described in gshard_. :: gate = topkgate(model_dim, num_experts) l_aux, combiSTRING
HIGHtraining/sensenovalm/model/moe/gshard_layer.py0gate module which implements top2gating as described in gshard_. :: gate = topkgate(model_dim, num_experts) l_aux, combiSTRING
HIGHtraining/sensenovalm/model/moe/dropless_layer_qwen.py0gate module which implements top2gating as described in gshard_. :: gate = topkgate(model_dim, num_experts) l_aux, combiSTRING
HIGHtraining/sensenovalm/model/ops/cross_entropy.py0a simple operator selector, used for compatibility with different platforms such as cuda and ascend, as well as whether STRING
HIGHtraining/sensenovalm/model/ops/attention.py0a simple operator selector, used for compatibility with different platforms such as cuda and ascend, as well as whether STRING
HIGHtraining/sensenovalm/model/ops/linear.py0a simple operator selector, used for compatibility with different platforms such as cuda and ascend, as well as whether STRING
HIGHtraining/sensenovalm/model/ops/rotary_emb.py0a simple operator selector, used for compatibility with different platforms such as cuda and ascend, as well as whether STRING
HIGHexamples/interleave/inference.py0you are a multimodal assistant capable of reasoning with both text and images. you support two modes: think mode: when rSTRING
HIGHevaluation/interleave/OpenING/infer_opening.py0you are a multimodal assistant capable of reasoning with both text and images. you support two modes: think mode: when rSTRING
HIGHevaluation/interleave/Unimmmu/inference_unimmmu.py0you are a multimodal assistant capable of reasoning with both text and images. you support two modes: think mode: when rSTRING
HIGHevaluation/interleave/Realunify/inference_realunify.py0you are a multimodal assistant capable of reasoning with both text and images. you support two modes: think mode: when rSTRING
Self-Referential Comments29 hits · 80 pts
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MEDIUMtraining/sensenovavl/utils/checkpoint.py32 # Create a uniform distribution for jitteringCOMMENT
MEDIUMtraining/sensenovavl/utils/utils.py62 # Create the batch index for each patch (B x patch count)COMMENT
MEDIUMtraining/sensenovavl/model/vit_moe/gshard_layer.py58 # Create a uniform distribution for jitteringCOMMENT
MEDIUMtraining/sensenovavl/model/vit_moe/gshard_layer.py85 # Create a mask for 1st's expert per tokenCOMMENT
MEDIUMtraining/sensenovavl/model/vit_moe/gshard_layer.py163 # Create a mask for 1st's expert per tokenCOMMENT
MEDIUMtraining/sensenovavl/model/vit_moe/gshard_layer.py171 # Create a mask for 2nd's expert per token using Gumbel-max trickCOMMENT
MEDIUMtraining/sensenovavl/model/vit_moe/gshard_layer.py254 # Create a mask by top-k expertsCOMMENT
MEDIUMtraining/sensenovavl/data/dataset.py326# Define the JPEG compression quality range, pre-create all JPEG compression functionsCOMMENT
MEDIUMtraining/sensenovalm/core/context/parallel_context.py143 """This class provides interface functions for users to get the parallel context,STRING
MEDIUMtraining/sensenovalm/core/context/random.py20 """This class is a manager of all random seeds involved in the system."""STRING
MEDIUMtraining/sensenovalm/utils/parallel.py128 r"""This function is changed from colossalai, which is ``sync_model_param``.STRING
MEDIUMtraining/sensenovalm/model/metrics.py199 # Create a mask of valid vocab ids (1 means it needs to be masked).COMMENT
MEDIUMtraining/sensenovalm/model/ops/cross_entropy.py187 # Create a mask of valid vocab ids (1 means it needs to be masked).COMMENT
MEDIUMtraining/sensenovalm/model/moe/gshard_layer.py138# The following functions are extracted and scriptedCOMMENT
MEDIUMtraining/sensenovalm/model/moe/gshard_layer.py183 # Create a mask for 1st's expert per tokenCOMMENT
MEDIUMtraining/sensenovalm/model/moe/gshard_layer.py258 # Create a mask for 1st's expert per tokenCOMMENT
MEDIUMtraining/sensenovalm/model/moe/gshard_layer.py263 # Create a mask for 2nd's expert per token using Gumbel-max trickCOMMENT
MEDIUMtraining/sensenovalm/model/moe/gshard_layer.py333 # Create a mask by top-k expertsCOMMENT
MEDIUMtraining/sensenovalm/model/moe/experts.py51 # The following code is designed for multiple experts.COMMENT
MEDIUMtraining/sensenovalm/model/moe/dropless_layer_qwen.py812 # Create a mask of mapping between global and local tokens where eachCOMMENT
MEDIUMtraining/sensenovalm/train/utils.py303 # Create the param MoE groups, leave param assign to next stepCOMMENT
MEDIUMtraining/sensenovalm/train/pipeline.py297 # This function is needed to make sure parameters that are not splitted by tensor parallelism areCOMMENT
MEDIUMtraining/sensenovalm/data/build_dataloader.py158 # Create the training data loaderCOMMENT
MEDIUMscripts/render_requirements.py63 "# This file is generated from pyproject.toml by scripts/render_requirements.py.",CODE
MEDIUMevaluation/gen/cvtg/unified_metrics_eval.py266 # Create a default zero tensor as placeholderCOMMENT
MEDIUM…uation/interleave/Realunify/inference_realunify_ueg.py89 # Create a small dummy image (32x32 white)COMMENT
MEDIUMsrc/sensenova_u1/models/neo_unify/modeling_neo_vit.py38 # Create the batch index for each patch (B x patch count)COMMENT
MEDIUMsrc/sensenova_u1/models/neo_unify/modeling_neo_chat.py134 # Create the batch index for each patch (B x patch count)COMMENT
MEDIUMsrc/sensenova_u1/models/neo_unify/modeling_qwen3.py1147 # Create the masksCOMMENT
Magic Placeholder Names15 hits · 72 pts
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HIGHdocs/u1.5_preview_CN.md912export OPENAI_API_KEY="your-api-key"CODE
HIGHdocs/u1.5_preview_CN.md965export OPENAI_API_KEY="your-api-key"CODE
HIGHdocs/u1.5_preview.md936export OPENAI_API_KEY="your-api-key"CODE
HIGHdocs/u1.5_preview.md1002export OPENAI_API_KEY="your-api-key"CODE
HIGHdocs/u1.5_best_practices.md57export PE_MODEL_API_KEY="your-api-key"CODE
HIGHdocs/u1.5_best_practices.md133export OPENAI_API_KEY="your-api-key"CODE
HIGHdocs/u1.5_best_practices.md150export OPENAI_API_KEY="your-api-key"CODE
HIGHdocs/u1.5_best_practices_CN.md57export PE_MODEL_API_KEY="your-api-key"CODE
HIGHdocs/u1.5_best_practices_CN.md133export OPENAI_API_KEY="your-api-key"CODE
HIGHdocs/u1.5_best_practices_CN.md150export OPENAI_API_KEY="your-api-key"CODE
HIGHevaluation/docs/interleaved.md93 --api-key your_api_key \CODE
HIGHevaluation/docs/interleaved.md189export OPENING_JUDGE_API_KEY=your_api_keyCODE
HIGHevaluation/gen/bizgeneval/run_bizgeneval.sh11API_KEY="your-api-key"CODE
HIGHevaluation/gen/igenbench/run_igenbench.sh11API_KEY="your-api-key"CODE
HIGHsrc/sensenova_u1_5/caption/caption.py8 export OPENAI_API_KEY="your-api-key"STRING
AI Structural Patterns83 hits · 68 pts
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LOW…sensenovavl_moe_chat/configuration_sensenovavl_chat.py25CODE
LOW…/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py390CODE
LOW…/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py706CODE
LOW…vl/model/sensenovavl_moe_chat/configuration_neo_vit.py63CODE
LOWtraining/sensenovavl/model/vit_moe/gshard_layer.py226CODE
LOWtraining/sensenovavl/model/vit_moe/gshard_layer.py420CODE
LOWtraining/sensenovavl/model/vit_moe/gshard_layer.py565CODE
LOWtraining/sensenovavl/data/multimodal_dataset.py1413CODE
LOWtraining/sensenovavl/data/multimodal_dataset.py62CODE
LOWtraining/sensenovavl/data/multimodal_dataset.py947CODE
LOWtraining/sensenovavl/data/multimodal_dataset.py1108CODE
LOWtraining/sensenovavl/data/multimodal_dataset.py1189CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py83CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py442CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py647CODE
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py826CODE
LOWtraining/sensenovalm/core/parallel/comm/isp.py228CODE
LOWtraining/sensenovalm/core/parallel/comm/tensor.py393CODE
LOWtraining/sensenovalm/core/parallel/comm/utils.py105CODE
LOWtraining/sensenovalm/core/parallel/comm/utils.py127CODE
LOWtraining/sensenovalm/core/scheduler/comm/p2p.py123CODE
LOWtraining/sensenovalm/utils/common.py49CODE
LOWtraining/sensenovalm/utils/common.py209CODE
LOWtraining/sensenovalm/utils/writer.py117CODE
LOWtraining/sensenovalm/solver/optimizer/utils.py449CODE
LOWtraining/sensenovalm/model/modeling_qwen3_moe_mot.py72CODE
LOWtraining/sensenovalm/model/modeling_qwen3_moe_mot.py436CODE
LOWtraining/sensenovalm/model/ops/attention.py689CODE
LOWtraining/sensenovalm/model/ops/attention.py723CODE
LOWtraining/sensenovalm/model/ops/norm.py114CODE
LOW…ash_attn/zigzag_ring_flash_attn_with_sliding_window.py515CODE
LOW…ash_attn/zigzag_ring_flash_attn_with_sliding_window.py552CODE
LOW…ash_attn/zigzag_ring_flash_attn_with_sliding_window.py588CODE
LOWtraining/sensenovalm/model/modules/linear.py1007CODE
LOWtraining/sensenovalm/model/modules/mlp.py211CODE
LOWtraining/sensenovalm/model/modules/mlp.py63CODE
LOWtraining/sensenovalm/model/modules/mlp.py143CODE
LOWtraining/sensenovalm/model/modules/mlp.py208CODE
LOWtraining/sensenovalm/model/modules/mha.py136CODE
LOWtraining/sensenovalm/model/modules/mha.py431CODE
LOWtraining/sensenovalm/model/modules/mha.py790CODE
LOWtraining/sensenovalm/model/modules/mha.py1259CODE
LOWtraining/sensenovalm/model/modules/mha.py1568CODE
LOWtraining/sensenovalm/model/moe/gshard_layer.py415CODE
LOWtraining/sensenovalm/model/moe/gshard_layer.py522CODE
LOWtraining/sensenovalm/model/moe/moe.py56CODE
LOWtraining/sensenovalm/model/moe/moe.py147CODE
LOWtraining/sensenovalm/model/moe/moe.py229CODE
LOWtraining/sensenovalm/model/moe/moe.py304CODE
LOWtraining/sensenovalm/model/moe/dropless_layer_qwen.py168CODE
LOWtraining/sensenovalm/model/moe/dropless_layer_qwen.py660CODE
LOWtraining/sensenovalm/data/tokenized/packed_dataset.py453CODE
LOWtraining/sensenovalm/data/tokenized/packed_dataset.py519CODE
LOWtraining/sensenovalm/data/tokenized/packed_dataset.py636CODE
LOWexamples/t2i/inference.py83CODE
LOWexamples/t2i/inference.py134CODE
LOWexamples/editing/inference.py215CODE
LOWexamples/editing/inference.py247CODE
LOWexamples/vqa/inference.py32CODE
LOWexamples/interleave/inference.py138CODE
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Docstring Block Structure12 hits · 60 pts
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HIGHtraining/sensenovavl/data/dataset.py79calculate the number of frames for video used for model inputs. Args: ele (dict): a dict contains the confiSTRING
HIGHtraining/sensenovalm/core/parallel/comm/isp.py759 Registers a callback function that specifies a prerequisite condition for prefetching parameters beforeSTRING
HIGHtraining/sensenovalm/core/context/parallel_context.py91Reads a python file and constructs a corresponding :class:`Config` object. Args: filename (str): NaSTRING
HIGHtraining/sensenovalm/model/registry.py44Retrieves a module with name `module_name` and returns the module if it has already been registered before. STRING
HIGHtraining/sensenovalm/data/train_state.py30 Args: dataloader (torch.utils.data.Dataset): Raises: ValueError: Only support tokenized/streamSTRING
HIGHtraining/sensenovalm/data/tokenized/collaters.py7 Collate function for packed input sequences. Args: batch (List[Dict]): List of dictionaries representiSTRING
HIGHtraining/sensenovalm/data/tokenized/dataset.py12 Return a dictionary of Datasets from a folder containing data files for validation. Args: folder (str)STRING
HIGH…u1/prompt_enhance/adapters/chat_completions_adapter.py154Extract the assistant message text from a chat/completions response. Handles both plain-string and list-of-contSTRING
HIGH…u1/prompt_enhance/adapters/chat_completions_adapter.py191Call the ``/chat/completions`` endpoint with vision content. Args: user_prompt: User-facing text inSTRING
HIGHsrc/sensenova_u1/prompt_enhance/adapters/utils.py13Read raw image bytes from a path or return bytes unchanged. Args: image: File path to an image, or raw imagSTRING
HIGH…senova_u1/prompt_enhance/adapters/anthropic_adapter.py160Extract the assistant message text from Anthropic Messages response. Handles responses with or without content STRING
HIGH…senova_u1/prompt_enhance/adapters/anthropic_adapter.py193Call the ``/v1/messages`` endpoint with vision content. Args: user_prompt: User-facing text instrucSTRING
Modern Structural Boilerplate48 hits · 48 pts
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LOWtraining/sensenovavl/utils/__init__.py8__all__ = [CODE
LOW…ing/sensenovavl/model/sensenovavl_moe_chat/__init__.py12__all__ = [CODE
LOWtraining/sensenovavl/model/vit_moe/__init__.py9__all__ = ["new_moe_layer"]CODE
LOWtraining/sensenovavl/data/__init__.py4__all__ = ["build_train_loader_with_data_type", "get_train_state"]CODE
LOWtraining/sensenovavl/data/distributed_sampler.py12__all__ = ["DistributedSampler"]CODE
LOWtraining/sensenovavl/data/distributed_sampler.py140 def set_epoch(self, epoch: int, consumed_samples=0) -> None:STRING
LOWtraining/sensenovalm/__init__.py4__all__ = [CODE
LOWtraining/sensenovalm/checkpoint/__init__.py3__all__ = ["CheckpointManager"]CODE
LOWtraining/sensenovalm/checkpoint/checkpoint_manager.py658 def set_averaged_model(self, averaged_model) -> None:CODE
LOWtraining/sensenovalm/core/model_average.py171 def set_extra_state(self, state: Dict[str, object]) -> None:STRING
LOWtraining/sensenovalm/core/__init__.py5__all__ = [CODE
LOWtraining/sensenovalm/core/context/__init__.py33__all__ = [CODE
LOWtraining/sensenovalm/core/scheduler/__init__.py9__all__ = [CODE
LOWtraining/sensenovalm/core/scheduler/comm/__init__.py17__all__ = [CODE
LOWtraining/sensenovalm/initialize/__init__.py10__all__ = [CODE
LOWtraining/sensenovalm/solver/__init__.py7__all__ = ["Beta2Scheduler", "FineTuneCosineAnnealingWarmupLR", "HybridZeroOptimizer"]CODE
LOWtraining/sensenovalm/solver/optimizer/store.py174 def set_param_to_rank(self, tensor: Tensor, rank: int) -> None:CODE
LOWtraining/sensenovalm/solver/optimizer/__init__.py7__all__ = ["FSDPadaptOptimizer", "HybridZeroOptimizer"]CODE
LOWtraining/sensenovalm/solver/schedulers/__init__.py8__all__ = ["Beta2Scheduler", "WarmupScheduler", "CosineAnnealingWarmupLR", "FineTuneCosineAnnealingWarmupLR"]CODE
LOWtraining/sensenovalm/accelerator/__init__.py5__all__ = [CODE
LOWtraining/sensenovalm/model/losses/__init__.py3__all__ = [CODE
LOW…ning/sensenovalm/model/ops/ring_flash_attn/__init__.py7__all__ = [CODE
LOWtraining/sensenovalm/model/ops/ring_flash_attn/utils.py16__all__ = ["update_out_and_lse", "RingComm"]CODE
LOWtraining/sensenovalm/model/moe/__init__.py5__all__ = [CODE
LOWtraining/sensenovalm/model/moe/monitor/__init__.py5__all__ = ["BaseMonitor", "MoEMonitor"]CODE
LOWtraining/sensenovalm/train/__init__.py13__all__ = [CODE
LOWtraining/sensenovalm/data/__init__.py7__all__ = [CODE
LOWtraining/sensenovalm/data/tokenized/__init__.py6__all__ = [CODE
LOWtraining/sensenovalm/data/tokenized/batch_sampler.py103 def set_epoch(self, epoch: int) -> None:CODE
LOWexamples/interleave/inference.py70def _set_seed(seed: int) -> None:CODE
LOWapps/comfyui/__init__.py33__all__ = ["comfy_entrypoint", "WEB_DIRECTORY"]CODE
LOWsrc/sensenova_u1/__init__.py27__all__ = [CODE
LOWsrc/sensenova_u1/utils/__init__.py39__all__ = [CODE
LOWsrc/sensenova_u1/utils/comparison.py11__all__ = ["ensure_cjk_font", "make_comparison", "save_compare"]CODE
LOWsrc/sensenova_u1/utils/layer_offload.py49logger = logging.getLogger(__name__)CODE
LOWsrc/sensenova_u1/utils/layer_offload.py607 def _setup(self) -> None:CODE
LOWsrc/sensenova_u1/utils/layer_offload.py863 def set_inference_phase(self, phase: str) -> None:CODE
LOWsrc/sensenova_u1/utils/profiler.py123 def set_config(self, config: Mapping[str, object]) -> None:CODE
LOWsrc/sensenova_u1/utils/profiler.py219 def update_last_batch(self, n: int) -> None:CODE
LOWsrc/sensenova_u1/models/__init__.py5__all__ = ["neo_unify"]CODE
LOWsrc/sensenova_u1/models/neo_unify/modeling_qwen3_moe.py541__all__ = [CODE
LOWsrc/sensenova_u1/models/neo_unify/__init__.py16__all__ = [CODE
LOWsrc/sensenova_u1/models/neo_unify/modeling_qwen3.py1291__all__ = [CODE
LOWsrc/sensenova_u1/prompt_enhance/__init__.py10__all__ = [CODE
LOW…u1/prompt_enhance/adapters/chat_completions_adapter.py31logger = logging.getLogger(__name__)CODE
LOWsrc/sensenova_u1/prompt_enhance/adapters/__init__.py5__all__ = ["AnthropicVlmAdapter", "ChatCompletionsVlmAdapter", "VlmAdapter"]CODE
LOW…senova_u1/prompt_enhance/adapters/anthropic_adapter.py30logger = logging.getLogger(__name__)CODE
LOWsrc/sensenova_u1_5/edit/__init__.py5__all__ = ["enhance_edit_prompt"]CODE
Cross-Language Confusion4 hits · 32 pts
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HIGHevaluation/interleave/BabyVision/eval_babyvision.py49- If the extracted answer is empty or null, return false.CODE
HIGHevaluation/interleave/OpenING/eval_opening.py952 help="Retry existing results whose score metrics are missing, null, or non-numeric",CODE
HIGHevaluation/interleave/OpenING/summarize_GPT_scores.py60 # Check if score is null, empty, or contains null scoresCOMMENT
HIGHevaluation/interleave/OpenING/summarize_GPT_scores.py60 # Check if score is null, empty, or contains null scoresCOMMENT
Over-Commented Block38 hits · 32 pts
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LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py41# 其中,内层不设置token数量限制,但是会根据图像数量限制对文档进行切割COMMENT
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py1581 # data_index: torch.LongTensor, # (seq_len,)COMMENT
LOW…ining/sensenovavl/data/dataset_interleaved_iterable.py1601 # assert num_tokens > 0COMMENT
LOWtraining/sensenovalm/utils/parallel.py241 # f"and rank{i}:{value_list[i]}",COMMENT
LOW…ning/sensenovalm/solver/optimizer/hybrid_zero_optim.py221 flat_tensor = flatten(tensor_list)COMMENT
LOWtraining/sensenovalm/model/modeling_qwen3_moe_mot.py981 # router_prob_per_expert = torch.mean(routing_weights, dim=1)COMMENT
LOWtraining/sensenovalm/model/modeling_qwen3_moe_mot.py1001COMMENT
LOWtraining/sensenovalm/model/modeling_qwen3_moe_mot.py1021COMMENT
LOWtraining/sensenovalm/model/modeling_qwen3_moe_mot.py1041 # l_aux_image = torch.sum(image_tokens_per_expert * image_router_prob_per_expert) * num_expertsCOMMENT
LOWtraining/sensenovalm/model/modeling_qwen3_moe_mot.py1061COMMENT
LOWtraining/sensenovalm/model/modeling_qwen3_moe_mot.py1081 # logger.info(COMMENT
LOWtraining/sensenovalm/model/ops/cross_entropy.py1# --------------------------------------------------------COMMENT
LOWtraining/sensenovalm/model/modules/embedding.py1#!/usr/bin/env pythonCOMMENT
LOWtraining/sensenovalm/model/modules/mha.py1581 # k_t[image_gen_indicators] = self.wk_mot_gen(x[image_gen_indicators])COMMENT
LOWtraining/sensenovalm/model/modules/mha.py1601 # kv_seq_len = v.size(0)COMMENT
LOWtraining/sensenovalm/model/modules/mha.py1621 # q_h, k_h = apply_norm(q_h, k_h, self.q_norm_h, self.q_norm_h_mot_gen, self.k_norm_h, self.k_norm_h_mot_genCOMMENT
LOWtraining/sensenovalm/model/modules/mha.py1641COMMENT
LOW.github/workflows/publish-comfyui.yml1# Sync apps/comfyui/ to the standalone publish mirrorCOMMENT
LOWevaluation/easi/scripts/setup.sh1#!/usr/bin/env bashCOMMENT
LOWevaluation/easi/scripts/setup.sh21# evaluation/easi/EASI/VLMEvalKit/vlmeval/sensenova_models.pyCOMMENT
LOWevaluation/easi/scripts/setup.sh241 # Re-activate LightLLM venv only if we set it up earlier.COMMENT
LOWevaluation/easi/scripts/serve_lb.sh1#!/usr/bin/env bashCOMMENT
LOWevaluation/easi/scripts/download_weights.sh1#!/usr/bin/env bashCOMMENT
LOWevaluation/easi/scripts/serve.sh1#!/usr/bin/env bashCOMMENT
LOWevaluation/easi/scripts/serve.sh21#COMMENT
LOWevaluation/easi/scripts/serve.sh41# LIGHTLLM_LOG_LEVEL debug|info|warning|error (default: info)COMMENT
LOWevaluation/interleave/Unimmmu/run.sh1#!/bin/bashCOMMENT
LOWevaluation/interleave/Realunify/run.sh1#!/bin/bashCOMMENT
LOWapps/comfyui/requirements.txt1httpxCOMMENT
LOWsrc/sensenova_u1/utils/param_count.py41 model_path: strCOMMENT
LOW…c/sensenova_u1/models/neo_unify/modeling_fm_modules.py81 h = self.mlp(h)COMMENT
LOW…c/sensenova_u1/models/neo_unify/modeling_fm_modules.py101# super().__init__()COMMENT
LOW…c/sensenova_u1/models/neo_unify/modeling_fm_modules.py121COMMENT
LOW…c/sensenova_u1/models/neo_unify/modeling_fm_modules.py141# nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)COMMENT
LOWsrc/sensenova_u1/models/neo_unify/modeling_qwen3.py501 )COMMENT
LOWsrc/sensenova_u1/models/neo_unify/modeling_qwen3.py521 # query_states_t, query_states_hw = query_states.chunk(2, dim=-1)COMMENT
LOWsrc/sensenova_u1/models/neo_unify/modeling_qwen3.py541 # query_states_w, key_states_w = apply_rotary_pos_emb(query_states_w, key_states_w, cos_w, sin_w)COMMENT
LOWsrc/sensenova_u1/models/neo_unify/modeling_qwen3.py561 # value_states = torch.cat([past_v, value_states], dim=2)COMMENT
Modern AI Meta-Vocabulary8 hits · 24 pts
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MEDIUMtraining/sensenovavl/data/conversation.py57 # The number of few shot examplesCOMMENT
MEDIUMdocs/u1_infographic_model.md585<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; oCODE
MEDIUMdocs/u1_infographic_model.md594<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; oCODE
MEDIUMdocs/u1_infographic_model.md594<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; oCODE
MEDIUMdocs/u1_infographic_model_CN.md583<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; oCODE
MEDIUMdocs/u1_infographic_model_CN.md592<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; oCODE
MEDIUMdocs/u1_infographic_model_CN.md592<td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; oCODE
MEDIUMsrc/sensenova_u1/models/neo_unify/conversation.py52 # The number of few shot examplesCOMMENT
Verbosity Indicators7 hits · 14 pts
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LOWtraining/sensenovavl/data/cfg_cond_drop_utils.py59 # Step 1: Build image spans within user intervalCOMMENT
LOWtraining/sensenovavl/data/cfg_cond_drop_utils.py78 # Step 2: Merge overlapping or adjacent spansCOMMENT
LOWtraining/sensenovavl/data/cfg_cond_drop_utils.py94 # Step 3: Compute complement = regions to maskCOMMENT
LOWevaluation/interleave/Realunify/inference_realunify.py535 # Step 1: Edit imageCOMMENT
LOWevaluation/interleave/Realunify/inference_realunify.py569 # Step 2: Image QA using edited imageCOMMENT
LOW…uation/interleave/Realunify/inference_realunify_ueg.py182 # Step 1: Understanding - refine promptCOMMENT
LOW…uation/interleave/Realunify/inference_realunify_ueg.py185 # Step 2: T2I generationCOMMENT
Structural Annotation Overuse7 hits · 14 pts
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LOWtraining/sensenovavl/data/cfg_cond_drop_utils.py59 # Step 1: Build image spans within user intervalCOMMENT
LOWtraining/sensenovavl/data/cfg_cond_drop_utils.py78 # Step 2: Merge overlapping or adjacent spansCOMMENT
LOWtraining/sensenovavl/data/cfg_cond_drop_utils.py94 # Step 3: Compute complement = regions to maskCOMMENT
LOWevaluation/interleave/Realunify/inference_realunify.py535 # Step 1: Edit imageCOMMENT
LOWevaluation/interleave/Realunify/inference_realunify.py569 # Step 2: Image QA using edited imageCOMMENT
LOW…uation/interleave/Realunify/inference_realunify_ueg.py182 # Step 1: Understanding - refine promptCOMMENT
LOW…uation/interleave/Realunify/inference_realunify_ueg.py185 # Step 2: T2I generationCOMMENT
AI Slop Vocabulary6 hits · 13 pts
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LOWtraining/sensenovalm/checkpoint/components.py740 # for new added empty group, since it has no params, just create it fakelyCOMMENT
MEDIUM…/sensenovalm/core/context/process_group_initializer.py35 # this is added to facilitate amp and grad clipping in hybrid parallelCOMMENT
LOWtraining/sensenovalm/utils/megatron_timers.py97 # polutes the runs list, so we just add each as a scalarCOMMENT
MEDIUM…yui/example_workflows/infographic_series_t2i_edit.json250 "You are a world-renowned \"Senior Visual Information Architect\" and \"AI Image Prompt Engineering Expert.\" YoCODE
MEDIUM…s/comfyui/example_workflows/sensenova_api_gen_t2i.json117 "You are a world-renowned \"Senior Visual Information Architect\" and \"AI Image Prompt Engineering Expert.\" YoCODE
MEDIUMsrc/sensenova_u1/utils/param_count.py166 # parameter, which is robust to mixed-dtype checkpoints (e.g. normsCOMMENT
Hallucination Indicators1 hit · 10 pts
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CRITICALtraining/sensenovalm/train/pipeline.py765 scaler = engine.optimizer.optim.grad_scaler._scale.item()CODE
Redundant / Tautological Comments5 hits · 9 pts
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LOW…ining/sensenovalm/core/scheduler/pipeline_scheduler.py1582 # Check if it is the last model chunk of the last pipeline stage, no need to send forward output.COMMENT
LOW…ining/sensenovalm/core/scheduler/pipeline_scheduler.py1587 # Check if it needs to receive the results from the previous rank.COMMENT
LOWevaluation/interleave/OpenING/summarize_GPT_scores.py60 # Check if score is null, empty, or contains null scoresCOMMENT
LOWevaluation/interleave/OpenING/summarize_GPT_scores.py68 # Check if required metrics exist and any metric_data['Score'] is NoneCOMMENT
LOWevaluation/interleave/OpenING/summarize_GPT_scores.py123 # Print resultsCOMMENT
Example Usage Blocks3 hits · 4 pts
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LOWevaluation/easi/scripts/setup.sh34# Usage:COMMENT
LOWevaluation/easi/scripts/download_weights.sh4# Usage:COMMENT
LOWevaluation/easi/scripts/serve.sh17# Usage:COMMENT