AI Slop Code Risk Monitor

huggingface/candle

SlopCodeMonitor badge for huggingface/candle Adjusted legacy signal; badge bands: 25 / 50 / 75 (different from dashboard bands).

Minimalist ML framework for Rust

2.6 Low pattern signal View on GitHub

Open-Source Code Quality, Security and Technical Debt Review

This report summarizes scanner evidence for huggingface/candle, a Rust project with 21,255 GitHub stars. SynthScan examined 258,711 lines of code across 939 source files, recording 502 pattern matches distributed across 15 syntactic categories. The legacy adjusted pattern signal is 2.6 (Low pattern signal); it is not an authorship estimate.

Use the findings to ask whether maintainers can explain intent, verify dependencies, simplify unnecessary scaffolding, and focus review effort. Risk Payload v1 adds ownership, supply-chain, over-engineering, and review-effort vectors when available. These are prompts for human verification, not probabilities, security findings, or measured maintenance costs.

2.6
Adjusted Signal
2.6
Raw Pattern Score
100%
Time Factor
2026-10-31
Last Push
21.3K
Stars
Rust
Language
258.7K
Lines of Code
939
Files
502
Pattern Hits
2026-10-31
Scan Date
0.00
HC Hit Rate

Risk Review · Payload v1

Use each vector to guide manual verification of the underlying findings.

Payload schema: 1.0.0 · Engine: 2.0.0 · Scoring: heuristic-density-v1 · Scan date: 2026-10-31.

Cognitive Ownership Risk 8.7/100 · LOW

Can maintainers explain intent, assumptions, and trade-offs? 415 finding(s).

Hallucinated Dependency & Supply Chain Risk 0.5/100 · LOW

Are imports declared and package/API names verified? 28 finding(s).

Agent Over-Engineering Ratio 4.0/100 · LOW

Are wrappers, guards, and repeated structures necessary? 101 finding(s).

PR Review Velocity Impact 11.5/100 · LOW

Which changes need focused human review? 502 finding(s).

What These Metrics Mean

Adjusted Signal
Legacy pattern score normalized per 1,000 scanned lines and multiplied by the repository-level time factor. It is not an estimate of AI authorship.
Raw Pattern Score
Sum of severity- and context-weighted pattern findings before applying the time factor.
Time Factor
A coarse multiplier based on repository last-push metadata. It does not identify when individual code was written.
Pattern Hits
Count of configured pattern findings. Review each location and surrounding code before acting.
HC Hit Rate
High- and critical-severity findings per scanned file. Severity is a configured detector weight, not proof of a vulnerability or authorship.
Lines / Files
Scan coverage denominators used by the legacy normalized score.

Pattern-Signal History

This chart tracks the legacy adjusted pattern score across scan runs. Changes can reflect source changes, scan coverage, or detector/scoring versions; they do not measure AI adoption or prove that a team refactored code. Compare runs only when their scoring versions are compatible. The dashed line tracks the raw pattern-hit count.

Severity Breakdown

Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.

CRITICAL 0HIGH 1MEDIUM 63LOW 438

Directory Score Breakdown

This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.

Pattern Findings

The scanner identified 502 distinct pattern matches across 15 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. 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 configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.

Over-Commented Block319 hits · 309 pts
SeverityFileLineSnippetContext
LOWcandle-transformers/src/utils.rs1//! Shared utilities: repeat_kv, repeat_penalty, causal mask.COMMENT
LOWcandle-transformers/src/utils.rs21 .collect();COMMENT
LOWcandle-transformers/src/models/starcoder2.rs1//! StarCoder model implementation with quantization support.COMMENT
LOWcandle-transformers/src/models/quantized_mixformer.rs1//! Module containing quantized MixFormer model implementation.COMMENT
LOWcandle-transformers/src/models/parler_tts.rs1//! Parler Model implementation for parler_tts text-to-speech synthesisCOMMENT
LOWcandle-transformers/src/models/eva2.rs1//! EVA-2 inference implementation.COMMENT
LOWcandle-transformers/src/models/quantized_moondream.rs1//! Implementation of a quantized Moondream vision language model.COMMENT
LOWcandle-transformers/src/models/dinov2.rs1//! Implementation of the DINOv2 models from Meta Research.COMMENT
LOWcandle-transformers/src/models/dinov2.rs21//!COMMENT
LOWcandle-transformers/src/models/csm.rs1//! Implementation of the Conversational Speech Model (CSM) from SesameCOMMENT
LOW…e-transformers/src/models/quantized_recurrent_gemma.rs1//! Recurrent Gemma model implementation with quantization support.COMMENT
LOWcandle-transformers/src/models/quantized_phi.rs1//! Phi2 model implementation with quantization support.COMMENT
LOWcandle-transformers/src/models/mobilenetv4.rs1//! # MobileNet-v4COMMENT
LOWcandle-transformers/src/models/rwkv_v7.rs1//! RWKV v7 "Goose" (x070) model implementation.COMMENT
LOWcandle-transformers/src/models/paligemma.rs1//! Multimodal multi-purpose model combining Gemma-based language model with SigLIP image understandingCOMMENT
LOWcandle-transformers/src/models/t5.rs1//! T5 model implementation.COMMENT
LOWcandle-transformers/src/models/t5.rs21//! ```bashCOMMENT
LOWcandle-transformers/src/models/t5.rs41//! --prompt "<2de> How are you, my friend?" \COMMENT
LOWcandle-transformers/src/models/phi3.rs1//! Microsoft Phi-3 model implementationCOMMENT
LOWcandle-transformers/src/models/bigcode.rs1//! BigCode implementation in Rust based on the GPT-BigCode model.COMMENT
LOWcandle-transformers/src/models/rwkv_v6.rs1//! RWKV v6 model implementation.COMMENT
LOWcandle-transformers/src/models/quantized_blip.rs1//! BLIP model implementation with quantization support.COMMENT
LOWcandle-transformers/src/models/quantized_rwkv_v5.rs1//! RWKV v5 model implementation with quantization support.COMMENT
LOWcandle-transformers/src/models/vgg.rs1//! VGG-16 model implementation.COMMENT
LOWcandle-transformers/src/models/recurrent_gemma.rs1//! Recurrent Gemma model implementationCOMMENT
LOWcandle-transformers/src/models/quantized_mpt.rs1//! Quantized MPT model implementation.COMMENT
LOWcandle-transformers/src/models/stable_lm.rs1//! StableLM model implementation.COMMENT
LOWcandle-transformers/src/models/quantized_stable_lm.rs1//! Module for quantized StableLM implementation.COMMENT
LOWcandle-transformers/src/models/mixtral.rs1//! Mixtral Model, a sparse mixture of expert model based on the Mistral architectureCOMMENT
LOWcandle-transformers/src/models/rwkv_v5.rs1//! RWKV v5 model implementation.COMMENT
LOWcandle-transformers/src/models/rwkv_v5.rs21//! ```bashCOMMENT
LOWcandle-transformers/src/models/olmo.rs1//! OLMo (Open Language Model) implementationCOMMENT
LOWcandle-transformers/src/models/qwen2.rs1//! Qwen2 model implementation with quantization support.COMMENT
LOWcandle-transformers/src/models/mod.rs1//! Candle implementations for various deep learning modelsCOMMENT
LOWcandle-transformers/src/models/dinov2reg4.rs1//! Implementation of the DINOv2 revision (4 regularization)COMMENT
LOWcandle-transformers/src/models/dinov2reg4.rs21//!COMMENT
LOWcandle-transformers/src/models/quantized_rwkv_v6.rs1//! RWKV v6 model implementation with quantization support.COMMENT
LOWcandle-transformers/src/models/phi.rs1//! Microsoft Phi model implementationCOMMENT
LOWcandle-transformers/src/models/convnext.rs1//! ConvNeXt implementation.COMMENT
LOWcandle-transformers/src/models/segformer.rs1//! Segformer model implementation for semantic segmentation and image classification.COMMENT
LOWcandle-transformers/src/models/quantized_gemma3.rs1//! Gemma 3 model implementation with quantization support.COMMENT
LOWcandle-transformers/src/models/qwen2_moe.rs1//! Qwen2 model implementation with Mixture of Experts support.COMMENT
LOWcandle-transformers/src/models/quantized_qwen2.rs1//! Qwen2 model implementation with quantization support.COMMENT
LOWcandle-transformers/src/models/quantized_t5.rs1//! T5 model implementation with quantization support.COMMENT
LOWcandle-transformers/src/models/quantized_blip_text.rs1//! Quantized BLIP text module implementation.COMMENT
LOWcandle-transformers/src/models/quantized_mistral.rs1//! Mistral model implementation with quantization support.COMMENT
LOWcandle-transformers/src/models/bert.rs1//! BERT (Bidirectional Encoder Representations from Transformers)COMMENT
LOWcandle-transformers/src/models/efficientvit.rs1//! EfficientViT (MSRA) inference implementation based on timm.COMMENT
LOWcandle-transformers/src/models/efficientvit.rs21//! --example efficientvit \COMMENT
LOWcandle-transformers/src/models/quantized_llama2_c.rs1//! Quantized Llama2 model implementation.COMMENT
LOWcandle-transformers/src/models/mobileclip.rs1//! Mobile CLIP model, combining a lightweight vision encoder with a text encoderCOMMENT
LOWcandle-transformers/src/models/quantized_llama.rs1//! Quantized llama model implementation.COMMENT
LOWcandle-transformers/src/models/quantized_llama.rs521 span,COMMENT
LOWcandle-transformers/src/models/quantized_llama.rs621COMMENT
LOWcandle-transformers/src/models/trocr.rs1//! TrOCR model implementation.COMMENT
LOWcandle-transformers/src/models/nomic_bert.rs1//! # NomicBERTCOMMENT
LOWcandle-transformers/src/models/mpt.rs1//! Module implementing the MPT (Multi-Purpose Transformer) modelCOMMENT
LOWcandle-transformers/src/models/yi.rs1//! Yi model implementation.COMMENT
LOWcandle-transformers/src/models/quantized_metavoice.rs1//! Quantized MetaVoice model implementation.COMMENT
LOWcandle-transformers/src/models/stella_en_v5.rs1//! Stella v5 model implementation.COMMENT
259 more matches not shown…
Decorative Section Separators51 hits · 153 pts
SeverityFileLineSnippetContext
MEDIUMcandle-transformers/src/models/rwkv_v7.rs23// ─── Config ──────────────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/rwkv_v7.rs77// ─── State ───────────────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/rwkv_v7.rs155// ─── Tokenizer ───────────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/rwkv_v7.rs159// ─── Helpers ─────────────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/rwkv_v7.rs191// ─── TimeMix (Attention) ─────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/rwkv_v7.rs458// ─── ChannelMix (FFN) ────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/rwkv_v7.rs549// ─── DeaAttention (v7b only) ─────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/rwkv_v7.rs724// ─── Block ───────────────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/rwkv_v7.rs831// ─── Model ───────────────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/quantized_llama.rs604 // ── Mask shape tests ──────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/quantized_llama.rs622 // ── Mask value tests ──────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/quantized_llama.rs672 // ── Mask broadcast compatibility test ─────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/config.rs5// ── Text config defaults ────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/config.rs50// ── Rope parameters ─────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/config.rs68// ── Gemma4TextConfig ────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/config.rs147// ── Vision config defaults ──────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/config.rs189// ── Gemma4VisionConfig ──────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/config.rs236// ── Audio config defaults ───────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/config.rs302// ── Gemma4AudioConfig ───────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/config.rs374// ── Top-level config defaults ───────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/config.rs386// ── Gemma4Config ────────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/vision.rs11// ── RmsNorm (Gemma-style) ───────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/vision.rs52// ── 2D Vision Rotary Embedding ──────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/vision.rs114// ── PatchEmbedder ───────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/vision.rs185// ── VisionAttention ─────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/vision.rs275// ── VisionMlp ───────────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/vision.rs310// ── VisionEncoderLayer ──────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/vision.rs368// ── VisionPooler ────────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/vision.rs437// ── VisionTower (public) ────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/mod.rs95 // ── Vision embedding injection ──────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/mod.rs118 // ── Audio embedding injection ───────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/audio.rs11// ── RmsNorm (standard, no +1 offset for audio) ─────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/audio.rs41// ── LayerNorm (for SSCP conv blocks) ────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/audio.rs67// ── SSCP Conv Blocks ────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/audio.rs170// ── SubSampleConvProjection ─────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/audio.rs225// ── Relative Position Embedding ─────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/audio.rs367// ── Conformer Attention ─────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/audio.rs645// ── Conformer FeedForward ───────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/audio.rs699// ── Conformer LightConv1d ───────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/audio.rs772// ── ConformerBlock ──────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/audio.rs808// ── AudioModel (public) ─────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/text.rs12// ── RmsNorm (Gemma-style with +1 offset) ────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/text.rs53// ── RotaryEmbedding (standard, for sliding layers) ──────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/text.rs100// ── ProportionalRotaryEmbedding (for global/full layers) ────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/text.rs153// ── MLP ─────────────────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/text.rs192// ── Flash attention ─────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/text.rs210// ── KvCache ─────────────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/text.rs218// ── Attention ───────────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/text.rs386// ── DecoderLayer ────────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/text.rs477// ── Causal mask ─────────────────────────────────────────────────────────────COMMENT
MEDIUMcandle-transformers/src/models/gemma4/text.rs515// ── TextModel ───────────────────────────────────────────────────────────────COMMENT
Hyper-Verbose Identifiers42 hits · 46 pts
SeverityFileLineSnippetContext
LOWcandle-wasm-examples/quant-qwen3/serve.py85def find_or_download_tokenizer():CODE
LOWcandle-pyo3/stub.py127 def process_additional_function(fn):CODE
LOW⚡candle-pyo3/tests/native/test_tensor.py8def test_tensor_can_be_constructed():CODE
LOW⚡candle-pyo3/tests/native/test_tensor.py13def test_tensor_can_be_constructed_from_list():CODE
LOW⚡candle-pyo3/tests/native/test_tensor.py18def test_tensor_can_be_constructed_from_list_of_lists():CODE
LOW⚡candle-pyo3/tests/native/test_tensor.py23def test_tensor_can_be_quantized():CODE
LOWcandle-pyo3/tests/native/test_tensor.py43def test_tensor_can_be_indexed():CODE
LOWcandle-pyo3/tests/native/test_tensor.py51def test_tensor_can_be_sliced():CODE
LOWcandle-pyo3/tests/native/test_tensor.py87def test_tensor_supports_equality_operations_with_scalars():CODE
LOWcandle-pyo3/tests/native/test_tensor.py109def test_tensor_supports_equality_operations_with_tensors():CODE
LOWcandle-pyo3/tests/native/test_tensor.py133def test_tensor_equality_operations_can_broadcast():CODE
LOWcandle-pyo3/tests/native/test_tensor.py145def test_tensor_can_be_hashed():CODE
LOWcandle-pyo3/tests/native/test_tensor.py153def test_tensor_can_be_expanded_with_none():CODE
LOWcandle-pyo3/tests/native/test_tensor.py168def test_tensor_can_be_index_via_tensor():CODE
LOWcandle-pyo3/tests/native/test_tensor.py179def test_tensor_can_be_index_via_list():CODE
LOWcandle-pyo3/tests/native/test_tensor.py190def test_tensor_can_be_cast_via_to():CODE
LOWcandle-pyo3/tests/native/test_tensor.py212def test_tensor_can_be_moved_via_to():CODE
LOWcandle-pyo3/tests/native/test_tensor.py234def test_tensor_can_be_moved_and_cast_via_to():CODE
LOWcandle-pyo3/tests/native/test_tensor.py272def test_tensor_can_be_subtracted():CODE
LOWcandle-pyo3/tests/native/test_tensor.py290def test_tensor_can_be_multiplied():CODE
LOWcandle-pyo3/tests/native/test_tensor.py308def test_tensor_can_be_divided():CODE
LOWcandle-pyo3/tests/native/test_utils.py10def test_can_roundtrip_safetensors():CODE
LOWcandle-pyo3/tests/native/test_shape.py6def test_absolute_shapes_are_valid():CODE
LOWcandle-pyo3/tests/native/test_shape.py17def test_relative_shapes_are_valid():CODE
LOWcandle-pyo3/tests/bindings/test_module.py9def test_module_can_be_constructed():CODE
LOWcandle-pyo3/tests/bindings/test_module.py18def test_module_registers_tensors():CODE
LOWcandle-pyo3/tests/bindings/test_module.py30def test_module_registers_submodules():CODE
LOWcandle-pyo3/tests/bindings/test_module.py45def test_module_can_dump_statedict():CODE
LOWcandle-pyo3/tests/bindings/test_module.py61def test_module_can_load_statedict():CODE
LOWcandle-pyo3/tests/bindings/test_module.py77def test_module_throws_on_shape_mismatch():CODE
LOWcandle-pyo3/tests/bindings/test_module.py92def test_module_throws_on_missing_key():CODE
LOWcandle-pyo3/tests/bindings/test_module.py108def test_module_can_load_quantized_tensors():CODE
LOWcandle-pyo3/tests/bindings/test_module.py124def test_module_dequantizes_tensors_automatically():CODE
LOWcandle-pyo3/tests/bindings/test_module.py139def test_module_can_be_moved_to_cuda():CODE
LOWcandle-pyo3/tests/bindings/test_module.py151def test_module_can_be_moved_from_cuda_to_cpu():CODE
LOW⚡candle-pyo3/tests/bindings/test_linear.py6def test_linear_layer_can_be_constructed():CODE
LOW⚡candle-pyo3/tests/bindings/test_linear.py11def test_linear_layer_can_forward_a_singular_input():CODE
LOW⚡candle-pyo3/tests/bindings/test_linear.py18def test_linear_layer_can_forward_a_batched_input():CODE
LOW⚡candle-pyo3/tests/bindings/test_linear.py25def test_quantized_linear_layer_can_forward_a_singular_input():CODE
LOW⚡candle-pyo3/tests/bindings/test_linear.py33def test_quantized_linear_layer_can_forward_a_batched_input():CODE
LOWcandle-pyo3/tests/bindings/test_testing.py8def test_assert_equal_asserts_correctly(dtype: candle.DType):CODE
LOWcandle-pyo3/tests/bindings/test_testing.py18def test_assert_almost_equal_asserts_correctly(dtype: candle.DType):CODE
Verbosity Indicators18 hits · 38 pts
SeverityFileLineSnippetContext
LOW⚡candle-transformers/src/models/z_image/transformer.rs766 // Step 1: Squeeze F dimension: (B, C, 1, H, W) -> (B, C, H, W)COMMENT
LOW⚡candle-transformers/src/models/z_image/transformer.rs769 // Step 2: Reshape H into (H_tokens, pH): (B, C, H, W) -> (B, C, H_t, pH, W)COMMENT
LOW⚡candle-transformers/src/models/z_image/transformer.rs772 // Step 3: Reshape W into (W_tokens, pW): (B, C, H_t, pH, W) -> (B, C, H_t, pH, W_t, pW)COMMENT
LOW⚡candle-transformers/src/models/z_image/transformer.rs775 // Step 4: Permute to match Python: (C, H_t, pH, W_t, pW) -> (H_t, W_t, pH, pW, C)COMMENT
LOW⚡candle-transformers/src/models/z_image/transformer.rs780 // Step 5: Reshape to patches: (B, H_t, W_t, pH, pW, C) -> (B, H_t*W_t, pH*pW*C)COMMENT
LOW⚡candle-transformers/src/models/z_image/transformer.rs823 // Step 1: Reshape to (B, H_t, W_t, pH, pW, C)COMMENT
LOW⚡candle-transformers/src/models/z_image/transformer.rs826 // Step 2: Permute to match Python: (H_t, W_t, pH, pW, C) -> (C, H_t, pH, W_t, pW)COMMENT
LOW⚡candle-transformers/src/models/z_image/transformer.rs831 // Step 3: Reshape to combine H and W: (B, C, H_t, pH, W_t, pW) -> (B, C, H, W)COMMENT
LOW⚡candle-transformers/src/models/z_image/transformer.rs834 // Step 4: Add back F dimension: (B, C, H, W) -> (B, C, 1, H, W)COMMENT
LOW⚡candle-transformers/src/models/paddleocr_vl/mod.rs915 // Step 1: Get text embeddingsCOMMENT
LOW⚡candle-transformers/src/models/paddleocr_vl/mod.rs923 // Step 2: Encode imagesCOMMENT
LOWcandle-transformers/src/models/paddleocr_vl/mod.rs934 // Step 3: Merge vision embeddings into text embeddingsCOMMENT
LOWcandle-transformers/src/models/paddleocr_vl/mod.rs956 // Step 4: Compute M-RoPE position IDsCOMMENT
LOWcandle-transformers/src/models/paddleocr_vl/mod.rs979 // Step 5: Forward through text model with exportCOMMENT
LOWcandle-transformers/src/models/paddleocr_vl/mod.rs1014 // Step 0: Prefill with imageCOMMENT
LOW⚡candle-core/tests/pth.py20# Step 1: Create a 3D NumPy array with Fortran order using a range of numbersCOMMENT
LOW⚡candle-core/tests/pth.py28# Step 2: Convert the NumPy array to a PyTorch tensorCOMMENT
LOW⚡candle-core/tests/pth.py34# Step 3: Save the PyTorch tensor to a .pth fileCOMMENT
Structural Annotation Overuse18 hits · 38 pts
SeverityFileLineSnippetContext
LOW⚡candle-transformers/src/models/z_image/transformer.rs766 // Step 1: Squeeze F dimension: (B, C, 1, H, W) -> (B, C, H, W)COMMENT
LOW⚡candle-transformers/src/models/z_image/transformer.rs769 // Step 2: Reshape H into (H_tokens, pH): (B, C, H, W) -> (B, C, H_t, pH, W)COMMENT
LOW⚡candle-transformers/src/models/z_image/transformer.rs772 // Step 3: Reshape W into (W_tokens, pW): (B, C, H_t, pH, W) -> (B, C, H_t, pH, W_t, pW)COMMENT
LOW⚡candle-transformers/src/models/z_image/transformer.rs775 // Step 4: Permute to match Python: (C, H_t, pH, W_t, pW) -> (H_t, W_t, pH, pW, C)COMMENT
LOW⚡candle-transformers/src/models/z_image/transformer.rs780 // Step 5: Reshape to patches: (B, H_t, W_t, pH, pW, C) -> (B, H_t*W_t, pH*pW*C)COMMENT
LOW⚡candle-transformers/src/models/z_image/transformer.rs823 // Step 1: Reshape to (B, H_t, W_t, pH, pW, C)COMMENT
LOW⚡candle-transformers/src/models/z_image/transformer.rs826 // Step 2: Permute to match Python: (H_t, W_t, pH, pW, C) -> (C, H_t, pH, W_t, pW)COMMENT
LOW⚡candle-transformers/src/models/z_image/transformer.rs831 // Step 3: Reshape to combine H and W: (B, C, H_t, pH, W_t, pW) -> (B, C, H, W)COMMENT
LOW⚡candle-transformers/src/models/z_image/transformer.rs834 // Step 4: Add back F dimension: (B, C, H, W) -> (B, C, 1, H, W)COMMENT
LOW⚡candle-transformers/src/models/paddleocr_vl/mod.rs915 // Step 1: Get text embeddingsCOMMENT
LOW⚡candle-transformers/src/models/paddleocr_vl/mod.rs923 // Step 2: Encode imagesCOMMENT
LOWcandle-transformers/src/models/paddleocr_vl/mod.rs934 // Step 3: Merge vision embeddings into text embeddingsCOMMENT
LOWcandle-transformers/src/models/paddleocr_vl/mod.rs956 // Step 4: Compute M-RoPE position IDsCOMMENT
LOWcandle-transformers/src/models/paddleocr_vl/mod.rs979 // Step 5: Forward through text model with exportCOMMENT
LOWcandle-transformers/src/models/paddleocr_vl/mod.rs1014 // Step 0: Prefill with imageCOMMENT
LOW⚡candle-core/tests/pth.py20# Step 1: Create a 3D NumPy array with Fortran order using a range of numbersCOMMENT
LOW⚡candle-core/tests/pth.py28# Step 2: Convert the NumPy array to a PyTorch tensorCOMMENT
LOW⚡candle-core/tests/pth.py34# Step 3: Save the PyTorch tensor to a .pth fileCOMMENT
Unused Imports26 hits · 26 pts
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LOWcandle-wasm-examples/quant-qwen3/serve.py10CODE
LOWcandle-pyo3/e5.py8CODE
LOWcandle-pyo3/py_src/candle/__init__.py4CODE
LOWcandle-pyo3/py_src/candle/__init__.py49CODE
LOWcandle-pyo3/py_src/candle/nn/linear.py1CODE
LOWcandle-pyo3/py_src/candle/nn/__init__.py1CODE
LOWcandle-pyo3/py_src/candle/nn/__init__.py2CODE
LOWcandle-pyo3/py_src/candle/nn/__init__.py2CODE
LOWcandle-pyo3/py_src/candle/nn/__init__.py2CODE
LOWcandle-pyo3/py_src/candle/nn/__init__.py3CODE
LOWcandle-pyo3/py_src/candle/nn/__init__.py4CODE
LOWcandle-pyo3/py_src/candle/nn/__init__.py5CODE
LOWcandle-pyo3/py_src/candle/nn/container.py3CODE
LOWcandle-pyo3/py_src/candle/nn/sparse.py2CODE
LOWcandle-pyo3/py_src/candle/nn/sparse.py2CODE
LOWcandle-pyo3/py_src/candle/nn/sparse.py2CODE
LOWcandle-pyo3/py_src/candle/nn/normalization.py4CODE
LOWcandle-pyo3/py_src/candle/nn/normalization.py4CODE
LOWcandle-pyo3/py_src/candle/models/llama.py3CODE
LOWcandle-pyo3/tests/native/test_utils.py2CODE
LOWcandle-pyo3/tests/native/test_utils.py2CODE
LOWcandle-pyo3/tests/native/test_shape.py1CODE
LOWcandle-pyo3/tests/bindings/test_linear.py2CODE
LOWcandle-examples/examples/flux/t5_tokenizer.py1CODE
LOWcandle-examples/examples/resnet/export_models.py2CODE
LOWcandle-examples/examples/resnet/export_models.py3CODE
Excessive Try-Catch Wrapping7 hits · 13 pts
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MEDIUMcandle-wasm-examples/quant-qwen3/serve.py12 print("Error: Required packages not installed", file=sys.stderr)CODE
LOW⚡candle-wasm-examples/quant-qwen3/serve.py50 except Exception as e:CODE
MEDIUM⚡candle-wasm-examples/quant-qwen3/serve.py51 print(f"Error downloading {filename}: {e}", file=sys.stderr)CODE
MEDIUM⚡candle-wasm-examples/quant-qwen3/serve.py60 print(f"Error: Custom path does not exist: {custom_path}", file=sys.stderr)CODE
LOWcandle-wasm-examples/quant-qwen3/serve.py142 except Exception as e:CODE
MEDIUMcandle-wasm-examples/quant-qwen3/serve.py133def send_file(self, filepath, content_type):CODE
LOWcandle-pyo3/py_src/candle/nn/module.py462 except Exception as ex:STRING
AI Slop Vocabulary4 hits · 12 pts
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MEDIUMcandle-transformers/src/models/rwkv_v7.rs66/// This is more robust than computing from a formula, as differentCOMMENT
MEDIUMcandle-transformers/src/models/rwkv_v5.rs72 // Certainly a weird convention but taken from modeling_rwkv5.pyCOMMENT
MEDIUMcandle-transformers/src/models/segment_anything/mod.rs4//! in an image based on prompts like points or boxes. //! This model provides a robust and fast image segmentation pipeCOMMENT
MEDIUMcandle-nn/src/loss.rs78/// A robust loss function that combines `MAE` and `MSE` losses:COMMENT
Self-Referential Comments3 hits · 10 pts
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MEDIUMcandle-pyo3/tests/native/test_tensor.py134 # Create a decoder attention mask as a test caseCOMMENT
MEDIUM⚡candle-core/tests/pth.py17# Create a tensor with fortran contiguous memory layoutCOMMENT
MEDIUM…ples/examples/reinforcement-learning/atari_wrappers.py303# Create the environment.COMMENT
Deep Nesting8 hits · 8 pts
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LOWcandle-pyo3/stub.py87CODE
LOWcandle-pyo3/py_src/candle/nn/container.py136CODE
LOWcandle-pyo3/py_src/candle/nn/container.py153CODE
LOWcandle-pyo3/py_src/candle/nn/module.py97CODE
LOWcandle-pyo3/py_src/candle/nn/module.py391CODE
LOWcandle-pyo3/py_src/candle/nn/module.py592CODE
LOWcandle-pyo3/py_src/candle/nn/module.py659CODE
LOW…ples/examples/reinforcement-learning/atari_wrappers.py232CODE
Synthetic Comment Markers1 hit · 8 pts
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HIGHcandle-examples/examples/quantized-lfm2/main.rs41 /// GGUF file to load, typically a .gguf file generated by llama.cpp.COMMENT
Modern AI Meta-Vocabulary1 hit · 3 pts
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MEDIUMcandle-examples/examples/stella-en-v5/README.md1# candle-stella-en-v5: Implementation of [stella_en_1.5B_v5](https://huggingface.co/dunzhang/stella_en_1.5B_v5) embeddinCOMMENT
Fake / Example Data2 hits · 2 pts
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LOWcandle-transformers/src/models/voxtral/model.rs901 let dummy_token = Tensor::new(&[1u32], &config.device)?;CODE
LOWcandle-transformers/src/models/voxtral/model.rs902 let dummy_embed = self.language_model.embed(&dummy_token)?;CODE
Modern Structural Boilerplate1 hit · 1 pts
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LOWcandle-pyo3/py_src/candle/nn/container.py19__all__ = ["Sequential", "ModuleList", "ModuleDict"]CODE
Redundant / Tautological Comments1 hit · 0 pts
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LOWcandle-pyo3/stub.py37 # Check if we have a return type annotation in the docstringSTRING