Minimalist ML framework for Rust
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.
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.
Can maintainers explain intent, assumptions, and trade-offs? 415 finding(s).
Are imports declared and package/API names verified? 28 finding(s).
Are wrappers, guards, and repeated structures necessary? 101 finding(s).
Which changes need focused human review? 502 finding(s).
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.
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.
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.
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.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | candle-transformers/src/utils.rs | 1 | //! Shared utilities: repeat_kv, repeat_penalty, causal mask. | COMMENT |
| LOW | candle-transformers/src/utils.rs | 21 | .collect(); | COMMENT |
| LOW | candle-transformers/src/models/starcoder2.rs | 1 | //! StarCoder model implementation with quantization support. | COMMENT |
| LOW | candle-transformers/src/models/quantized_mixformer.rs | 1 | //! Module containing quantized MixFormer model implementation. | COMMENT |
| LOW | candle-transformers/src/models/parler_tts.rs | 1 | //! Parler Model implementation for parler_tts text-to-speech synthesis | COMMENT |
| LOW | candle-transformers/src/models/eva2.rs | 1 | //! EVA-2 inference implementation. | COMMENT |
| LOW | candle-transformers/src/models/quantized_moondream.rs | 1 | //! Implementation of a quantized Moondream vision language model. | COMMENT |
| LOW | candle-transformers/src/models/dinov2.rs | 1 | //! Implementation of the DINOv2 models from Meta Research. | COMMENT |
| LOW | candle-transformers/src/models/dinov2.rs | 21 | //! | COMMENT |
| LOW | candle-transformers/src/models/csm.rs | 1 | //! Implementation of the Conversational Speech Model (CSM) from Sesame | COMMENT |
| LOW | …e-transformers/src/models/quantized_recurrent_gemma.rs | 1 | //! Recurrent Gemma model implementation with quantization support. | COMMENT |
| LOW | candle-transformers/src/models/quantized_phi.rs | 1 | //! Phi2 model implementation with quantization support. | COMMENT |
| LOW | candle-transformers/src/models/mobilenetv4.rs | 1 | //! # MobileNet-v4 | COMMENT |
| LOW | candle-transformers/src/models/rwkv_v7.rs | 1 | //! RWKV v7 "Goose" (x070) model implementation. | COMMENT |
| LOW | candle-transformers/src/models/paligemma.rs | 1 | //! Multimodal multi-purpose model combining Gemma-based language model with SigLIP image understanding | COMMENT |
| LOW | candle-transformers/src/models/t5.rs | 1 | //! T5 model implementation. | COMMENT |
| LOW | candle-transformers/src/models/t5.rs | 21 | //! ```bash | COMMENT |
| LOW | candle-transformers/src/models/t5.rs | 41 | //! --prompt "<2de> How are you, my friend?" \ | COMMENT |
| LOW | candle-transformers/src/models/phi3.rs | 1 | //! Microsoft Phi-3 model implementation | COMMENT |
| LOW | candle-transformers/src/models/bigcode.rs | 1 | //! BigCode implementation in Rust based on the GPT-BigCode model. | COMMENT |
| LOW | candle-transformers/src/models/rwkv_v6.rs | 1 | //! RWKV v6 model implementation. | COMMENT |
| LOW | candle-transformers/src/models/quantized_blip.rs | 1 | //! BLIP model implementation with quantization support. | COMMENT |
| LOW | candle-transformers/src/models/quantized_rwkv_v5.rs | 1 | //! RWKV v5 model implementation with quantization support. | COMMENT |
| LOW | candle-transformers/src/models/vgg.rs | 1 | //! VGG-16 model implementation. | COMMENT |
| LOW | candle-transformers/src/models/recurrent_gemma.rs | 1 | //! Recurrent Gemma model implementation | COMMENT |
| LOW | candle-transformers/src/models/quantized_mpt.rs | 1 | //! Quantized MPT model implementation. | COMMENT |
| LOW | candle-transformers/src/models/stable_lm.rs | 1 | //! StableLM model implementation. | COMMENT |
| LOW | candle-transformers/src/models/quantized_stable_lm.rs | 1 | //! Module for quantized StableLM implementation. | COMMENT |
| LOW | candle-transformers/src/models/mixtral.rs | 1 | //! Mixtral Model, a sparse mixture of expert model based on the Mistral architecture | COMMENT |
| LOW | candle-transformers/src/models/rwkv_v5.rs | 1 | //! RWKV v5 model implementation. | COMMENT |
| LOW | candle-transformers/src/models/rwkv_v5.rs | 21 | //! ```bash | COMMENT |
| LOW | candle-transformers/src/models/olmo.rs | 1 | //! OLMo (Open Language Model) implementation | COMMENT |
| LOW | candle-transformers/src/models/qwen2.rs | 1 | //! Qwen2 model implementation with quantization support. | COMMENT |
| LOW | candle-transformers/src/models/mod.rs | 1 | //! Candle implementations for various deep learning models | COMMENT |
| LOW | candle-transformers/src/models/dinov2reg4.rs | 1 | //! Implementation of the DINOv2 revision (4 regularization) | COMMENT |
| LOW | candle-transformers/src/models/dinov2reg4.rs | 21 | //! | COMMENT |
| LOW | candle-transformers/src/models/quantized_rwkv_v6.rs | 1 | //! RWKV v6 model implementation with quantization support. | COMMENT |
| LOW | candle-transformers/src/models/phi.rs | 1 | //! Microsoft Phi model implementation | COMMENT |
| LOW | candle-transformers/src/models/convnext.rs | 1 | //! ConvNeXt implementation. | COMMENT |
| LOW | candle-transformers/src/models/segformer.rs | 1 | //! Segformer model implementation for semantic segmentation and image classification. | COMMENT |
| LOW | candle-transformers/src/models/quantized_gemma3.rs | 1 | //! Gemma 3 model implementation with quantization support. | COMMENT |
| LOW | candle-transformers/src/models/qwen2_moe.rs | 1 | //! Qwen2 model implementation with Mixture of Experts support. | COMMENT |
| LOW | candle-transformers/src/models/quantized_qwen2.rs | 1 | //! Qwen2 model implementation with quantization support. | COMMENT |
| LOW | candle-transformers/src/models/quantized_t5.rs | 1 | //! T5 model implementation with quantization support. | COMMENT |
| LOW | candle-transformers/src/models/quantized_blip_text.rs | 1 | //! Quantized BLIP text module implementation. | COMMENT |
| LOW | candle-transformers/src/models/quantized_mistral.rs | 1 | //! Mistral model implementation with quantization support. | COMMENT |
| LOW | candle-transformers/src/models/bert.rs | 1 | //! BERT (Bidirectional Encoder Representations from Transformers) | COMMENT |
| LOW | candle-transformers/src/models/efficientvit.rs | 1 | //! EfficientViT (MSRA) inference implementation based on timm. | COMMENT |
| LOW | candle-transformers/src/models/efficientvit.rs | 21 | //! --example efficientvit \ | COMMENT |
| LOW | candle-transformers/src/models/quantized_llama2_c.rs | 1 | //! Quantized Llama2 model implementation. | COMMENT |
| LOW | candle-transformers/src/models/mobileclip.rs | 1 | //! Mobile CLIP model, combining a lightweight vision encoder with a text encoder | COMMENT |
| LOW | candle-transformers/src/models/quantized_llama.rs | 1 | //! Quantized llama model implementation. | COMMENT |
| LOW | candle-transformers/src/models/quantized_llama.rs | 521 | span, | COMMENT |
| LOW | candle-transformers/src/models/quantized_llama.rs | 621 | COMMENT | |
| LOW | candle-transformers/src/models/trocr.rs | 1 | //! TrOCR model implementation. | COMMENT |
| LOW | candle-transformers/src/models/nomic_bert.rs | 1 | //! # NomicBERT | COMMENT |
| LOW | candle-transformers/src/models/mpt.rs | 1 | //! Module implementing the MPT (Multi-Purpose Transformer) model | COMMENT |
| LOW | candle-transformers/src/models/yi.rs | 1 | //! Yi model implementation. | COMMENT |
| LOW | candle-transformers/src/models/quantized_metavoice.rs | 1 | //! Quantized MetaVoice model implementation. | COMMENT |
| LOW | candle-transformers/src/models/stella_en_v5.rs | 1 | //! Stella v5 model implementation. | COMMENT |
| 259 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | candle-transformers/src/models/rwkv_v7.rs | 23 | // ─── Config ────────────────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/rwkv_v7.rs | 77 | // ─── State ─────────────────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/rwkv_v7.rs | 155 | // ─── Tokenizer ─────────────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/rwkv_v7.rs | 159 | // ─── Helpers ───────────────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/rwkv_v7.rs | 191 | // ─── TimeMix (Attention) ───────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/rwkv_v7.rs | 458 | // ─── ChannelMix (FFN) ──────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/rwkv_v7.rs | 549 | // ─── DeaAttention (v7b only) ───────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/rwkv_v7.rs | 724 | // ─── Block ─────────────────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/rwkv_v7.rs | 831 | // ─── Model ─────────────────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/quantized_llama.rs | 604 | // ── Mask shape tests ────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/quantized_llama.rs | 622 | // ── Mask value tests ────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/quantized_llama.rs | 672 | // ── Mask broadcast compatibility test ───────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/config.rs | 5 | // ── Text config defaults ──────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/config.rs | 50 | // ── Rope parameters ───────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/config.rs | 68 | // ── Gemma4TextConfig ──────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/config.rs | 147 | // ── Vision config defaults ────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/config.rs | 189 | // ── Gemma4VisionConfig ────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/config.rs | 236 | // ── Audio config defaults ─────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/config.rs | 302 | // ── Gemma4AudioConfig ─────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/config.rs | 374 | // ── Top-level config defaults ─────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/config.rs | 386 | // ── Gemma4Config ──────────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/vision.rs | 11 | // ── RmsNorm (Gemma-style) ─────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/vision.rs | 52 | // ── 2D Vision Rotary Embedding ────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/vision.rs | 114 | // ── PatchEmbedder ─────────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/vision.rs | 185 | // ── VisionAttention ───────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/vision.rs | 275 | // ── VisionMlp ─────────────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/vision.rs | 310 | // ── VisionEncoderLayer ────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/vision.rs | 368 | // ── VisionPooler ──────────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/vision.rs | 437 | // ── VisionTower (public) ──────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/mod.rs | 95 | // ── Vision embedding injection ────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/mod.rs | 118 | // ── Audio embedding injection ─────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/audio.rs | 11 | // ── RmsNorm (standard, no +1 offset for audio) ───────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/audio.rs | 41 | // ── LayerNorm (for SSCP conv blocks) ──────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/audio.rs | 67 | // ── SSCP Conv Blocks ──────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/audio.rs | 170 | // ── SubSampleConvProjection ───────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/audio.rs | 225 | // ── Relative Position Embedding ───────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/audio.rs | 367 | // ── Conformer Attention ───────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/audio.rs | 645 | // ── Conformer FeedForward ─────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/audio.rs | 699 | // ── Conformer LightConv1d ─────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/audio.rs | 772 | // ── ConformerBlock ────────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/audio.rs | 808 | // ── AudioModel (public) ───────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/text.rs | 12 | // ── RmsNorm (Gemma-style with +1 offset) ──────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/text.rs | 53 | // ── RotaryEmbedding (standard, for sliding layers) ────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/text.rs | 100 | // ── ProportionalRotaryEmbedding (for global/full layers) ──────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/text.rs | 153 | // ── MLP ───────────────────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/text.rs | 192 | // ── Flash attention ───────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/text.rs | 210 | // ── KvCache ───────────────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/text.rs | 218 | // ── Attention ─────────────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/text.rs | 386 | // ── DecoderLayer ──────────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/text.rs | 477 | // ── Causal mask ───────────────────────────────────────────────────────────── | COMMENT |
| MEDIUM | candle-transformers/src/models/gemma4/text.rs | 515 | // ── TextModel ─────────────────────────────────────────────────────────────── | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | candle-wasm-examples/quant-qwen3/serve.py | 85 | def find_or_download_tokenizer(): | CODE |
| LOW | candle-pyo3/stub.py | 127 | def process_additional_function(fn): | CODE |
| LOW⚡ | candle-pyo3/tests/native/test_tensor.py | 8 | def test_tensor_can_be_constructed(): | CODE |
| LOW⚡ | candle-pyo3/tests/native/test_tensor.py | 13 | def test_tensor_can_be_constructed_from_list(): | CODE |
| LOW⚡ | candle-pyo3/tests/native/test_tensor.py | 18 | def test_tensor_can_be_constructed_from_list_of_lists(): | CODE |
| LOW⚡ | candle-pyo3/tests/native/test_tensor.py | 23 | def test_tensor_can_be_quantized(): | CODE |
| LOW | candle-pyo3/tests/native/test_tensor.py | 43 | def test_tensor_can_be_indexed(): | CODE |
| LOW | candle-pyo3/tests/native/test_tensor.py | 51 | def test_tensor_can_be_sliced(): | CODE |
| LOW | candle-pyo3/tests/native/test_tensor.py | 87 | def test_tensor_supports_equality_operations_with_scalars(): | CODE |
| LOW | candle-pyo3/tests/native/test_tensor.py | 109 | def test_tensor_supports_equality_operations_with_tensors(): | CODE |
| LOW | candle-pyo3/tests/native/test_tensor.py | 133 | def test_tensor_equality_operations_can_broadcast(): | CODE |
| LOW | candle-pyo3/tests/native/test_tensor.py | 145 | def test_tensor_can_be_hashed(): | CODE |
| LOW | candle-pyo3/tests/native/test_tensor.py | 153 | def test_tensor_can_be_expanded_with_none(): | CODE |
| LOW | candle-pyo3/tests/native/test_tensor.py | 168 | def test_tensor_can_be_index_via_tensor(): | CODE |
| LOW | candle-pyo3/tests/native/test_tensor.py | 179 | def test_tensor_can_be_index_via_list(): | CODE |
| LOW | candle-pyo3/tests/native/test_tensor.py | 190 | def test_tensor_can_be_cast_via_to(): | CODE |
| LOW | candle-pyo3/tests/native/test_tensor.py | 212 | def test_tensor_can_be_moved_via_to(): | CODE |
| LOW | candle-pyo3/tests/native/test_tensor.py | 234 | def test_tensor_can_be_moved_and_cast_via_to(): | CODE |
| LOW | candle-pyo3/tests/native/test_tensor.py | 272 | def test_tensor_can_be_subtracted(): | CODE |
| LOW | candle-pyo3/tests/native/test_tensor.py | 290 | def test_tensor_can_be_multiplied(): | CODE |
| LOW | candle-pyo3/tests/native/test_tensor.py | 308 | def test_tensor_can_be_divided(): | CODE |
| LOW | candle-pyo3/tests/native/test_utils.py | 10 | def test_can_roundtrip_safetensors(): | CODE |
| LOW | candle-pyo3/tests/native/test_shape.py | 6 | def test_absolute_shapes_are_valid(): | CODE |
| LOW | candle-pyo3/tests/native/test_shape.py | 17 | def test_relative_shapes_are_valid(): | CODE |
| LOW | candle-pyo3/tests/bindings/test_module.py | 9 | def test_module_can_be_constructed(): | CODE |
| LOW | candle-pyo3/tests/bindings/test_module.py | 18 | def test_module_registers_tensors(): | CODE |
| LOW | candle-pyo3/tests/bindings/test_module.py | 30 | def test_module_registers_submodules(): | CODE |
| LOW | candle-pyo3/tests/bindings/test_module.py | 45 | def test_module_can_dump_statedict(): | CODE |
| LOW | candle-pyo3/tests/bindings/test_module.py | 61 | def test_module_can_load_statedict(): | CODE |
| LOW | candle-pyo3/tests/bindings/test_module.py | 77 | def test_module_throws_on_shape_mismatch(): | CODE |
| LOW | candle-pyo3/tests/bindings/test_module.py | 92 | def test_module_throws_on_missing_key(): | CODE |
| LOW | candle-pyo3/tests/bindings/test_module.py | 108 | def test_module_can_load_quantized_tensors(): | CODE |
| LOW | candle-pyo3/tests/bindings/test_module.py | 124 | def test_module_dequantizes_tensors_automatically(): | CODE |
| LOW | candle-pyo3/tests/bindings/test_module.py | 139 | def test_module_can_be_moved_to_cuda(): | CODE |
| LOW | candle-pyo3/tests/bindings/test_module.py | 151 | def test_module_can_be_moved_from_cuda_to_cpu(): | CODE |
| LOW⚡ | candle-pyo3/tests/bindings/test_linear.py | 6 | def test_linear_layer_can_be_constructed(): | CODE |
| LOW⚡ | candle-pyo3/tests/bindings/test_linear.py | 11 | def test_linear_layer_can_forward_a_singular_input(): | CODE |
| LOW⚡ | candle-pyo3/tests/bindings/test_linear.py | 18 | def test_linear_layer_can_forward_a_batched_input(): | CODE |
| LOW⚡ | candle-pyo3/tests/bindings/test_linear.py | 25 | def test_quantized_linear_layer_can_forward_a_singular_input(): | CODE |
| LOW⚡ | candle-pyo3/tests/bindings/test_linear.py | 33 | def test_quantized_linear_layer_can_forward_a_batched_input(): | CODE |
| LOW | candle-pyo3/tests/bindings/test_testing.py | 8 | def test_assert_equal_asserts_correctly(dtype: candle.DType): | CODE |
| LOW | candle-pyo3/tests/bindings/test_testing.py | 18 | def test_assert_almost_equal_asserts_correctly(dtype: candle.DType): | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW⚡ | candle-transformers/src/models/z_image/transformer.rs | 766 | // Step 1: Squeeze F dimension: (B, C, 1, H, W) -> (B, C, H, W) | COMMENT |
| LOW⚡ | candle-transformers/src/models/z_image/transformer.rs | 769 | // 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.rs | 772 | // 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.rs | 775 | // 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.rs | 780 | // 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.rs | 823 | // Step 1: Reshape to (B, H_t, W_t, pH, pW, C) | COMMENT |
| LOW⚡ | candle-transformers/src/models/z_image/transformer.rs | 826 | // 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.rs | 831 | // 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.rs | 834 | // Step 4: Add back F dimension: (B, C, H, W) -> (B, C, 1, H, W) | COMMENT |
| LOW⚡ | candle-transformers/src/models/paddleocr_vl/mod.rs | 915 | // Step 1: Get text embeddings | COMMENT |
| LOW⚡ | candle-transformers/src/models/paddleocr_vl/mod.rs | 923 | // Step 2: Encode images | COMMENT |
| LOW | candle-transformers/src/models/paddleocr_vl/mod.rs | 934 | // Step 3: Merge vision embeddings into text embeddings | COMMENT |
| LOW | candle-transformers/src/models/paddleocr_vl/mod.rs | 956 | // Step 4: Compute M-RoPE position IDs | COMMENT |
| LOW | candle-transformers/src/models/paddleocr_vl/mod.rs | 979 | // Step 5: Forward through text model with export | COMMENT |
| LOW | candle-transformers/src/models/paddleocr_vl/mod.rs | 1014 | // Step 0: Prefill with image | COMMENT |
| LOW⚡ | candle-core/tests/pth.py | 20 | # Step 1: Create a 3D NumPy array with Fortran order using a range of numbers | COMMENT |
| LOW⚡ | candle-core/tests/pth.py | 28 | # Step 2: Convert the NumPy array to a PyTorch tensor | COMMENT |
| LOW⚡ | candle-core/tests/pth.py | 34 | # Step 3: Save the PyTorch tensor to a .pth file | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW⚡ | candle-transformers/src/models/z_image/transformer.rs | 766 | // Step 1: Squeeze F dimension: (B, C, 1, H, W) -> (B, C, H, W) | COMMENT |
| LOW⚡ | candle-transformers/src/models/z_image/transformer.rs | 769 | // 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.rs | 772 | // 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.rs | 775 | // 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.rs | 780 | // 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.rs | 823 | // Step 1: Reshape to (B, H_t, W_t, pH, pW, C) | COMMENT |
| LOW⚡ | candle-transformers/src/models/z_image/transformer.rs | 826 | // 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.rs | 831 | // 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.rs | 834 | // Step 4: Add back F dimension: (B, C, H, W) -> (B, C, 1, H, W) | COMMENT |
| LOW⚡ | candle-transformers/src/models/paddleocr_vl/mod.rs | 915 | // Step 1: Get text embeddings | COMMENT |
| LOW⚡ | candle-transformers/src/models/paddleocr_vl/mod.rs | 923 | // Step 2: Encode images | COMMENT |
| LOW | candle-transformers/src/models/paddleocr_vl/mod.rs | 934 | // Step 3: Merge vision embeddings into text embeddings | COMMENT |
| LOW | candle-transformers/src/models/paddleocr_vl/mod.rs | 956 | // Step 4: Compute M-RoPE position IDs | COMMENT |
| LOW | candle-transformers/src/models/paddleocr_vl/mod.rs | 979 | // Step 5: Forward through text model with export | COMMENT |
| LOW | candle-transformers/src/models/paddleocr_vl/mod.rs | 1014 | // Step 0: Prefill with image | COMMENT |
| LOW⚡ | candle-core/tests/pth.py | 20 | # Step 1: Create a 3D NumPy array with Fortran order using a range of numbers | COMMENT |
| LOW⚡ | candle-core/tests/pth.py | 28 | # Step 2: Convert the NumPy array to a PyTorch tensor | COMMENT |
| LOW⚡ | candle-core/tests/pth.py | 34 | # Step 3: Save the PyTorch tensor to a .pth file | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | candle-wasm-examples/quant-qwen3/serve.py | 10 | CODE | |
| LOW | candle-pyo3/e5.py | 8 | CODE | |
| LOW | candle-pyo3/py_src/candle/__init__.py | 4 | CODE | |
| LOW | candle-pyo3/py_src/candle/__init__.py | 49 | CODE | |
| LOW | candle-pyo3/py_src/candle/nn/linear.py | 1 | CODE | |
| LOW | candle-pyo3/py_src/candle/nn/__init__.py | 1 | CODE | |
| LOW | candle-pyo3/py_src/candle/nn/__init__.py | 2 | CODE | |
| LOW | candle-pyo3/py_src/candle/nn/__init__.py | 2 | CODE | |
| LOW | candle-pyo3/py_src/candle/nn/__init__.py | 2 | CODE | |
| LOW | candle-pyo3/py_src/candle/nn/__init__.py | 3 | CODE | |
| LOW | candle-pyo3/py_src/candle/nn/__init__.py | 4 | CODE | |
| LOW | candle-pyo3/py_src/candle/nn/__init__.py | 5 | CODE | |
| LOW | candle-pyo3/py_src/candle/nn/container.py | 3 | CODE | |
| LOW | candle-pyo3/py_src/candle/nn/sparse.py | 2 | CODE | |
| LOW | candle-pyo3/py_src/candle/nn/sparse.py | 2 | CODE | |
| LOW | candle-pyo3/py_src/candle/nn/sparse.py | 2 | CODE | |
| LOW | candle-pyo3/py_src/candle/nn/normalization.py | 4 | CODE | |
| LOW | candle-pyo3/py_src/candle/nn/normalization.py | 4 | CODE | |
| LOW | candle-pyo3/py_src/candle/models/llama.py | 3 | CODE | |
| LOW | candle-pyo3/tests/native/test_utils.py | 2 | CODE | |
| LOW | candle-pyo3/tests/native/test_utils.py | 2 | CODE | |
| LOW | candle-pyo3/tests/native/test_shape.py | 1 | CODE | |
| LOW | candle-pyo3/tests/bindings/test_linear.py | 2 | CODE | |
| LOW | candle-examples/examples/flux/t5_tokenizer.py | 1 | CODE | |
| LOW | candle-examples/examples/resnet/export_models.py | 2 | CODE | |
| LOW | candle-examples/examples/resnet/export_models.py | 3 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | candle-wasm-examples/quant-qwen3/serve.py | 12 | print("Error: Required packages not installed", file=sys.stderr) | CODE |
| LOW⚡ | candle-wasm-examples/quant-qwen3/serve.py | 50 | except Exception as e: | CODE |
| MEDIUM⚡ | candle-wasm-examples/quant-qwen3/serve.py | 51 | print(f"Error downloading {filename}: {e}", file=sys.stderr) | CODE |
| MEDIUM⚡ | candle-wasm-examples/quant-qwen3/serve.py | 60 | print(f"Error: Custom path does not exist: {custom_path}", file=sys.stderr) | CODE |
| LOW | candle-wasm-examples/quant-qwen3/serve.py | 142 | except Exception as e: | CODE |
| MEDIUM | candle-wasm-examples/quant-qwen3/serve.py | 133 | def send_file(self, filepath, content_type): | CODE |
| LOW | candle-pyo3/py_src/candle/nn/module.py | 462 | except Exception as ex: | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | candle-transformers/src/models/rwkv_v7.rs | 66 | /// This is more robust than computing from a formula, as different | COMMENT |
| MEDIUM | candle-transformers/src/models/rwkv_v5.rs | 72 | // Certainly a weird convention but taken from modeling_rwkv5.py | COMMENT |
| MEDIUM | candle-transformers/src/models/segment_anything/mod.rs | 4 | //! in an image based on prompts like points or boxes. //! This model provides a robust and fast image segmentation pipe | COMMENT |
| MEDIUM | candle-nn/src/loss.rs | 78 | /// A robust loss function that combines `MAE` and `MSE` losses: | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | candle-pyo3/tests/native/test_tensor.py | 134 | # Create a decoder attention mask as a test case | COMMENT |
| MEDIUM⚡ | candle-core/tests/pth.py | 17 | # Create a tensor with fortran contiguous memory layout | COMMENT |
| MEDIUM | …ples/examples/reinforcement-learning/atari_wrappers.py | 303 | # Create the environment. | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | candle-pyo3/stub.py | 87 | CODE | |
| LOW | candle-pyo3/py_src/candle/nn/container.py | 136 | CODE | |
| LOW | candle-pyo3/py_src/candle/nn/container.py | 153 | CODE | |
| LOW | candle-pyo3/py_src/candle/nn/module.py | 97 | CODE | |
| LOW | candle-pyo3/py_src/candle/nn/module.py | 391 | CODE | |
| LOW | candle-pyo3/py_src/candle/nn/module.py | 592 | CODE | |
| LOW | candle-pyo3/py_src/candle/nn/module.py | 659 | CODE | |
| LOW | …ples/examples/reinforcement-learning/atari_wrappers.py | 232 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | candle-examples/examples/quantized-lfm2/main.rs | 41 | /// GGUF file to load, typically a .gguf file generated by llama.cpp. | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | candle-examples/examples/stella-en-v5/README.md | 1 | # candle-stella-en-v5: Implementation of [stella_en_1.5B_v5](https://huggingface.co/dunzhang/stella_en_1.5B_v5) embeddin | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | candle-transformers/src/models/voxtral/model.rs | 901 | let dummy_token = Tensor::new(&[1u32], &config.device)?; | CODE |
| LOW | candle-transformers/src/models/voxtral/model.rs | 902 | let dummy_embed = self.language_model.embed(&dummy_token)?; | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | candle-pyo3/py_src/candle/nn/container.py | 19 | __all__ = ["Sequential", "ModuleList", "ModuleDict"] | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | candle-pyo3/stub.py | 37 | # Check if we have a return type annotation in the docstring | STRING |