Retrieval and Retrieval-augmented LLMs
This report summarizes scanner evidence for FlagOpen/FlagEmbedding, a Python project with 12,230 GitHub stars. SynthScan examined 143,769 lines of code across 737 source files, recording 1892 pattern matches distributed across 20 syntactic categories. The legacy adjusted pattern signal is 23.6 (Elevated 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.
Can maintainers explain intent, assumptions, and trade-offs? 642 finding(s).
Are imports declared and package/API names verified? 745 finding(s).
Are wrappers, guards, and repeated structures necessary? 780 finding(s).
Which changes need focused human review? 1892 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 1892 distinct pattern matches across 20 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 |
|---|---|---|---|---|
| HIGH | …h/llm_reranker/merge/configuration_minicpm_reranker.py | 0 | this is the configuration class to store the configuration of a [`llamamodel`]. it is used to instantiate an llama model | STRING |
| HIGH | …netune_for_layerwise/configuration_minicpm_reranker.py | 0 | this is the configuration class to store the configuration of a [`llamamodel`]. it is used to instantiate an llama model | STRING |
| HIGH | …coder_only/layerwise/configuration_minicpm_reranker.py | 0 | this is the configuration class to store the configuration of a [`llamamodel`]. it is used to instantiate an llama model | STRING |
| HIGH | …/decoder_only/models/configuration_minicpm_reranker.py | 0 | this is the configuration class to store the configuration of a [`llamamodel`]. it is used to instantiate an llama model | STRING |
| HIGH | …LLM/activation_beacon/src/llama/configuration_llama.py | 0 | this is the configuration class to store the configuration of a [`llamamodel`]. it is used to instantiate an llama model | STRING |
| HIGH | …search/llm_reranker/merge/modeling_minicpm_reranker.py | 0 | minicpmrotaryembedding extended with linear scaling. credits to the reddit user /u/kaiokendev | STRING |
| HIGH | …er/finetune_for_layerwise/modeling_minicpm_reranker.py | 0 | minicpmrotaryembedding extended with linear scaling. credits to the reddit user /u/kaiokendev | STRING |
| HIGH | …er/decoder_only/layerwise/modeling_minicpm_reranker.py | 0 | minicpmrotaryembedding extended with linear scaling. credits to the reddit user /u/kaiokendev | STRING |
| HIGH | …anker/decoder_only/models/modeling_minicpm_reranker.py | 0 | minicpmrotaryembedding extended with linear scaling. credits to the reddit user /u/kaiokendev | STRING |
| HIGH | …search/llm_reranker/merge/modeling_minicpm_reranker.py | 0 | rotaryembedding extended with dynamic ntk scaling. credits to the reddit users /u/bloc97 and /u/emozilla | STRING |
| HIGH | …er/finetune_for_layerwise/modeling_minicpm_reranker.py | 0 | rotaryembedding extended with dynamic ntk scaling. credits to the reddit users /u/bloc97 and /u/emozilla | STRING |
| HIGH | …er/decoder_only/layerwise/modeling_minicpm_reranker.py | 0 | rotaryembedding extended with dynamic ntk scaling. credits to the reddit users /u/bloc97 and /u/emozilla | STRING |
| HIGH | …anker/decoder_only/models/modeling_minicpm_reranker.py | 0 | rotaryembedding extended with dynamic ntk scaling. credits to the reddit users /u/bloc97 and /u/emozilla | STRING |
| HIGH | …earch/Long_LLM/activation_beacon/src/modeling_utils.py | 0 | rotaryembedding extended with dynamic ntk scaling. credits to the reddit users /u/bloc97 and /u/emozilla | STRING |
| HIGH | …search/llm_reranker/merge/modeling_minicpm_reranker.py | 0 | applies rotary position embedding to the query and key tensors. args: q (`torch.tensor`): the query tensor. k (`torch.te | STRING |
| HIGH | …er/finetune_for_layerwise/modeling_minicpm_reranker.py | 0 | applies rotary position embedding to the query and key tensors. args: q (`torch.tensor`): the query tensor. k (`torch.te | STRING |
| HIGH | …er/decoder_only/layerwise/modeling_minicpm_reranker.py | 0 | applies rotary position embedding to the query and key tensors. args: q (`torch.tensor`): the query tensor. k (`torch.te | STRING |
| HIGH | …anker/decoder_only/models/modeling_minicpm_reranker.py | 0 | applies rotary position embedding to the query and key tensors. args: q (`torch.tensor`): the query tensor. k (`torch.te | STRING |
| HIGH | …search/llm_reranker/merge/modeling_minicpm_reranker.py | 0 | this is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). the hidden states go from (batch, num_key_va | STRING |
| HIGH | …er/finetune_for_layerwise/modeling_minicpm_reranker.py | 0 | this is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). the hidden states go from (batch, num_key_va | STRING |
| HIGH | …Long_LLM/activation_beacon/src/qwen2/modeling_qwen2.py | 0 | this is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). the hidden states go from (batch, num_key_va | STRING |
| HIGH | …_LLM/activation_beacon/src/mistral/modeling_mistral.py | 0 | this is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). the hidden states go from (batch, num_key_va | STRING |
| HIGH | …Long_LLM/activation_beacon/src/llama/modeling_llama.py | 0 | this is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). the hidden states go from (batch, num_key_va | STRING |
| HIGH | …er/decoder_only/layerwise/modeling_minicpm_reranker.py | 0 | this is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). the hidden states go from (batch, num_key_va | STRING |
| HIGH | …anker/decoder_only/models/modeling_minicpm_reranker.py | 0 | this is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). the hidden states go from (batch, num_key_va | STRING |
| HIGH | …search/llm_reranker/merge/modeling_minicpm_reranker.py | 0 | multi-headed attention from 'attention is all you need' paper | STRING |
| HIGH | …er/finetune_for_layerwise/modeling_minicpm_reranker.py | 0 | multi-headed attention from 'attention is all you need' paper | STRING |
| HIGH | …Long_LLM/activation_beacon/src/qwen2/modeling_qwen2.py | 0 | multi-headed attention from 'attention is all you need' paper | STRING |
| HIGH | …_LLM/activation_beacon/src/mistral/modeling_mistral.py | 0 | multi-headed attention from 'attention is all you need' paper | STRING |
| HIGH | …Long_LLM/activation_beacon/src/llama/modeling_llama.py | 0 | multi-headed attention from 'attention is all you need' paper | STRING |
| HIGH | research/BGE_VL/modeling_MMRet_CLIP.py | 0 | multi-headed attention from 'attention is all you need' paper | STRING |
| HIGH | …er/decoder_only/layerwise/modeling_minicpm_reranker.py | 0 | multi-headed attention from 'attention is all you need' paper | STRING |
| HIGH | …anker/decoder_only/models/modeling_minicpm_reranker.py | 0 | multi-headed attention from 'attention is all you need' paper | STRING |
| HIGH | …search/llm_reranker/merge/modeling_minicpm_reranker.py | 0 | clipattention flash attention module. this module inherits from `clipattention` as the weights of the module stays untou | STRING |
| HIGH | …er/finetune_for_layerwise/modeling_minicpm_reranker.py | 0 | clipattention flash attention module. this module inherits from `clipattention` as the weights of the module stays untou | STRING |
| HIGH | …er/decoder_only/layerwise/modeling_minicpm_reranker.py | 0 | clipattention flash attention module. this module inherits from `clipattention` as the weights of the module stays untou | STRING |
| HIGH | …anker/decoder_only/models/modeling_minicpm_reranker.py | 0 | clipattention flash attention module. this module inherits from `clipattention` as the weights of the module stays untou | STRING |
| HIGH | …Long_LLM/activation_beacon/src/qwen2/modeling_qwen2.py | 0 | clipattention flash attention module. this module inherits from `clipattention` as the weights of the module stays untou | STRING |
| HIGH | …_LLM/activation_beacon/src/mistral/modeling_mistral.py | 0 | clipattention flash attention module. this module inherits from `clipattention` as the weights of the module stays untou | STRING |
| HIGH | …Long_LLM/activation_beacon/src/llama/modeling_llama.py | 0 | clipattention flash attention module. this module inherits from `clipattention` as the weights of the module stays untou | STRING |
| HIGH | research/BGE_VL/modeling_MMRet_CLIP.py | 0 | clipattention flash attention module. this module inherits from `clipattention` as the weights of the module stays untou | STRING |
| HIGH | …search/llm_reranker/merge/modeling_minicpm_reranker.py | 0 | calls the forward method of flash attention - if the input hidden states contain at least one padding token first unpad | STRING |
| HIGH | …er/finetune_for_layerwise/modeling_minicpm_reranker.py | 0 | calls the forward method of flash attention - if the input hidden states contain at least one padding token first unpad | STRING |
| HIGH | …er/decoder_only/layerwise/modeling_minicpm_reranker.py | 0 | calls the forward method of flash attention - if the input hidden states contain at least one padding token first unpad | STRING |
| HIGH | …anker/decoder_only/models/modeling_minicpm_reranker.py | 0 | calls the forward method of flash attention - if the input hidden states contain at least one padding token first unpad | STRING |
| HIGH | …Long_LLM/activation_beacon/src/qwen2/modeling_qwen2.py | 0 | calls the forward method of flash attention - if the input hidden states contain at least one padding token first unpad | STRING |
| HIGH | …_LLM/activation_beacon/src/mistral/modeling_mistral.py | 0 | calls the forward method of flash attention - if the input hidden states contain at least one padding token first unpad | STRING |
| HIGH | …Long_LLM/activation_beacon/src/llama/modeling_llama.py | 0 | calls the forward method of flash attention - if the input hidden states contain at least one padding token first unpad | STRING |
| HIGH | …search/llm_reranker/merge/modeling_minicpm_reranker.py | 0 | sdpa attention module using torch.nn.functional.scaled_dot_product_attention. this module inherits from `clipattention` | STRING |
| HIGH | …er/finetune_for_layerwise/modeling_minicpm_reranker.py | 0 | sdpa attention module using torch.nn.functional.scaled_dot_product_attention. this module inherits from `clipattention` | STRING |
| HIGH | …er/decoder_only/layerwise/modeling_minicpm_reranker.py | 0 | sdpa attention module using torch.nn.functional.scaled_dot_product_attention. this module inherits from `clipattention` | STRING |
| HIGH | …anker/decoder_only/models/modeling_minicpm_reranker.py | 0 | sdpa attention module using torch.nn.functional.scaled_dot_product_attention. this module inherits from `clipattention` | STRING |
| HIGH | …Long_LLM/activation_beacon/src/qwen2/modeling_qwen2.py | 0 | sdpa attention module using torch.nn.functional.scaled_dot_product_attention. this module inherits from `clipattention` | STRING |
| HIGH | …_LLM/activation_beacon/src/mistral/modeling_mistral.py | 0 | sdpa attention module using torch.nn.functional.scaled_dot_product_attention. this module inherits from `clipattention` | STRING |
| HIGH | …Long_LLM/activation_beacon/src/llama/modeling_llama.py | 0 | sdpa attention module using torch.nn.functional.scaled_dot_product_attention. this module inherits from `clipattention` | STRING |
| HIGH | research/BGE_VL/modeling_MMRet_CLIP.py | 0 | sdpa attention module using torch.nn.functional.scaled_dot_product_attention. this module inherits from `clipattention` | STRING |
| HIGH | …search/llm_reranker/merge/modeling_minicpm_reranker.py | 0 | args: hidden_states (`torch.floattensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`to | STRING |
| HIGH | …er/finetune_for_layerwise/modeling_minicpm_reranker.py | 0 | args: hidden_states (`torch.floattensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`to | STRING |
| HIGH | …er/decoder_only/layerwise/modeling_minicpm_reranker.py | 0 | args: hidden_states (`torch.floattensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`to | STRING |
| HIGH | …anker/decoder_only/models/modeling_minicpm_reranker.py | 0 | args: hidden_states (`torch.floattensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`to | STRING |
| 230 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …h/llm_reranker/merge/configuration_minicpm_reranker.py | 183 | CODE | |
| LOW | research/llm_reranker/merge/__init__.py | 1 | CODE | |
| LOW | research/llm_reranker/merge/__init__.py | 2 | CODE | |
| LOW | research/llm_reranker/merge/__init__.py | 3 | CODE | |
| LOW | …search/llm_reranker/merge/modeling_minicpm_reranker.py | 21 | CODE | |
| LOW | …search/llm_reranker/merge/modeling_minicpm_reranker.py | 31 | CODE | |
| LOW | …search/llm_reranker/merge/modeling_minicpm_reranker.py | 31 | CODE | |
| LOW | …search/llm_reranker/merge/modeling_minicpm_reranker.py | 41 | CODE | |
| LOW | …search/llm_reranker/merge/modeling_minicpm_reranker.py | 45 | CODE | |
| LOW | …earch/llm_reranker/finetune_for_instruction/trainer.py | 1 | CODE | |
| LOW | …netune_for_layerwise/configuration_minicpm_reranker.py | 184 | CODE | |
| LOW | research/llm_reranker/finetune_for_layerwise/trainer.py | 1 | CODE | |
| LOW | …er/finetune_for_layerwise/modeling_minicpm_reranker.py | 21 | CODE | |
| LOW | …er/finetune_for_layerwise/modeling_minicpm_reranker.py | 31 | CODE | |
| LOW | …er/finetune_for_layerwise/modeling_minicpm_reranker.py | 31 | CODE | |
| LOW | …er/finetune_for_layerwise/modeling_minicpm_reranker.py | 41 | CODE | |
| LOW | …er/finetune_for_layerwise/modeling_minicpm_reranker.py | 45 | CODE | |
| LOW | research/llm_reranker/finetune_for_layerwise/data.py | 1 | CODE | |
| LOW | research/BGE_Coder/evaluation/coir_eval/arguments.py | 4 | CODE | |
| LOW | …ch/BGE_Coder/evaluation/coderag_eval/test/arguments.py | 1 | CODE | |
| LOW | …ch/BGE_Coder/evaluation/coderag_eval/test/arguments.py | 4 | CODE | |
| LOW | research/BGE_Coder/evaluation/coderag_eval/test/main.py | 6 | CODE | |
| LOW | research/BGE_Coder/evaluation/coderag_eval/test/main.py | 7 | CODE | |
| LOW | research/BGE_Coder/evaluation/coderag_eval/test/main.py | 10 | CODE | |
| LOW | research/BGE_Coder/evaluation/coderag_eval/test/main.py | 11 | CODE | |
| LOW | research/BGE_Coder/evaluation/coderag_eval/test/main.py | 19 | CODE | |
| LOW | …evaluation/coderag_eval/test/create/live_code_bench.py | 4 | CODE | |
| LOW | …evaluation/coderag_eval/test/create/code_search_net.py | 5 | CODE | |
| LOW | …evaluation/coderag_eval/test/create/code_search_net.py | 6 | CODE | |
| LOW | …/BGE_Coder/evaluation/coderag_eval/test/create/odex.py | 2 | CODE | |
| LOW | …/BGE_Coder/evaluation/coderag_eval/test/create/odex.py | 3 | CODE | |
| LOW | …/BGE_Coder/evaluation/coderag_eval/test/create/odex.py | 7 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/runner.py | 8 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/runner.py | 8 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/runner.py | 11 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/arguments.py | 4 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/dataset.py | 1 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/dataset.py | 5 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/dataset.py | 6 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/dataset.py | 7 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/dataset.py | 9 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/dataset.py | 10 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/dataset.py | 10 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/dataset.py | 10 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/dataset.py | 10 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/dataset.py | 18 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/dataset.py | 19 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/modeling.py | 6 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/modeling.py | 8 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/modeling.py | 11 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/modeling.py | 12 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/modeling.py | 12 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/modeling.py | 13 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/modeling.py | 18 | CODE | |
| LOW | …rch/Reinforced_IR/finetune/generator/save_tokenizer.py | 2 | CODE | |
| LOW | …rch/Reinforced_IR/finetune/generator/save_tokenizer.py | 3 | CODE | |
| LOW | …rch/Reinforced_IR/finetune/generator/save_tokenizer.py | 4 | CODE | |
| LOW | research/Reinforced_IR/inference/ir_model.py | 1 | CODE | |
| LOW | research/Reinforced_IR/inference/ir_model.py | 4 | CODE | |
| LOW | research/Reinforced_IR/inference/ir_model.py | 4 | CODE | |
| 682 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | research/llm_reranker/evaluate.py | 45 | CODE | |
| LOW | …reranker/merge/merge_layerwise_model_from_raw_model.py | 6 | CODE | |
| LOW | …search/llm_reranker/merge/modeling_minicpm_reranker.py | 1210 | CODE | |
| LOW | …er/merge/merge_layerwise_model_from_finetuned_model.py | 5 | CODE | |
| LOW | research/llm_reranker/merge/merge_base_model.py | 5 | CODE | |
| LOW | research/llm_reranker/finetune_for_instruction/data.py | 149 | CODE | |
| LOW | …arch/llm_reranker/finetune_for_layerwise/load_model.py | 8 | CODE | |
| LOW | …er/finetune_for_layerwise/modeling_minicpm_reranker.py | 1211 | CODE | |
| LOW | research/llm_reranker/finetune_for_layerwise/data.py | 144 | CODE | |
| LOW | research/BGE_Coder/evaluation/coderag_eval/test/main.py | 111 | CODE | |
| LOW | …_Coder/evaluation/coderag_eval/test/create/swebench.py | 128 | CODE | |
| LOW | …_Coder/evaluation/coderag_eval/test/create/swebench.py | 144 | CODE | |
| LOW | …r/evaluation/coderag_eval/test/create/repoeval_repo.py | 210 | CODE | |
| LOW | …r/evaluation/coderag_eval/test/create/repoeval_repo.py | 116 | CODE | |
| LOW | …_Coder/evaluation/coderag_eval/test/create/repoeval.py | 221 | CODE | |
| LOW | …_Coder/evaluation/coderag_eval/test/create/repoeval.py | 116 | CODE | |
| LOW | …r/evaluation/coderag_eval/test/create/swebench_repo.py | 128 | CODE | |
| LOW | …r/evaluation/coderag_eval/test/create/swebench_repo.py | 144 | CODE | |
| LOW | research/BGE_Coder/data_generation/corpus_generator.py | 18 | CODE | |
| LOW | research/BGE_Coder/data_generation/triplet_generator.py | 341 | CODE | |
| LOW | research/BGE_Coder/data_generation/triplet_generator.py | 485 | CODE | |
| LOW | …BGE_Coder/data_generation/format_generated_examples.py | 56 | CODE | |
| LOW | research/BGE_Coder/data_generation/constant.py | 423 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/dataset.py | 99 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/dataset.py | 277 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/dataset.py | 414 | CODE | |
| LOW | …inforced_IR/data_generation/generate_generator_data.py | 47 | CODE | |
| LOW | research/Reinforced_IR/data_generation/utils.py | 22 | CODE | |
| LOW | research/Reinforced_IR/data_generation/utils.py | 65 | CODE | |
| LOW | research/Reinforced_IR/data_generation/utils.py | 302 | CODE | |
| LOW | …nforced_IR/data_generation/generate_universal_query.py | 33 | CODE | |
| LOW | …inforced_IR/data_generation/generate_retriever_data.py | 46 | CODE | |
| LOW | …arch/Long_LLM/activation_beacon/main/eval_longbench.py | 96 | CODE | |
| LOW | …arch/Long_LLM/activation_beacon/main/eval_multiturn.py | 50 | CODE | |
| LOW | …earch/Long_LLM/activation_beacon/main/eval_infbench.py | 97 | CODE | |
| LOW | …arch/Long_LLM/activation_beacon/main/infbench_utils.py | 406 | CODE | |
| LOW | …arch/Long_LLM/activation_beacon/main/infbench_utils.py | 516 | CODE | |
| LOW | …earch/Long_LLM/activation_beacon/main/pretrain_data.py | 54 | CODE | |
| LOW | research/Long_LLM/activation_beacon/main/eval_needle.py | 212 | CODE | |
| LOW | research/Long_LLM/activation_beacon/main/eval_mmlu.py | 64 | CODE | |
| LOW | research/Long_LLM/activation_beacon/main/eval_mmlu.py | 98 | CODE | |
| LOW | research/Long_LLM/activation_beacon/main/eval_topic.py | 93 | CODE | |
| LOW | research/Long_LLM/activation_beacon/src/__init__.py | 15 | CODE | |
| LOW | research/Long_LLM/activation_beacon/src/chat.py | 31 | CODE | |
| LOW | research/Long_LLM/activation_beacon/src/chat.py | 280 | CODE | |
| LOW | research/Long_LLM/activation_beacon/src/chat.py | 529 | CODE | |
| LOW | research/Long_LLM/activation_beacon/src/utils.py | 38 | CODE | |
| LOW | research/Long_LLM/activation_beacon/src/utils.py | 220 | CODE | |
| LOW | …earch/Long_LLM/activation_beacon/src/modeling_utils.py | 32 | CODE | |
| LOW | …earch/Long_LLM/activation_beacon/src/modeling_utils.py | 249 | CODE | |
| LOW | research/Long_LLM/activation_beacon/src/trainer.py | 147 | CODE | |
| LOW | …arch/Long_LLM/activation_beacon/src/modeling_beacon.py | 930 | CODE | |
| LOW | …arch/Long_LLM/activation_beacon/src/modeling_beacon.py | 200 | CODE | |
| LOW | …arch/Long_LLM/activation_beacon/src/modeling_beacon.py | 273 | CODE | |
| LOW | …arch/Long_LLM/activation_beacon/src/modeling_beacon.py | 394 | CODE | |
| LOW | …arch/Long_LLM/activation_beacon/src/modeling_beacon.py | 757 | CODE | |
| LOW | research/Long_LLM/activation_beacon/src/data.py | 28 | CODE | |
| LOW | research/Long_LLM/activation_beacon/src/data.py | 102 | CODE | |
| LOW | …Long_LLM/activation_beacon/src/qwen2/modeling_qwen2.py | 187 | CODE | |
| LOW | …Long_LLM/activation_beacon/src/qwen2/modeling_qwen2.py | 933 | CODE | |
| 150 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | research/BGE_Coder/data_generation/constant.py | 430 | Given a task, return the generation prompt for the task. Args: - task: Task: the task object - tex | STRING |
| HIGH | research/Reinforced_IR/finetune/retriever/modeling.py | 160 | Compute the distillation loss. Args: kd_loss_type (str): Type of knowledge distillation loss, suppo | STRING |
| HIGH | FlagEmbedding/abc/finetune/reranker/AbsDataset.py | 57 | Load dataset from path. Args: file_path (str): Path to load the datasets from. Raises: | STRING |
| HIGH | FlagEmbedding/abc/finetune/embedder/AbsModeling.py | 305 | Compute the distillation loss. Args: kd_loss_type (str): Type of knowledge distillation loss, suppo | STRING |
| HIGH | FlagEmbedding/abc/finetune/embedder/AbsDataset.py | 55 | Load dataset from path. Args: file_path (str): Path to load the datasets from. Raises: | STRING |
| HIGH | FlagEmbedding/abc/inference/AbsReranker.py | 100 | Args: devices (Union[str, int, List[str], List[int]]): Specified devices, can be `str`, `int`, lis | STRING |
| HIGH | FlagEmbedding/abc/inference/AbsEmbedder.py | 111 | Args: devices (Union[str, int, List[str], List[int]]): specified devices, can be `str`, `int`, lis | STRING |
| HIGH | FlagEmbedding/abc/inference/AbsEmbedder.py | 438 | concatenate and return the results from all the processes Args: results_list (List[Union[torch.Tens | STRING |
| HIGH | FlagEmbedding/abc/evaluation/searcher.py | 51 | Abstract method to be overrode. This is called during the retrieval process. Parameters: | STRING |
| HIGH | FlagEmbedding/abc/evaluation/searcher.py | 83 | This is called during the retrieval process. Parameters: corpus: Dict[str, Dict[st | STRING |
| HIGH | FlagEmbedding/abc/evaluation/searcher.py | 191 | This is called during the reranking process. Parameters: corpus: Dict[str, Dict[st | STRING |
| HIGH | FlagEmbedding/abc/evaluation/data_loader.py | 56 | Check the validity of dataset names Args: dataset_names (Union[str, List[str]]): a dataset name (st | STRING |
| HIGH | FlagEmbedding/abc/evaluation/data_loader.py | 116 | Load the qrels from the dataset. Args: dataset_name (Optional[str], optional): Name of the dataset. | STRING |
| HIGH | FlagEmbedding/abc/evaluation/data_loader.py | 144 | Load the queries from the dataset. Args: dataset_name (Optional[str], optional): Name of the datase | STRING |
| HIGH | FlagEmbedding/abc/evaluation/data_loader.py | 176 | Abstract method to load corpus from remote dataset, to be overrode in child class. Args: dataset_na | STRING |
| HIGH | FlagEmbedding/abc/evaluation/data_loader.py | 196 | Abstract method to load relevance from remote dataset, to be overrode in child class. Args: dataset | STRING |
| HIGH | FlagEmbedding/abc/evaluation/data_loader.py | 217 | Abstract method to load queries from remote dataset, to be overrode in child class. Args: dataset_n | STRING |
| HIGH | FlagEmbedding/abc/evaluation/data_loader.py | 256 | Load relevance from local dataset. Args: save_dir (str): Path to save the loaded relevance. | STRING |
| HIGH | FlagEmbedding/abc/evaluation/data_loader.py | 291 | Load queries from local dataset. Args: save_dir (str): Path to save the loaded queries. | STRING |
| HIGH | FlagEmbedding/abc/evaluation/data_loader.py | 320 | Download file from provided URL. Args: download_url (str): Source URL of the file. save | STRING |
| HIGH | FlagEmbedding/abc/evaluation/data_loader.py | 370 | Download and unzip the gzip file from provided URL. Args: download_url (str): Source URL of the gzi | STRING |
| HIGH | FlagEmbedding/abc/evaluation/data_loader.py | 396 | Download and unzip the zip file from provided URL. Args: download_url (str): Source URL of the zip | STRING |
| HIGH | …mbedding/finetune/embedder/encoder_only/m3/modeling.py | 82 | Use the pooling method to get the dense embedding. Args: last_hidden_state (torch.Tensor): The mode | STRING |
| HIGH | …mbedding/finetune/embedder/encoder_only/m3/modeling.py | 240 | Compute the ensemble score of the three methods. Args: q_reps (torch.Tensor): Query representations | STRING |
| HIGH | …edding/finetune/embedder/encoder_only/base/modeling.py | 109 | Use the pooling method to get the sentence embedding. Args: last_hidden_state (torch.Tensor): The m | STRING |
| HIGH | …bedding/finetune/embedder/decoder_only/icl/modeling.py | 110 | Use the pooling method to get the sentence embedding. Args: last_hidden_state (torch.Tensor): The m | STRING |
| HIGH | …edding/finetune/embedder/decoder_only/base/modeling.py | 110 | Use the pooling method to get the sentence embedding. Args: last_hidden_state (torch.Tensor): The m | STRING |
| HIGH | FlagEmbedding/inference/auto_embedder.py | 38 | Load a finetuned model according to the provided vars. Args: model_name_or_path (str): If | STRING |
| HIGH | FlagEmbedding/inference/auto_reranker.py | 32 | Load a finetuned model according to the provided vars. Args: model_name_or_path (str): If | STRING |
| HIGH | FlagEmbedding/inference/embedder/encoder_only/base.py | 292 | The pooling function. Args: last_hidden_state (torch.Tensor): The last hidden state of the model. | STRING |
| HIGH | FlagEmbedding/evaluation/bright/searcher.py | 27 | This is called during the retrieval process. Parameters: corpus: Dict[str, Dict[str, Any]] | STRING |
| HIGH | FlagEmbedding/evaluation/beir/data_loader.py | 311 | Load the qrels from the dataset. Args: dataset_name (Optional[str], optional): Name of the dataset. | STRING |
| HIGH | FlagEmbedding/evaluation/beir/data_loader.py | 340 | Load the queries from the dataset. Args: dataset_name (Optional[str], optional): Name of the datase | STRING |
| HIGH | FlagEmbedding/evaluation/beir/data_loader.py | 398 | Load relevance from local dataset. Args: save_dir (str): Path to save the loaded relevance. | STRING |
| HIGH | FlagEmbedding/evaluation/beir/data_loader.py | 439 | Load queries from local dataset. Args: save_dir (str): Path to save the loaded queries. | STRING |
| HIGH | FlagEmbedding/evaluation/mkqa/data_loader.py | 57 | Try to load qrels from local datasets. Args: save_dir (str): Directory that save the data files. | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …search/llm_reranker/merge/modeling_minicpm_reranker.py | 1407 | def prepare_inputs_for_generation( | CODE |
| LOW | …er/merge/merge_layerwise_model_from_finetuned_model.py | 5 | def merge_layerwise_finetuned_llm(model_name_or_path, lora_name_or_path, save_path, cache_dir: str = None, token: str = | CODE |
| LOW | …arch/llm_reranker/finetune_for_instruction/modeling.py | 38 | def gradient_checkpointing_enable(self, **kwargs): | CODE |
| LOW | …arch/llm_reranker/finetune_for_instruction/modeling.py | 41 | def enable_input_require_grads(self, **kwargs): | CODE |
| LOW | …er/finetune_for_layerwise/modeling_minicpm_reranker.py | 1408 | def prepare_inputs_for_generation( | CODE |
| LOW | …search/llm_reranker/finetune_for_layerwise/modeling.py | 40 | def gradient_checkpointing_enable(self, **kwargs): | CODE |
| LOW | …search/llm_reranker/finetune_for_layerwise/modeling.py | 43 | def enable_input_require_grads(self, **kwargs): | CODE |
| LOW | research/reranker/modeling.py | 30 | def gradient_checkpointing_enable(self, **kwargs): | CODE |
| LOW | research/BGE_Coder/evaluation/coir_eval/prompts.py | 4 | def get_task_def_by_task_name(task_name: str) -> str: | CODE |
| LOW | …arch/BGE_Coder/evaluation/coderag_eval/test/prompts.py | 4 | def get_task_def_by_task_name(task_name: str) -> str: | CODE |
| LOW | …_Coder/evaluation/coderag_eval/test/create/swebench.py | 128 | def ingest_directory_contents(root_dir, include_tests=False): | CODE |
| LOW | …r/evaluation/coderag_eval/test/create/repoeval_repo.py | 102 | def _merge_windows_with_same_context(self, code_windows): | CODE |
| LOW | …_Coder/evaluation/coderag_eval/test/create/repoeval.py | 102 | def _merge_windows_with_same_context(self, code_windows): | CODE |
| LOW | …r/evaluation/coderag_eval/test/create/swebench_repo.py | 128 | def ingest_directory_contents(root_dir, include_tests=False): | CODE |
| LOW | research/BGE_Coder/data_generation/triplet_generator.py | 34 | def _gen_for_code_modification_retrieval( | CODE |
| LOW | research/BGE_Coder/data_generation/triplet_generator.py | 81 | def _gen_for_code_comparison_retrieval( | CODE |
| LOW | research/BGE_Coder/data_generation/triplet_generator.py | 126 | def _gen_for_code_context_retrieval( | CODE |
| LOW | research/BGE_Coder/data_generation/triplet_generator.py | 208 | def _gen_for_bug_desc_retrieval( | CODE |
| LOW | research/BGE_Coder/data_generation/triplet_generator.py | 253 | def _gen_for_two_step_not_use_last( | CODE |
| LOW | research/BGE_Coder/data_generation/triplet_generator.py | 297 | def _gen_for_two_step_use_last( | CODE |
| LOW | …BGE_Coder/data_generation/format_generated_examples.py | 7 | def format_generated_examples( | CODE |
| LOW | research/BGE_Coder/data_generation/constant.py | 65 | def get_task_def_by_task_type(task_type: Union[str, TaskType]) -> Tuple[str, TaskType, str]: | CODE |
| LOW | research/BGE_Coder/data_generation/constant.py | 391 | def get_pos_as_input_by_task_type(task_type: TaskType) -> bool: | CODE |
| LOW | research/BGE_Coder/data_generation/constant.py | 759 | def get_quality_control_prompt( | STRING |
| LOW | research/Reinforced_IR/data_generation/utils.py | 229 | def generate_llm_dpo_train_data( | CODE |
| LOW | …/Reinforced_IR/data_generation/prompts/hyde_prompts.py | 42 | def get_additional_info_generation_prompt(dataset_name: str, query: str) -> str: | CODE |
| LOW | …h/Reinforced_IR/data_generation/prompts/get_prompts.py | 324 | def get_query_generation_prompt(dataset_name: str, passage: str, use_examples: bool) -> str: | CODE |
| LOW | …h/Reinforced_IR/data_generation/prompts/get_prompts.py | 465 | def get_additional_info_generation_prompt(dataset_name: str, query: str) -> str: | CODE |
| LOW | …h/Reinforced_IR/data_generation/prompts/get_prompts.py | 488 | def get_additional_info_generation_long_prompt(dataset_name: str, query: str) -> str: | CODE |
| LOW | …h/Reinforced_IR/data_generation/prompts/get_prompts.py | 511 | def get_additional_info_generation_long_air_prompt(dataset_name: str, query: str) -> str: | CODE |
| LOW | …h/Reinforced_IR/data_generation/prompts/get_prompts.py | 535 | def get_additional_info_generation_train_prompt(dataset_name: str, query: str, reference: str) -> str: | CODE |
| LOW | …h/Reinforced_IR/data_generation/prompts/get_prompts.py | 670 | def get_quality_control_prompt(dataset_name: str, query: str, passage: str) -> str: | CODE |
| LOW | …arch/Long_LLM/activation_beacon/main/infbench_utils.py | 134 | def get_score_one_kv_retrieval(pred, label, model_name: str) -> bool: | CODE |
| LOW | …arch/Long_LLM/activation_beacon/main/infbench_utils.py | 147 | def get_score_one_number_string(pred, label, model_name: str) -> bool: | CODE |
| LOW | …arch/Long_LLM/activation_beacon/main/infbench_utils.py | 230 | def get_score_one_longdialogue_qa_eng(pred, label, model_name: str) -> bool: | CODE |
| LOW | …arch/Long_LLM/activation_beacon/main/infbench_utils.py | 239 | def get_score_one_longbook_choice_eng(pred, label, model_name: str) -> bool: | CODE |
| LOW | …arch/Long_LLM/activation_beacon/main/infbench_utils.py | 280 | def get_score_one_longbook_qa_eng(pred, label, model_name: str) -> float: | CODE |
| LOW | …arch/Long_LLM/activation_beacon/main/infbench_utils.py | 284 | def get_score_one_longbook_sum_eng( | CODE |
| LOW | …arch/Long_LLM/activation_beacon/main/infbench_utils.py | 297 | def get_score_one_longbook_qa_chn(pred, label, model_name: str) -> float: | CODE |
| LOW | research/Long_LLM/activation_beacon/src/chat.py | 625 | def extract_text_from_messages(self): | CODE |
| LOW | research/Long_LLM/activation_beacon/src/utils.py | 175 | def get_max_length_in_nested_lists(lst): | CODE |
| LOW | research/Long_LLM/activation_beacon/src/utils.py | 213 | def are_elements_of_same_length(lst: List): | CODE |
| LOW | …arch/Long_LLM/activation_beacon/src/modeling_beacon.py | 890 | def _extract_beacon_and_raw_memory( | CODE |
| LOW | research/Long_LLM/activation_beacon/src/data.py | 28 | def _process_language_modeling(data, indices, tokenizer, min_length, max_length): | CODE |
| LOW | research/Long_LLM/activation_beacon/src/data.py | 54 | def _process_instruction_tuning(data, indices, tokenizer, chat_template, min_length, max_length, eval_mode=False): | CODE |
| LOW | …Long_LLM/activation_beacon/src/qwen2/modeling_qwen2.py | 1293 | def prepare_inputs_for_generation( | CODE |
| LOW | …_LLM/activation_beacon/src/mistral/modeling_mistral.py | 1279 | def prepare_inputs_for_generation( | CODE |
| LOW | …Long_LLM/activation_beacon/src/llama/modeling_llama.py | 1346 | def prepare_inputs_for_generation( | CODE |
| LOW | …search/Long_LLM/longllm_qlora/data_pipeline/_openai.py | 110 | async def process_api_requests_from_file( | CODE |
| LOW | …search/Long_LLM/longllm_qlora/data_pipeline/_openai.py | 394 | def num_tokens_consumed_from_request( | CODE |
| LOW | …search/Long_LLM/longllm_qlora/data_pipeline/_openai.py | 453 | def task_id_generator_function(): | CODE |
| LOW | research/Long_LLM/longllm_qlora/main/infbench_utils.py | 134 | def get_score_one_kv_retrieval(pred, label, model_name: str) -> bool: | CODE |
| LOW | research/Long_LLM/longllm_qlora/main/infbench_utils.py | 147 | def get_score_one_number_string(pred, label, model_name: str) -> bool: | CODE |
| LOW | research/Long_LLM/longllm_qlora/main/infbench_utils.py | 230 | def get_score_one_longdialogue_qa_eng(pred, label, model_name: str) -> bool: | CODE |
| LOW | research/Long_LLM/longllm_qlora/main/infbench_utils.py | 239 | def get_score_one_longbook_choice_eng(pred, label, model_name: str) -> bool: | CODE |
| LOW | research/Long_LLM/longllm_qlora/main/infbench_utils.py | 280 | def get_score_one_longbook_qa_eng(pred, label, model_name: str) -> float: | CODE |
| LOW | research/Long_LLM/longllm_qlora/main/infbench_utils.py | 284 | def get_score_one_longbook_sum_eng( | CODE |
| LOW | research/Long_LLM/longllm_qlora/main/infbench_utils.py | 295 | def get_score_one_longbook_qa_chn(pred, label, model_name: str) -> float: | CODE |
| LOW | research/Long_LLM/longllm_qlora/src/chat.py | 592 | def extract_text_from_messages(self): | CODE |
| LOW | research/Long_LLM/longllm_qlora/src/utils.py | 175 | def get_max_length_in_nested_lists(lst): | CODE |
| 114 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | research/llm_reranker/finetune_for_instruction/run.py | 18 | logger = logging.getLogger(__name__) | CODE |
| LOW | …arch/llm_reranker/finetune_for_instruction/modeling.py | 10 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_reranker/finetune_for_layerwise/run.py | 18 | logger = logging.getLogger(__name__) | CODE |
| LOW | …search/llm_reranker/finetune_for_layerwise/modeling.py | 10 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/reranker/run.py | 16 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/reranker/trainer.py | 10 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/reranker/modeling.py | 10 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/Reinforced_IR/finetune/retriever/runner.py | 16 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/Reinforced_IR/finetune/retriever/dataset.py | 21 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/Reinforced_IR/finetune/retriever/trainer.py | 8 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/Reinforced_IR/finetune/retriever/modeling.py | 21 | logger = logging.getLogger(__name__) | CODE |
| LOW | …arch/Reinforced_IR/data_generation/prompts/__init__.py | 30 | __all__ = [ | CODE |
| LOW | research/Long_LLM/activation_beacon/main/train.py | 17 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/Long_LLM/activation_beacon/src/utils.py | 20 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/Long_LLM/longllm_qlora/main/train.py | 18 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/Long_LLM/longllm_qlora/src/utils.py | 20 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_embedder/run_ranker.py | 15 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_embedder/run_dense.py | 16 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_embedder/run_lm_score.py | 16 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_embedder/evaluation/eval_icl.py | 25 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_embedder/evaluation/icl_utils.py | 12 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_embedder/evaluation/eval_tool.py | 11 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_embedder/evaluation/eval_retrieval.py | 19 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_embedder/evaluation/eval_qa.py | 24 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_embedder/evaluation/eval_popqa.py | 23 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_embedder/evaluation/eval_mmlu.py | 24 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_embedder/evaluation/eval_lrlm.py | 15 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_embedder/evaluation/eval_msc.py | 15 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_embedder/evaluation/eval_qrecc.py | 25 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_embedder/src/lm/modeling_lm.py | 8 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_embedder/src/lm/modeling_srlm.py | 14 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_embedder/src/utils/util.py | 21 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_embedder/src/utils/llama_patch.py | 16 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_embedder/src/retrieval/metrics.py | 10 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_embedder/src/retrieval/modeling_unified.py | 8 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_embedder/src/retrieval/trainer.py | 16 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/old-examples/pretrain/retromae_pretrain/run.py | 24 | logger = logging.getLogger(__name__) | CODE |
| LOW | …rch/old-examples/pretrain/retromae_pretrain/trainer.py | 8 | logger = logging.getLogger(__name__) | CODE |
| LOW | …ch/old-examples/pretrain/retromae_pretrain/modeling.py | 12 | logger = logging.getLogger(__name__) | CODE |
| LOW | …earch/Matroyshka_reranker/finetune/compensation/run.py | 18 | logger = logging.getLogger(__name__) | CODE |
| LOW | …/Matroyshka_reranker/finetune/compensation/modeling.py | 15 | logger = logging.getLogger(__name__) | CODE |
| LOW | …/Matroyshka_reranker/finetune/self_distillation/run.py | 18 | logger = logging.getLogger(__name__) | CODE |
| LOW | …oyshka_reranker/finetune/self_distillation/modeling.py | 11 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_dense_retriever/finetune/run.py | 19 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/llm_dense_retriever/finetune/modeling.py | 15 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/LLARA/finetune/run.py | 18 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/LLARA/finetune/modeling.py | 13 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/LLARA/pretrain/run.py | 18 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/baai_general_embedding/finetune/run.py | 17 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/baai_general_embedding/finetune/modeling.py | 11 | logger = logging.getLogger(__name__) | CODE |
| LOW | …search/baai_general_embedding/finetune/eval_msmarco.py | 12 | logger = logging.getLogger(__name__) | CODE |
| LOW | …search/baai_general_embedding/retromae_pretrain/run.py | 24 | logger = logging.getLogger(__name__) | CODE |
| LOW | …ch/baai_general_embedding/retromae_pretrain/trainer.py | 8 | logger = logging.getLogger(__name__) | CODE |
| LOW | …h/baai_general_embedding/retromae_pretrain/modeling.py | 12 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/visual_bge/visual_bge/modeling.py | 15 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/visual_bge/visual_bge/eva_clip/openai.py | 15 | __all__ = ["list_openai_models", "load_openai_model"] | CODE |
| LOW | research/BGE_VL/eval/eval_fashioniq.py | 19 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/BGE_VL/eval/eval_Circo.py | 14 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/BGE_M3/run.py | 25 | logger = logging.getLogger(__name__) | CODE |
| LOW | research/BGE_M3/modeling.py | 14 | logger = logging.getLogger(__name__) | CODE |
| 83 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
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| LOW | …h/llm_reranker/merge/configuration_minicpm_reranker.py | 115 | CODE | |
| LOW | …search/llm_reranker/merge/modeling_minicpm_reranker.py | 990 | CODE | |
| LOW | …search/llm_reranker/merge/modeling_minicpm_reranker.py | 1210 | CODE | |
| LOW | …search/llm_reranker/merge/modeling_minicpm_reranker.py | 1473 | CODE | |
| LOW | …netune_for_layerwise/configuration_minicpm_reranker.py | 116 | CODE | |
| LOW | …er/finetune_for_layerwise/modeling_minicpm_reranker.py | 991 | CODE | |
| LOW | …er/finetune_for_layerwise/modeling_minicpm_reranker.py | 1211 | CODE | |
| LOW | …er/finetune_for_layerwise/modeling_minicpm_reranker.py | 1474 | CODE | |
| LOW | research/BGE_Coder/data_generation/triplet_generator.py | 143 | CODE | |
| LOW | research/BGE_Coder/data_generation/triplet_generator.py | 552 | CODE | |
| LOW | research/BGE_Coder/data_generation/triplet_generator.py | 605 | CODE | |
| LOW | research/BGE_Coder/data_generation/llm.py | 47 | CODE | |
| LOW | research/Reinforced_IR/finetune/retriever/modeling.py | 40 | CODE | |
| LOW | research/Reinforced_IR/inference/ir_model.py | 24 | CODE | |
| LOW | research/Reinforced_IR/inference/agent/vllm_instruct.py | 23 | CODE | |
| LOW | research/Reinforced_IR/inference/agent/vllm_instruct.py | 61 | CODE | |
| LOW | research/Reinforced_IR/data_generation/utils.py | 65 | CODE | |
| LOW | research/Reinforced_IR/data_generation/utils.py | 229 | CODE | |
| LOW | …h/Reinforced_IR/data_generation/agent/vllm_instruct.py | 23 | CODE | |
| LOW | …h/Reinforced_IR/data_generation/agent/vllm_instruct.py | 61 | CODE | |
| LOW | research/Long_LLM/activation_beacon/src/data.py | 102 | CODE | |
| LOW | research/Long_LLM/activation_beacon/src/data.py | 174 | CODE | |
| LOW | …LLM/activation_beacon/src/qwen2/configuration_qwen2.py | 98 | CODE | |
| LOW | …Long_LLM/activation_beacon/src/qwen2/modeling_qwen2.py | 971 | CODE | |
| LOW | …Long_LLM/activation_beacon/src/qwen2/modeling_qwen2.py | 1144 | CODE | |
| LOW | …Long_LLM/activation_beacon/src/qwen2/modeling_qwen2.py | 1206 | CODE | |
| LOW | …activation_beacon/src/mistral/configuration_mistral.py | 104 | CODE | |
| LOW | …_LLM/activation_beacon/src/mistral/modeling_mistral.py | 961 | CODE | |
| LOW | …_LLM/activation_beacon/src/mistral/modeling_mistral.py | 1134 | CODE | |
| LOW | …_LLM/activation_beacon/src/mistral/modeling_mistral.py | 1196 | CODE | |
| LOW | …LLM/activation_beacon/src/llama/configuration_llama.py | 117 | CODE | |
| LOW | …Long_LLM/activation_beacon/src/llama/modeling_llama.py | 1028 | CODE | |
| LOW | …Long_LLM/activation_beacon/src/llama/modeling_llama.py | 1201 | CODE | |
| LOW | …Long_LLM/activation_beacon/src/llama/modeling_llama.py | 1263 | CODE | |
| LOW | research/Long_LLM/longllm_qlora/src/data.py | 88 | CODE | |
| LOW | research/Long_LLM/longllm_qlora/src/data.py | 157 | CODE | |
| LOW | research/llm_embedder/run_lm_score.py | 56 | CODE | |
| LOW | research/llm_embedder/src/lm/modeling_srlm.py | 25 | CODE | |
| LOW | research/llm_embedder/src/retrieval/modeling_bm25.py | 109 | CODE | |
| LOW | research/llm_embedder/src/retrieval/modeling_dense.py | 21 | CODE | |
| LOW | research/llm_embedder/src/retrieval/modeling_dense.py | 345 | CODE | |
| LOW | research/llm_embedder/src/retrieval/data.py | 122 | CODE | |
| LOW | research/llm_embedder/src/retrieval/data.py | 187 | CODE | |
| LOW | research/C_MTEB/MLDR/mteb_dense_eval/flag_dres_model.py | 40 | CODE | |
| LOW | …oyshka_reranker/finetune/compensation/mistral_model.py | 228 | CODE | |
| LOW | …oyshka_reranker/finetune/compensation/mistral_model.py | 517 | CODE | |
| LOW | …a_reranker/finetune/self_distillation/mistral_model.py | 228 | CODE | |
| LOW | …a_reranker/finetune/self_distillation/mistral_model.py | 517 | CODE | |
| LOW | research/Matroyshka_reranker/inference/rank_model.py | 49 | CODE | |
| LOW | research/Matroyshka_reranker/inference/rank_model.py | 151 | CODE | |
| LOW | research/Matroyshka_reranker/inference/mistral_model.py | 228 | CODE | |
| LOW | research/Matroyshka_reranker/inference/mistral_model.py | 517 | CODE | |
| LOW | research/llm_dense_retriever/finetune/modeling.py | 116 | CODE | |
| LOW | research/LLARA/pretrain/modeling.py | 23 | CODE | |
| LOW | research/LLARA/pretrain/modeling.py | 266 | CODE | |
| LOW | …arch/visual_bge/visual_bge/eva_clip/modified_resnet.py | 55 | CODE | |
| LOW | research/visual_bge/visual_bge/eva_clip/factory.py | 211 | CODE | |
| LOW | research/visual_bge/visual_bge/eva_clip/factory.py | 362 | CODE | |
| LOW | research/visual_bge/visual_bge/eva_clip/factory.py | 416 | CODE | |
| LOW | research/visual_bge/visual_bge/eva_clip/factory.py | 471 | CODE | |
| 41 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | …h/llm_reranker/merge/configuration_minicpm_reranker.py | 185 | except: | CODE |
| HIGH | …search/llm_reranker/merge/modeling_minicpm_reranker.py | 60 | except: | CODE |
| HIGH | …netune_for_layerwise/configuration_minicpm_reranker.py | 186 | except: | CODE |
| HIGH | …er/finetune_for_layerwise/modeling_minicpm_reranker.py | 61 | except: | CODE |
| HIGH | research/Reinforced_IR/data_generation/utils.py | 103 | except: | CODE |
| HIGH | research/Long_LLM/activation_beacon/main/eval_needle.py | 357 | except ValueError: | CODE |
| HIGH | research/Long_LLM/activation_beacon/main/eval_needle.py | 348 | except ValueError: | CODE |
| HIGH | research/Long_LLM/longllm_qlora/main/eval_needle.py | 361 | except ValueError: | CODE |
| HIGH | FlagEmbedding/abc/inference/AbsReranker.py | 17 | except Exception: | CODE |
| HIGH | FlagEmbedding/abc/inference/AbsReranker.py | 93 | except: | CODE |
| HIGH | FlagEmbedding/abc/inference/AbsEmbedder.py | 18 | except Exception: | CODE |
| HIGH | FlagEmbedding/abc/inference/AbsEmbedder.py | 104 | except: | CODE |
| HIGH | FlagEmbedding/utils/transformers_compat.py | 27 | except Exception: | CODE |
| HIGH | …coder_only/layerwise/configuration_minicpm_reranker.py | 186 | except: | CODE |
| HIGH | …er/decoder_only/layerwise/modeling_minicpm_reranker.py | 61 | except: | CODE |
| HIGH | …/decoder_only/models/configuration_minicpm_reranker.py | 186 | except: | CODE |
| HIGH | …anker/decoder_only/models/modeling_minicpm_reranker.py | 61 | except: | CODE |
| HIGH | FlagEmbedding/inference/embedder/decoder_only/icl.py | 192 | except: | CODE |
| HIGH | FlagEmbedding/evaluation/mteb/searcher.py | 50 | except: | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM⚡ | research/llm_embedder/data/toy/lrlm.json | 7 | {"query_id": 11209, "query": "Because the meta element provides additional information about your web page, you have to | CODE |
| LOW⚡ | research/llm_embedder/data/toy/lrlm.json | 7 | {"query_id": 11209, "query": "Because the meta element provides additional information about your web page, you have to | CODE |
| LOW⚡ | research/llm_embedder/data/toy/tool.json | 3 | {"query": "I need to ensure that the URL 'https://friendwebsite.com' is safe. Please check if it is a known phishing att | CODE |
| MEDIUM⚡ | research/llm_embedder/data/toy/tool.json | 3 | {"query": "I need to ensure that the URL 'https://friendwebsite.com' is safe. Please check if it is a known phishing att | CODE |
| LOW⚡ | research/llm_embedder/data/toy/tool.json | 5 | {"query": "I want to explore Instagram profiles related to a specific user with the ID 18527. Can you fetch the related | CODE |
| MEDIUM | …rence/reranker/encoder_only/auto_base_single_device.py | 12 | devices=["cuda:3"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …/inference/reranker/encoder_only/base_single_device.py | 12 | devices=["cuda:3"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …/inference/reranker/encoder_only/base_multi_devices.py | 12 | devices=["cuda:3", "cuda:4"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …rence/reranker/encoder_only/auto_base_multi_devices.py | 12 | devices=["cuda:3", "cuda:4"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …eranker/decoder_only/auto_lightweight_multi_devices.py | 12 | devices=["cuda:3", "cuda:4"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …eranker/decoder_only/auto_lightweight_single_device.py | 12 | devices=["cuda:3"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …rence/reranker/decoder_only/auto_base_single_device.py | 11 | devices=["cuda:3"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …/inference/reranker/decoder_only/base_single_device.py | 11 | devices=["cuda:3"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …nce/reranker/decoder_only/lightweight_multi_devices.py | 12 | devices=["cuda:3", "cuda:4"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …nce/reranker/decoder_only/lightweight_single_device.py | 12 | devices=["cuda:3"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …/inference/reranker/decoder_only/base_multi_devices.py | 11 | devices=["cuda:3", "cuda:4"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …rence/reranker/decoder_only/auto_base_multi_devices.py | 11 | devices=["cuda:3", "cuda:4"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …rence/reranker/decoder_only/layerwise_multi_devices.py | 12 | devices=["cuda:3"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …rence/reranker/decoder_only/layerwise_single_device.py | 12 | devices=["cuda:3"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …/reranker/decoder_only/auto_layerwise_multi_devices.py | 12 | devices=["cuda:3", "cuda:4"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …/reranker/decoder_only/auto_layerwise_single_device.py | 12 | devices=["cuda:3"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …rence/embedder/encoder_only/auto_base_single_device.py | 9 | devices="cuda:0", # if you don't have a GPU, you can use "cpu" | CODE |
| MEDIUM | …/inference/embedder/encoder_only/base_single_device.py | 10 | devices="cuda:0", # if you don't have a GPU, you can use "cpu" | CODE |
| MEDIUM | …/inference/embedder/encoder_only/base_multi_devices.py | 10 | devices=["cuda:0", "cuda:1"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …rence/embedder/encoder_only/auto_base_multi_devices.py | 9 | devices=["cuda:0", "cuda:1"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …ference/embedder/encoder_only/auto_m3_multi_devices.py | 8 | devices=["cuda:0", "cuda:1"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …mbedder/encoder_only/m3_single_device_compute_score.py | 8 | devices="cuda:0", # if you don't have a GPU, you can use "cpu" | CODE |
| MEDIUM | …ference/embedder/encoder_only/auto_m3_single_device.py | 8 | devices="cuda:0", # if you don't have a GPU, you can use "cpu" | CODE |
| MEDIUM | …mbedder/encoder_only/m3_multi_devices_compute_score.py | 8 | devices=["cuda:0", "cuda:1"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …es/inference/embedder/encoder_only/m3_single_device.py | 8 | devices="cuda:0", # if you don't have a GPU, you can use "cpu" | CODE |
| MEDIUM | …es/inference/embedder/encoder_only/m3_multi_devices.py | 8 | devices=["cuda:0", "cuda:1"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …erence/embedder/decoder_only/auto_icl_multi_devices.py | 23 | devices=["cuda:0", "cuda:1"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …erence/embedder/decoder_only/auto_icl_single_device.py | 23 | devices="cuda:0", # if you don't have a GPU, you can use "cpu" | CODE |
| MEDIUM | …rence/embedder/decoder_only/auto_base_single_device.py | 9 | devices="cuda:0", # if you don't have a GPU, you can use "cpu" | CODE |
| MEDIUM | …/inference/embedder/decoder_only/base_single_device.py | 10 | devices="cuda:0", # if you don't have a GPU, you can use "cpu" | CODE |
| MEDIUM | …ence/embedder/decoder_only/pseudo_moe_single_device.py | 16 | devices="cuda:0", # if you don't have a GPU, you can use "cpu" | CODE |
| MEDIUM | …/inference/embedder/decoder_only/base_multi_devices.py | 10 | devices=["cuda:0", "cuda:1"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …ence/embedder/decoder_only/pseudo_moe_multi_devices.py | 16 | devices=["cuda:0", "cuda:1"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …rence/embedder/decoder_only/auto_base_multi_devices.py | 9 | devices=["cuda:0", "cuda:1"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …s/inference/embedder/decoder_only/icl_single_device.py | 24 | devices="cuda:0", # if you don't have a GPU, you can use "cpu" | CODE |
| MEDIUM | …embedder/decoder_only/auto_pseudo_moe_multi_devices.py | 17 | devices=["cuda:0", "cuda:1"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …s/inference/embedder/decoder_only/icl_multi_devices.py | 24 | devices=["cuda:0", "cuda:1"], # if you don't have GPUs, you can use ["cpu", "cpu"] | CODE |
| MEDIUM | …embedder/decoder_only/auto_pseudo_moe_single_device.py | 17 | devices="cuda:0", # if you don't have a GPU, you can use "cpu" | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …h/llm_reranker/merge/configuration_minicpm_reranker.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …search/llm_reranker/merge/modeling_minicpm_reranker.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …netune_for_layerwise/configuration_minicpm_reranker.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …er/finetune_for_layerwise/modeling_minicpm_reranker.py | 1 | # coding=utf-8 | COMMENT |
| LOW | research/BGE_Coder/data_generation/run_generation.py | 281 | # if os.path.exists(save_path) and not overwrite: | COMMENT |
| LOW | research/BGE_Coder/data_generation/constant.py | 241 | # cs = 'Czech' # 捷克语 | COMMENT |
| LOW | research/Reinforced_IR/finetune/retriever/trainer.py | 41 | # if self.is_world_process_zero(): | COMMENT |
| LOW | …Reinforced_IR/data_generation/prompts/train_prompts.py | 21 | # The query is a user query and should be short. | COMMENT |
| LOW | research/Long_LLM/activation_beacon/src/metrics.py | 101 | # if labels is None: | COMMENT |
| LOW | …arch/Long_LLM/activation_beacon/src/modeling_beacon.py | 641 | self._end_idx = end_idx | COMMENT |
| LOW | …LLM/activation_beacon/src/qwen2/configuration_qwen2.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …Long_LLM/activation_beacon/src/qwen2/modeling_qwen2.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …activation_beacon/src/mistral/configuration_mistral.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …_LLM/activation_beacon/src/mistral/modeling_mistral.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …LLM/activation_beacon/src/llama/configuration_llama.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …Long_LLM/activation_beacon/src/llama/modeling_llama.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …Long_LLM/activation_beacon/src/llama/modeling_llama.py | 261 | # ordinal_hidden_states = hidden_states[:, cur_beacon_indices == 0] | COMMENT |
| LOW | …Long_LLM/activation_beacon/src/llama/modeling_llama.py | 281 | # key_states[:, cur_beacon_indices == 0] = ordinal_key_states | COMMENT |
| LOW | …Long_LLM/activation_beacon/src/llama/modeling_llama.py | 301 | COMMENT | |
| LOW | research/Long_LLM/longllm_qlora/src/metrics.py | 101 | # if labels is None: | COMMENT |
| LOW | research/llm_embedder/src/lm/modeling_srlm.py | 481 | COMMENT | |
| LOW | research/llm_embedder/src/lm/modeling_srlm.py | 501 | # # concatenate extra context | COMMENT |
| LOW | research/llm_embedder/src/lm/modeling_srlm.py | 521 | # retrieved_chunks, retrieved_indices = self._dense_retrieval(query_embedding, key_embeddings, value_chunks) | COMMENT |
| LOW | research/llm_embedder/src/lm/modeling_srlm.py | 541 | # fixed_context = torch.cat([fixed_context, outputs.unsqueeze(1)], dim=1) # batch_size, -, chunk_size | COMMENT |
| LOW | research/llm_embedder/src/retrieval/metrics.py | 281 | # delete pos, neg, and teacher scores because they do not comply with new keys | COMMENT |
| LOW | research/Matroyshka_reranker/README.md | 261 | ### | COMMENT |
| LOW | …atroyshka_reranker/finetune/compensation/load_model.py | 61 | # peft_config = LoraConfig( | COMMENT |
| LOW | …yshka_reranker/finetune/compensation/mistral_config.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …oyshka_reranker/finetune/compensation/mistral_model.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …_reranker/finetune/self_distillation/mistral_config.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …a_reranker/finetune/self_distillation/mistral_model.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …search/Matroyshka_reranker/inference/mistral_config.py | 1 | # coding=utf-8 | COMMENT |
| LOW | research/Matroyshka_reranker/inference/mistral_model.py | 1 | # coding=utf-8 | COMMENT |
| LOW | research/visual_bge/visual_bge/eva_clip/transform.py | 41 | COMMENT | |
| LOW | …/MLVU/evaluation/generation_evaluation/evaluate_ssc.py | 221 | # count += 1 | COMMENT |
| LOW | research/BGE_VL/modeling_MMRet_CLIP.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …coder_only/layerwise/configuration_minicpm_reranker.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …ng/finetune/reranker/decoder_only/layerwise/trainer.py | 41 | COMMENT | |
| LOW | …er/decoder_only/layerwise/modeling_minicpm_reranker.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …bedding/finetune/reranker/decoder_only/base/trainer.py | 41 | COMMENT | |
| LOW | …Embedding/finetune/embedder/encoder_only/m3/trainer.py | 41 | # save_ckpt_for_sentence_transformers(output_dir, | COMMENT |
| LOW | …bedding/finetune/embedder/encoder_only/base/trainer.py | 41 | # save_ckpt_for_sentence_transformers(output_dir, | COMMENT |
| LOW | …mbedding/finetune/embedder/decoder_only/icl/trainer.py | 41 | # if self.is_world_process_zero(): | COMMENT |
| LOW | …bedding/finetune/embedder/decoder_only/base/trainer.py | 41 | # if self.is_world_process_zero(): | COMMENT |
| LOW | …/inference/reranker/decoder_only/models/gemma_model.py | 1 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 | COMMENT |
| LOW | …inference/reranker/decoder_only/models/gemma_config.py | 1 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 | COMMENT |
| LOW | …/decoder_only/models/configuration_minicpm_reranker.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …anker/decoder_only/models/modeling_minicpm_reranker.py | 1 | # coding=utf-8 | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | research/llm_reranker/evaluate.py | 112 | except Exception as e: | STRING |
| LOW | research/llm_reranker/finetune_for_instruction/data.py | 32 | except Exception as e: | CODE |
| LOW | research/llm_reranker/finetune_for_layerwise/data.py | 32 | except Exception as e: | CODE |
| LOW | …_Coder/evaluation/coderag_eval/test/create/swebench.py | 162 | except Exception as e: | CODE |
| LOW | …r/evaluation/coderag_eval/test/create/repoeval_repo.py | 50 | except Exception as e: | CODE |
| LOW | …_Coder/evaluation/coderag_eval/test/create/repoeval.py | 50 | except Exception as e: | CODE |
| LOW | …r/evaluation/coderag_eval/test/create/swebench_repo.py | 162 | except Exception as e: | CODE |
| LOW | …r/evaluation/coderag_eval/test/create/swebench_repo.py | 223 | except Exception as e: | CODE |
| MEDIUM | …r/evaluation/coderag_eval/test/create/swebench_repo.py | 224 | print(f"Error processing item {item['instance_id']}: {str(e)}") | CODE |
| LOW | research/BGE_Coder/data_generation/triplet_generator.py | 465 | except Exception as e: | CODE |
| LOW | research/BGE_Coder/data_generation/llm.py | 83 | except Exception as e: | CODE |
| MEDIUM | research/BGE_Coder/data_generation/llm.py | 62 | def create_completion(results): | CODE |
| MEDIUM | research/BGE_Coder/data_generation/run_generation.py | 311 | print(f'Error for loading examples from {examples_path}') | CODE |
| LOW | research/Reinforced_IR/inference/agent/gpt.py | 71 | except Exception as e: | CODE |
| LOW | research/Reinforced_IR/inference/agent/gpt.py | 123 | except Exception as e: | CODE |
| LOW | research/Reinforced_IR/data_generation/agent/gpt.py | 71 | except Exception as e: | CODE |
| LOW | research/Reinforced_IR/data_generation/agent/gpt.py | 123 | except Exception as e: | CODE |
| LOW | …arch/Long_LLM/activation_beacon/main/infbench_utils.py | 168 | except Exception: | CODE |
| LOW | research/Long_LLM/activation_beacon/src/utils.py | 48 | except Exception as e: | CODE |
| LOW | research/Long_LLM/longllm_qlora/main/infbench_utils.py | 168 | except Exception: | CODE |
| LOW | research/Long_LLM/longllm_qlora/src/utils.py | 48 | except Exception as e: | CODE |
| LOW | research/llm_embedder/src/utils/util.py | 43 | except Exception as e: | CODE |
| LOW | …arch/Matroyshka_reranker/finetune/compensation/data.py | 45 | except Exception as e: | CODE |
| LOW | …Matroyshka_reranker/finetune/self_distillation/data.py | 45 | except Exception as e: | CODE |
| LOW | research/Matroyshka_reranker/inference/rank_model.py | 123 | except Exception as e: | CODE |
| LOW | research/visual_bge/visual_bge/eva_clip/timm_model.py | 116 | except Exception as e: | CODE |
| MEDIUM | research/visual_bge/visual_bge/eva_clip/timm_model.py | 113 | def set_grad_checkpointing(self, enable=True): | CODE |
| LOW | …/MLVU/evaluation/generation_evaluation/evaluate_ssc.py | 108 | except Exception as e: | STRING |
| MEDIUM | …/MLVU/evaluation/generation_evaluation/evaluate_ssc.py | 109 | print(f"Error processing file '{key}': {e}") | STRING |
| LOW | …/MLVU/evaluation/generation_evaluation/evaluate_ssc.py | 191 | except Exception as e: | STRING |
| MEDIUM | …/MLVU/evaluation/generation_evaluation/evaluate_ssc.py | 192 | print(f"Error: {e}") | STRING |
| LOW | …U/evaluation/generation_evaluation/evaluate_summary.py | 98 | except Exception as e: | STRING |
| MEDIUM | …U/evaluation/generation_evaluation/evaluate_summary.py | 99 | print(f"Error processing file '{key}': {e}") | STRING |
| LOW | …U/evaluation/generation_evaluation/evaluate_summary.py | 181 | except Exception as e: | STRING |
| MEDIUM | …U/evaluation/generation_evaluation/evaluate_summary.py | 182 | print(f"Error: {e}") | STRING |
| LOW | FlagEmbedding/abc/inference/AbsReranker.py | 17 | except Exception: | CODE |
| LOW | FlagEmbedding/abc/inference/AbsEmbedder.py | 18 | except Exception: | CODE |
| LOW | FlagEmbedding/utils/transformers_compat.py | 15 | except Exception: | CODE |
| LOW | FlagEmbedding/utils/transformers_compat.py | 27 | except Exception: | CODE |
| MEDIUM | FlagEmbedding/utils/transformers_compat.py | 10 | def is_torch_fx_available(): | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | research/Long_LLM/activation_beacon/src/args.py | 260 | # ============================== | COMMENT |
| MEDIUM | research/Long_LLM/activation_beacon/src/args.py | 262 | # ============================== | COMMENT |
| MEDIUM | research/Long_LLM/activation_beacon/src/args.py | 293 | # ============================== | COMMENT |
| MEDIUM | research/Long_LLM/activation_beacon/src/args.py | 295 | # ============================== | COMMENT |
| MEDIUM | research/Long_LLM/longllm_qlora/src/args.py | 243 | # ============================== | COMMENT |
| MEDIUM | research/Long_LLM/longllm_qlora/src/args.py | 245 | # ============================== | COMMENT |
| MEDIUM | research/Long_LLM/longllm_qlora/src/args.py | 259 | # ============================== | COMMENT |
| MEDIUM | research/Long_LLM/longllm_qlora/src/args.py | 261 | # ============================== | COMMENT |
| MEDIUM | research/Long_LLM/longllm_qlora/src/args.py | 292 | # ============================== | COMMENT |
| MEDIUM | research/Long_LLM/longllm_qlora/src/args.py | 294 | # ============================== | COMMENT |
| MEDIUM | …search/visual_bge/visual_bge/eva_clip/eva_vit_model.py | 1 | # -------------------------------------------------------- | COMMENT |
| MEDIUM | …search/visual_bge/visual_bge/eva_clip/eva_vit_model.py | 3 | # -------------------------------------------------------- | COMMENT |
| MEDIUM | research/BGE_VL/modeling_MMRet_CLIP.py | 915 | # ------------------------------------------------------------ | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …arch/Long_LLM/activation_beacon/main/eval_multiturn.py | 147 | # NOTE: unwrap because we just use the model for evaluation | COMMENT |
| LOW | research/Long_LLM/activation_beacon/main/eval_lm.py | 63 | # stride=0 indicates we just use one forward pass with max_length for each text | COMMENT |
| LOW | research/Long_LLM/longllm_qlora/main/eval_generation.py | 89 | # NOTE: unwrap because we just use the model for evaluation | COMMENT |
| LOW | research/Long_LLM/longllm_qlora/main/eval_lm.py | 63 | # stride=0 indicates we just use one forward pass with max_length for each text | COMMENT |
| MEDIUM | research/llm_embedder/data/toy/convsearch.json | 4 | {"query": "How did Van Halen reunite with David Lee Roth? David Lee Roth called Eddie Van Halen to discuss what tracks w | CODE |
| MEDIUM⚡ | research/llm_embedder/data/toy/lrlm.json | 5 | {"query_id": 24236, "query": "that understand them.\n\nAnd as we'll discuss in greater length in the chapters to follow, | CODE |
| MEDIUM⚡ | research/llm_embedder/data/toy/lrlm.json | 5 | {"query_id": 24236, "query": "that understand them.\n\nAnd as we'll discuss in greater length in the chapters to follow, | CODE |
| MEDIUM⚡ | research/llm_embedder/data/toy/lrlm.json | 5 | {"query_id": 24236, "query": "that understand them.\n\nAnd as we'll discuss in greater length in the chapters to follow, | CODE |
| LOW⚡ | research/llm_embedder/data/toy/tool.json | 8 | {"query": "I am in the market for a new car and I want to explore different makes and models. Can you provide me with a | CODE |
| MEDIUM | research/visual_bge/visual_bge/eva_clip/tokenizer.py | 196 | # adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuance | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | research/BGE_Coder/data_generation/run_generation.py | 309 | min(30, len(examples_pool))) # sample 30 examples for few-shot generatio | CODE |
| MEDIUM | research/Long_LLM/activation_beacon/src/chat.py | 269 | # The number of few shot examples | COMMENT |
| MEDIUM | research/Long_LLM/longllm_qlora/src/chat.py | 236 | # The number of few shot examples | COMMENT |
| MEDIUM | research/llm_embedder/docs/fine-tune.md | 65 | "key": Optional[List[str]], # Retrieval results of the query. Usually used for RAG or reranking. | CODE |
| MEDIUM | research/llm_embedder/evaluation/eval_popqa.py | 257 | # popqa extracts few-shot examples from test data | COMMENT |
| MEDIUM | research/llm_embedder/data/toy/convsearch.json | 7 | {"query": "What is MMM? MMM is the debut mixtape by Puff Daddy, originally released on November 4, 2015 as a free mixtap | CODE |
| MEDIUM | research/C_MTEB/README.md | 44 | ### Evaluate embedding model | COMMENT |
| MEDIUM | Tutorials/README.md | 66 | ## [RAG](./6_RAG/) | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | …search/Long_LLM/activation_beacon/main/eval_passkey.py | 240 | # Create the heatmap with better aesthetics | COMMENT |
| MEDIUM | research/Long_LLM/activation_beacon/main/eval_needle.py | 369 | # Create the heatmap with better aesthetics | COMMENT |
| MEDIUM | research/Long_LLM/longllm_qlora/main/eval_passkey.py | 249 | # Create the heatmap with better aesthetics | COMMENT |
| MEDIUM | research/Long_LLM/longllm_qlora/main/eval_needle.py | 373 | # Create the heatmap with better aesthetics | COMMENT |
| MEDIUM | …/MLVU/evaluation/generation_evaluation/evaluate_ssc.py | 134 | # Create a new sample with the modified key | STRING |
| MEDIUM | …rch/MLVU/evaluation/generation_evaluation/calculate.py | 9 | # Define the keys to locate in the text | COMMENT |
| MEDIUM | …MLVU/evaluation/generation_evaluation/calculate_sum.py | 8 | # Define the keys to locate in the text | COMMENT |
| MEDIUM | …U/evaluation/generation_evaluation/evaluate_summary.py | 124 | # Create a new sample with the modified key | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| CRITICAL | …search/MLVU/evaluation/models/videochat2/open_bench.py | 109 | seg_embs = [model.llama_model.base_model.model.model.embed_tokens(seg_t) for seg_t in seg_tokens] | CODE |
| CRITICAL | …arch/MLVU/evaluation/models/videochat2/choice_bench.py | 109 | seg_embs = [model.llama_model.base_model.model.model.embed_tokens(seg_t) for seg_t in seg_tokens] | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH⚡ | research/llm_embedder/data/toy/lrlm.json | 7 | {"query_id": 11209, "query": "Because the meta element provides additional information about your web page, you have to | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | research/Long_LLM/longllm_qlora/README.md | 9 | We extend the context length of Llama-3-8B-Instruct from 8K to 80K via QLoRA fine-tuning. The entire training cycle is s | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | …search/Long_LLM/longllm_qlora/data_pipeline/_openai.py | 469 | parser.add_argument("--api_key", default="YOUR_API_KEY") | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | tests/conftest.py | 11 | # Check if we're using transformers v5+ | COMMENT |
| LOW⚡ | tests/test_imports_v5.py | 15 | # Check if we're using transformers v5+ | COMMENT |