Repository Analysis

FlagOpen/FlagEmbedding

Retrieval and Retrieval-augmented LLMs

22.9 Moderate AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of FlagOpen/FlagEmbedding, a Python project with 12,100 GitHub stars. SynthScan v2.0 examined 143,769 lines of code across 737 source files, recording 1873 pattern matches distributed across 19 syntactic categories. The overall adjusted score of 22.9 places this repository in the Moderate AI signal band.

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

22.9
Adjusted Score
22.9
Raw Score
100%
Time Factor
2026-08-24
Last Push
12.1K
Stars
Python
Language
143.8K
Lines of Code
737
Files
1.9K
Pattern Hits
2026-08-29
Scan Date
0.45
HC Hit Rate

What These Metrics Mean

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

Score History

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

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

Severity Breakdown

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

CRITICAL 2HIGH 329MEDIUM 83LOW 1459

Directory Score Breakdown

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

Pattern Findings

The scanner identified 1873 distinct pattern matches across 19 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

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

Cross-File Repetition290 hits · 1450 pts
SeverityFileLineSnippetContext
HIGH…h/llm_reranker/merge/configuration_minicpm_reranker.py0this is the configuration class to store the configuration of a [`llamamodel`]. it is used to instantiate an llama modelSTRING
HIGH…netune_for_layerwise/configuration_minicpm_reranker.py0this is the configuration class to store the configuration of a [`llamamodel`]. it is used to instantiate an llama modelSTRING
HIGH…coder_only/layerwise/configuration_minicpm_reranker.py0this is the configuration class to store the configuration of a [`llamamodel`]. it is used to instantiate an llama modelSTRING
HIGH…/decoder_only/models/configuration_minicpm_reranker.py0this is the configuration class to store the configuration of a [`llamamodel`]. it is used to instantiate an llama modelSTRING
HIGH…LLM/activation_beacon/src/llama/configuration_llama.py0this is the configuration class to store the configuration of a [`llamamodel`]. it is used to instantiate an llama modelSTRING
HIGH…search/llm_reranker/merge/modeling_minicpm_reranker.py0minicpmrotaryembedding extended with linear scaling. credits to the reddit user /u/kaiokendevSTRING
HIGH…er/finetune_for_layerwise/modeling_minicpm_reranker.py0minicpmrotaryembedding extended with linear scaling. credits to the reddit user /u/kaiokendevSTRING
HIGH…er/decoder_only/layerwise/modeling_minicpm_reranker.py0minicpmrotaryembedding extended with linear scaling. credits to the reddit user /u/kaiokendevSTRING
HIGH…anker/decoder_only/models/modeling_minicpm_reranker.py0minicpmrotaryembedding extended with linear scaling. credits to the reddit user /u/kaiokendevSTRING
HIGH…search/llm_reranker/merge/modeling_minicpm_reranker.py0rotaryembedding extended with dynamic ntk scaling. credits to the reddit users /u/bloc97 and /u/emozillaSTRING
HIGH…er/finetune_for_layerwise/modeling_minicpm_reranker.py0rotaryembedding extended with dynamic ntk scaling. credits to the reddit users /u/bloc97 and /u/emozillaSTRING
HIGH…er/decoder_only/layerwise/modeling_minicpm_reranker.py0rotaryembedding extended with dynamic ntk scaling. credits to the reddit users /u/bloc97 and /u/emozillaSTRING
HIGH…anker/decoder_only/models/modeling_minicpm_reranker.py0rotaryembedding extended with dynamic ntk scaling. credits to the reddit users /u/bloc97 and /u/emozillaSTRING
HIGH…earch/Long_LLM/activation_beacon/src/modeling_utils.py0rotaryembedding extended with dynamic ntk scaling. credits to the reddit users /u/bloc97 and /u/emozillaSTRING
HIGH…search/llm_reranker/merge/modeling_minicpm_reranker.py0applies rotary position embedding to the query and key tensors. args: q (`torch.tensor`): the query tensor. k (`torch.teSTRING
HIGH…er/finetune_for_layerwise/modeling_minicpm_reranker.py0applies rotary position embedding to the query and key tensors. args: q (`torch.tensor`): the query tensor. k (`torch.teSTRING
HIGH…er/decoder_only/layerwise/modeling_minicpm_reranker.py0applies rotary position embedding to the query and key tensors. args: q (`torch.tensor`): the query tensor. k (`torch.teSTRING
HIGH…anker/decoder_only/models/modeling_minicpm_reranker.py0applies rotary position embedding to the query and key tensors. args: q (`torch.tensor`): the query tensor. k (`torch.teSTRING
HIGH…search/llm_reranker/merge/modeling_minicpm_reranker.py0this is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). the hidden states go from (batch, num_key_vaSTRING
HIGH…er/finetune_for_layerwise/modeling_minicpm_reranker.py0this is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). the hidden states go from (batch, num_key_vaSTRING
HIGH…Long_LLM/activation_beacon/src/qwen2/modeling_qwen2.py0this is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). the hidden states go from (batch, num_key_vaSTRING
HIGH…_LLM/activation_beacon/src/mistral/modeling_mistral.py0this is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). the hidden states go from (batch, num_key_vaSTRING
HIGH…Long_LLM/activation_beacon/src/llama/modeling_llama.py0this is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). the hidden states go from (batch, num_key_vaSTRING
HIGH…er/decoder_only/layerwise/modeling_minicpm_reranker.py0this is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). the hidden states go from (batch, num_key_vaSTRING
HIGH…anker/decoder_only/models/modeling_minicpm_reranker.py0this is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). the hidden states go from (batch, num_key_vaSTRING
HIGH…search/llm_reranker/merge/modeling_minicpm_reranker.py0multi-headed attention from 'attention is all you need' paperSTRING
HIGH…er/finetune_for_layerwise/modeling_minicpm_reranker.py0multi-headed attention from 'attention is all you need' paperSTRING
HIGH…Long_LLM/activation_beacon/src/qwen2/modeling_qwen2.py0multi-headed attention from 'attention is all you need' paperSTRING
HIGH…_LLM/activation_beacon/src/mistral/modeling_mistral.py0multi-headed attention from 'attention is all you need' paperSTRING
HIGH…Long_LLM/activation_beacon/src/llama/modeling_llama.py0multi-headed attention from 'attention is all you need' paperSTRING
HIGHresearch/BGE_VL/modeling_MMRet_CLIP.py0multi-headed attention from 'attention is all you need' paperSTRING
HIGH…er/decoder_only/layerwise/modeling_minicpm_reranker.py0multi-headed attention from 'attention is all you need' paperSTRING
HIGH…anker/decoder_only/models/modeling_minicpm_reranker.py0multi-headed attention from 'attention is all you need' paperSTRING
HIGH…search/llm_reranker/merge/modeling_minicpm_reranker.py0clipattention flash attention module. this module inherits from `clipattention` as the weights of the module stays untouSTRING
HIGH…er/finetune_for_layerwise/modeling_minicpm_reranker.py0clipattention flash attention module. this module inherits from `clipattention` as the weights of the module stays untouSTRING
HIGH…er/decoder_only/layerwise/modeling_minicpm_reranker.py0clipattention flash attention module. this module inherits from `clipattention` as the weights of the module stays untouSTRING
HIGH…anker/decoder_only/models/modeling_minicpm_reranker.py0clipattention flash attention module. this module inherits from `clipattention` as the weights of the module stays untouSTRING
HIGH…Long_LLM/activation_beacon/src/qwen2/modeling_qwen2.py0clipattention flash attention module. this module inherits from `clipattention` as the weights of the module stays untouSTRING
HIGH…_LLM/activation_beacon/src/mistral/modeling_mistral.py0clipattention flash attention module. this module inherits from `clipattention` as the weights of the module stays untouSTRING
HIGH…Long_LLM/activation_beacon/src/llama/modeling_llama.py0clipattention flash attention module. this module inherits from `clipattention` as the weights of the module stays untouSTRING
HIGHresearch/BGE_VL/modeling_MMRet_CLIP.py0clipattention flash attention module. this module inherits from `clipattention` as the weights of the module stays untouSTRING
HIGH…search/llm_reranker/merge/modeling_minicpm_reranker.py0calls 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.py0calls 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.py0calls 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.py0calls 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.py0calls 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.py0calls 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.py0calls 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.py0sdpa attention module using torch.nn.functional.scaled_dot_product_attention. this module inherits from `clipattention` STRING
HIGH…er/finetune_for_layerwise/modeling_minicpm_reranker.py0sdpa attention module using torch.nn.functional.scaled_dot_product_attention. this module inherits from `clipattention` STRING
HIGH…er/decoder_only/layerwise/modeling_minicpm_reranker.py0sdpa attention module using torch.nn.functional.scaled_dot_product_attention. this module inherits from `clipattention` STRING
HIGH…anker/decoder_only/models/modeling_minicpm_reranker.py0sdpa 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.py0sdpa attention module using torch.nn.functional.scaled_dot_product_attention. this module inherits from `clipattention` STRING
HIGH…_LLM/activation_beacon/src/mistral/modeling_mistral.py0sdpa 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.py0sdpa attention module using torch.nn.functional.scaled_dot_product_attention. this module inherits from `clipattention` STRING
HIGHresearch/BGE_VL/modeling_MMRet_CLIP.py0sdpa attention module using torch.nn.functional.scaled_dot_product_attention. this module inherits from `clipattention` STRING
HIGH…search/llm_reranker/merge/modeling_minicpm_reranker.py0args: hidden_states (`torch.floattensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`toSTRING
HIGH…er/finetune_for_layerwise/modeling_minicpm_reranker.py0args: hidden_states (`torch.floattensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`toSTRING
HIGH…er/decoder_only/layerwise/modeling_minicpm_reranker.py0args: hidden_states (`torch.floattensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`toSTRING
HIGH…anker/decoder_only/models/modeling_minicpm_reranker.py0args: hidden_states (`torch.floattensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`toSTRING
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Unused Imports742 hits · 726 pts
SeverityFileLineSnippetContext
LOW…h/llm_reranker/merge/configuration_minicpm_reranker.py183CODE
LOWresearch/llm_reranker/merge/__init__.py1CODE
LOWresearch/llm_reranker/merge/__init__.py2CODE
LOWresearch/llm_reranker/merge/__init__.py3CODE
LOW…search/llm_reranker/merge/modeling_minicpm_reranker.py21CODE
LOW…search/llm_reranker/merge/modeling_minicpm_reranker.py31CODE
LOW…search/llm_reranker/merge/modeling_minicpm_reranker.py31CODE
LOW…search/llm_reranker/merge/modeling_minicpm_reranker.py41CODE
LOW…search/llm_reranker/merge/modeling_minicpm_reranker.py45CODE
LOW…earch/llm_reranker/finetune_for_instruction/trainer.py1CODE
LOW…netune_for_layerwise/configuration_minicpm_reranker.py184CODE
LOWresearch/llm_reranker/finetune_for_layerwise/trainer.py1CODE
LOW…er/finetune_for_layerwise/modeling_minicpm_reranker.py21CODE
LOW…er/finetune_for_layerwise/modeling_minicpm_reranker.py31CODE
LOW…er/finetune_for_layerwise/modeling_minicpm_reranker.py31CODE
LOW…er/finetune_for_layerwise/modeling_minicpm_reranker.py41CODE
LOW…er/finetune_for_layerwise/modeling_minicpm_reranker.py45CODE
LOWresearch/llm_reranker/finetune_for_layerwise/data.py1CODE
LOWresearch/BGE_Coder/evaluation/coir_eval/arguments.py4CODE
LOW…ch/BGE_Coder/evaluation/coderag_eval/test/arguments.py1CODE
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LOWresearch/BGE_Coder/evaluation/coderag_eval/test/main.py19CODE
LOW…evaluation/coderag_eval/test/create/live_code_bench.py4CODE
LOW…evaluation/coderag_eval/test/create/code_search_net.py5CODE
LOW…evaluation/coderag_eval/test/create/code_search_net.py6CODE
LOW…/BGE_Coder/evaluation/coderag_eval/test/create/odex.py2CODE
LOW…/BGE_Coder/evaluation/coderag_eval/test/create/odex.py3CODE
LOW…/BGE_Coder/evaluation/coderag_eval/test/create/odex.py7CODE
LOWresearch/Reinforced_IR/finetune/retriever/runner.py8CODE
LOWresearch/Reinforced_IR/finetune/retriever/runner.py8CODE
LOWresearch/Reinforced_IR/finetune/retriever/runner.py11CODE
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LOWresearch/Reinforced_IR/finetune/retriever/dataset.py1CODE
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LOWresearch/Reinforced_IR/finetune/retriever/dataset.py10CODE
LOWresearch/Reinforced_IR/finetune/retriever/dataset.py10CODE
LOWresearch/Reinforced_IR/finetune/retriever/dataset.py10CODE
LOWresearch/Reinforced_IR/finetune/retriever/dataset.py10CODE
LOWresearch/Reinforced_IR/finetune/retriever/dataset.py18CODE
LOWresearch/Reinforced_IR/finetune/retriever/dataset.py19CODE
LOWresearch/Reinforced_IR/finetune/retriever/modeling.py6CODE
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LOWresearch/Reinforced_IR/finetune/retriever/modeling.py13CODE
LOWresearch/Reinforced_IR/finetune/retriever/modeling.py18CODE
LOW…rch/Reinforced_IR/finetune/generator/save_tokenizer.py2CODE
LOW…rch/Reinforced_IR/finetune/generator/save_tokenizer.py3CODE
LOW…rch/Reinforced_IR/finetune/generator/save_tokenizer.py4CODE
LOWresearch/Reinforced_IR/inference/ir_model.py1CODE
LOWresearch/Reinforced_IR/inference/ir_model.py4CODE
LOWresearch/Reinforced_IR/inference/ir_model.py4CODE
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Deep Nesting210 hits · 208 pts
SeverityFileLineSnippetContext
LOWresearch/llm_reranker/evaluate.py45CODE
LOW…reranker/merge/merge_layerwise_model_from_raw_model.py6CODE
LOW…search/llm_reranker/merge/modeling_minicpm_reranker.py1210CODE
LOW…er/merge/merge_layerwise_model_from_finetuned_model.py5CODE
LOWresearch/llm_reranker/merge/merge_base_model.py5CODE
LOWresearch/llm_reranker/finetune_for_instruction/data.py149CODE
LOW…arch/llm_reranker/finetune_for_layerwise/load_model.py8CODE
LOW…er/finetune_for_layerwise/modeling_minicpm_reranker.py1211CODE
LOWresearch/llm_reranker/finetune_for_layerwise/data.py144CODE
LOWresearch/BGE_Coder/evaluation/coderag_eval/test/main.py111CODE
LOW…_Coder/evaluation/coderag_eval/test/create/swebench.py128CODE
LOW…_Coder/evaluation/coderag_eval/test/create/swebench.py144CODE
LOW…r/evaluation/coderag_eval/test/create/repoeval_repo.py210CODE
LOW…r/evaluation/coderag_eval/test/create/repoeval_repo.py116CODE
LOW…_Coder/evaluation/coderag_eval/test/create/repoeval.py221CODE
LOW…_Coder/evaluation/coderag_eval/test/create/repoeval.py116CODE
LOW…r/evaluation/coderag_eval/test/create/swebench_repo.py128CODE
LOW…r/evaluation/coderag_eval/test/create/swebench_repo.py144CODE
LOWresearch/BGE_Coder/data_generation/corpus_generator.py18CODE
LOWresearch/BGE_Coder/data_generation/triplet_generator.py341CODE
LOWresearch/BGE_Coder/data_generation/triplet_generator.py485CODE
LOW…BGE_Coder/data_generation/format_generated_examples.py56CODE
LOWresearch/BGE_Coder/data_generation/constant.py423CODE
LOWresearch/Reinforced_IR/finetune/retriever/dataset.py99CODE
LOWresearch/Reinforced_IR/finetune/retriever/dataset.py277CODE
LOWresearch/Reinforced_IR/finetune/retriever/dataset.py414CODE
LOW…inforced_IR/data_generation/generate_generator_data.py47CODE
LOWresearch/Reinforced_IR/data_generation/utils.py22CODE
LOWresearch/Reinforced_IR/data_generation/utils.py65CODE
LOWresearch/Reinforced_IR/data_generation/utils.py302CODE
LOW…nforced_IR/data_generation/generate_universal_query.py33CODE
LOW…inforced_IR/data_generation/generate_retriever_data.py46CODE
LOW…arch/Long_LLM/activation_beacon/main/eval_longbench.py96CODE
LOW…arch/Long_LLM/activation_beacon/main/eval_multiturn.py50CODE
LOW…earch/Long_LLM/activation_beacon/main/eval_infbench.py97CODE
LOW…arch/Long_LLM/activation_beacon/main/infbench_utils.py406CODE
LOW…arch/Long_LLM/activation_beacon/main/infbench_utils.py516CODE
LOW…earch/Long_LLM/activation_beacon/main/pretrain_data.py54CODE
LOWresearch/Long_LLM/activation_beacon/main/eval_needle.py212CODE
LOWresearch/Long_LLM/activation_beacon/main/eval_mmlu.py64CODE
LOWresearch/Long_LLM/activation_beacon/main/eval_mmlu.py98CODE
LOWresearch/Long_LLM/activation_beacon/main/eval_topic.py93CODE
LOWresearch/Long_LLM/activation_beacon/src/__init__.py15CODE
LOWresearch/Long_LLM/activation_beacon/src/chat.py31CODE
LOWresearch/Long_LLM/activation_beacon/src/chat.py280CODE
LOWresearch/Long_LLM/activation_beacon/src/chat.py529CODE
LOWresearch/Long_LLM/activation_beacon/src/utils.py38CODE
LOWresearch/Long_LLM/activation_beacon/src/utils.py220CODE
LOW…earch/Long_LLM/activation_beacon/src/modeling_utils.py32CODE
LOW…earch/Long_LLM/activation_beacon/src/modeling_utils.py249CODE
LOWresearch/Long_LLM/activation_beacon/src/trainer.py147CODE
LOW…arch/Long_LLM/activation_beacon/src/modeling_beacon.py930CODE
LOW…arch/Long_LLM/activation_beacon/src/modeling_beacon.py200CODE
LOW…arch/Long_LLM/activation_beacon/src/modeling_beacon.py273CODE
LOW…arch/Long_LLM/activation_beacon/src/modeling_beacon.py394CODE
LOW…arch/Long_LLM/activation_beacon/src/modeling_beacon.py757CODE
LOWresearch/Long_LLM/activation_beacon/src/data.py28CODE
LOWresearch/Long_LLM/activation_beacon/src/data.py102CODE
LOW…Long_LLM/activation_beacon/src/qwen2/modeling_qwen2.py187CODE
LOW…Long_LLM/activation_beacon/src/qwen2/modeling_qwen2.py933CODE
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Docstring Block Structure36 hits · 180 pts
SeverityFileLineSnippetContext
HIGHresearch/BGE_Coder/data_generation/constant.py430 Given a task, return the generation prompt for the task. Args: - task: Task: the task object - texSTRING
HIGHresearch/Reinforced_IR/finetune/retriever/modeling.py160Compute the distillation loss. Args: kd_loss_type (str): Type of knowledge distillation loss, suppoSTRING
HIGHFlagEmbedding/abc/finetune/reranker/AbsDataset.py57Load dataset from path. Args: file_path (str): Path to load the datasets from. Raises: STRING
HIGHFlagEmbedding/abc/finetune/embedder/AbsModeling.py305Compute the distillation loss. Args: kd_loss_type (str): Type of knowledge distillation loss, suppoSTRING
HIGHFlagEmbedding/abc/finetune/embedder/AbsDataset.py55Load dataset from path. Args: file_path (str): Path to load the datasets from. Raises: STRING
HIGHFlagEmbedding/abc/inference/AbsReranker.py100 Args: devices (Union[str, int, List[str], List[int]]): Specified devices, can be `str`, `int`, lisSTRING
HIGHFlagEmbedding/abc/inference/AbsEmbedder.py111 Args: devices (Union[str, int, List[str], List[int]]): specified devices, can be `str`, `int`, lisSTRING
HIGHFlagEmbedding/abc/inference/AbsEmbedder.py438concatenate and return the results from all the processes Args: results_list (List[Union[torch.TensSTRING
HIGHFlagEmbedding/abc/evaluation/searcher.py51 Abstract method to be overrode. This is called during the retrieval process. Parameters: STRING
HIGHFlagEmbedding/abc/evaluation/searcher.py83 This is called during the retrieval process. Parameters: corpus: Dict[str, Dict[stSTRING
HIGHFlagEmbedding/abc/evaluation/searcher.py191 This is called during the reranking process. Parameters: corpus: Dict[str, Dict[stSTRING
HIGHFlagEmbedding/abc/evaluation/data_loader.py56Check the validity of dataset names Args: dataset_names (Union[str, List[str]]): a dataset name (stSTRING
HIGHFlagEmbedding/abc/evaluation/data_loader.py116Load the qrels from the dataset. Args: dataset_name (Optional[str], optional): Name of the dataset.STRING
HIGHFlagEmbedding/abc/evaluation/data_loader.py144Load the queries from the dataset. Args: dataset_name (Optional[str], optional): Name of the dataseSTRING
HIGHFlagEmbedding/abc/evaluation/data_loader.py176Abstract method to load corpus from remote dataset, to be overrode in child class. Args: dataset_naSTRING
HIGHFlagEmbedding/abc/evaluation/data_loader.py196Abstract method to load relevance from remote dataset, to be overrode in child class. Args: datasetSTRING
HIGHFlagEmbedding/abc/evaluation/data_loader.py217Abstract method to load queries from remote dataset, to be overrode in child class. Args: dataset_nSTRING
HIGHFlagEmbedding/abc/evaluation/data_loader.py256Load relevance from local dataset. Args: save_dir (str): Path to save the loaded relevance. STRING
HIGHFlagEmbedding/abc/evaluation/data_loader.py291Load queries from local dataset. Args: save_dir (str): Path to save the loaded queries. STRING
HIGHFlagEmbedding/abc/evaluation/data_loader.py320Download file from provided URL. Args: download_url (str): Source URL of the file. saveSTRING
HIGHFlagEmbedding/abc/evaluation/data_loader.py370Download and unzip the gzip file from provided URL. Args: download_url (str): Source URL of the gziSTRING
HIGHFlagEmbedding/abc/evaluation/data_loader.py396Download 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.py82Use the pooling method to get the dense embedding. Args: last_hidden_state (torch.Tensor): The modeSTRING
HIGH…mbedding/finetune/embedder/encoder_only/m3/modeling.py240Compute the ensemble score of the three methods. Args: q_reps (torch.Tensor): Query representationsSTRING
HIGH…edding/finetune/embedder/encoder_only/base/modeling.py109Use the pooling method to get the sentence embedding. Args: last_hidden_state (torch.Tensor): The mSTRING
HIGH…bedding/finetune/embedder/decoder_only/icl/modeling.py110Use the pooling method to get the sentence embedding. Args: last_hidden_state (torch.Tensor): The mSTRING
HIGH…edding/finetune/embedder/decoder_only/base/modeling.py110Use the pooling method to get the sentence embedding. Args: last_hidden_state (torch.Tensor): The mSTRING
HIGHFlagEmbedding/inference/auto_embedder.py38 Load a finetuned model according to the provided vars. Args: model_name_or_path (str): If STRING
HIGHFlagEmbedding/inference/auto_reranker.py32 Load a finetuned model according to the provided vars. Args: model_name_or_path (str): If STRING
HIGHFlagEmbedding/inference/embedder/encoder_only/base.py292The pooling function. Args: last_hidden_state (torch.Tensor): The last hidden state of the model. STRING
HIGHFlagEmbedding/evaluation/bright/searcher.py27 This is called during the retrieval process. Parameters: corpus: Dict[str, Dict[str, Any]]STRING
HIGHFlagEmbedding/evaluation/beir/data_loader.py311Load the qrels from the dataset. Args: dataset_name (Optional[str], optional): Name of the dataset.STRING
HIGHFlagEmbedding/evaluation/beir/data_loader.py340Load the queries from the dataset. Args: dataset_name (Optional[str], optional): Name of the dataseSTRING
HIGHFlagEmbedding/evaluation/beir/data_loader.py398Load relevance from local dataset. Args: save_dir (str): Path to save the loaded relevance. STRING
HIGHFlagEmbedding/evaluation/beir/data_loader.py439Load queries from local dataset. Args: save_dir (str): Path to save the loaded queries. STRING
HIGHFlagEmbedding/evaluation/mkqa/data_loader.py57Try to load qrels from local datasets. Args: save_dir (str): Directory that save the data files. STRING
Hyper-Verbose Identifiers174 hits · 175 pts
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LOW…search/llm_reranker/merge/modeling_minicpm_reranker.py1407 def prepare_inputs_for_generation(CODE
LOW…er/merge/merge_layerwise_model_from_finetuned_model.py5def 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.py38 def gradient_checkpointing_enable(self, **kwargs):CODE
LOW…arch/llm_reranker/finetune_for_instruction/modeling.py41 def enable_input_require_grads(self, **kwargs):CODE
LOW…er/finetune_for_layerwise/modeling_minicpm_reranker.py1408 def prepare_inputs_for_generation(CODE
LOW…search/llm_reranker/finetune_for_layerwise/modeling.py40 def gradient_checkpointing_enable(self, **kwargs):CODE
LOW…search/llm_reranker/finetune_for_layerwise/modeling.py43 def enable_input_require_grads(self, **kwargs):CODE
LOWresearch/reranker/modeling.py30 def gradient_checkpointing_enable(self, **kwargs):CODE
LOWresearch/BGE_Coder/evaluation/coir_eval/prompts.py4def get_task_def_by_task_name(task_name: str) -> str:CODE
LOW…arch/BGE_Coder/evaluation/coderag_eval/test/prompts.py4def get_task_def_by_task_name(task_name: str) -> str:CODE
LOW…_Coder/evaluation/coderag_eval/test/create/swebench.py128def ingest_directory_contents(root_dir, include_tests=False):CODE
LOW…r/evaluation/coderag_eval/test/create/repoeval_repo.py102 def _merge_windows_with_same_context(self, code_windows):CODE
LOW…_Coder/evaluation/coderag_eval/test/create/repoeval.py102 def _merge_windows_with_same_context(self, code_windows):CODE
LOW…r/evaluation/coderag_eval/test/create/swebench_repo.py128def ingest_directory_contents(root_dir, include_tests=False):CODE
LOWresearch/BGE_Coder/data_generation/triplet_generator.py34 def _gen_for_code_modification_retrieval(CODE
LOWresearch/BGE_Coder/data_generation/triplet_generator.py81 def _gen_for_code_comparison_retrieval(CODE
LOWresearch/BGE_Coder/data_generation/triplet_generator.py126 def _gen_for_code_context_retrieval(CODE
LOWresearch/BGE_Coder/data_generation/triplet_generator.py208 def _gen_for_bug_desc_retrieval(CODE
LOWresearch/BGE_Coder/data_generation/triplet_generator.py253 def _gen_for_two_step_not_use_last(CODE
LOWresearch/BGE_Coder/data_generation/triplet_generator.py297 def _gen_for_two_step_use_last(CODE
LOW…BGE_Coder/data_generation/format_generated_examples.py7def format_generated_examples(CODE
LOWresearch/BGE_Coder/data_generation/constant.py65def get_task_def_by_task_type(task_type: Union[str, TaskType]) -> Tuple[str, TaskType, str]:CODE
LOWresearch/BGE_Coder/data_generation/constant.py391def get_pos_as_input_by_task_type(task_type: TaskType) -> bool:CODE
LOWresearch/BGE_Coder/data_generation/constant.py759def get_quality_control_prompt(STRING
LOWresearch/Reinforced_IR/data_generation/utils.py229def generate_llm_dpo_train_data(CODE
LOW…/Reinforced_IR/data_generation/prompts/hyde_prompts.py42def get_additional_info_generation_prompt(dataset_name: str, query: str) -> str:CODE
LOW…h/Reinforced_IR/data_generation/prompts/get_prompts.py324def get_query_generation_prompt(dataset_name: str, passage: str, use_examples: bool) -> str:CODE
LOW…h/Reinforced_IR/data_generation/prompts/get_prompts.py465def get_additional_info_generation_prompt(dataset_name: str, query: str) -> str:CODE
LOW…h/Reinforced_IR/data_generation/prompts/get_prompts.py488def get_additional_info_generation_long_prompt(dataset_name: str, query: str) -> str:CODE
LOW…h/Reinforced_IR/data_generation/prompts/get_prompts.py511def get_additional_info_generation_long_air_prompt(dataset_name: str, query: str) -> str:CODE
LOW…h/Reinforced_IR/data_generation/prompts/get_prompts.py535def get_additional_info_generation_train_prompt(dataset_name: str, query: str, reference: str) -> str:CODE
LOW…h/Reinforced_IR/data_generation/prompts/get_prompts.py670def get_quality_control_prompt(dataset_name: str, query: str, passage: str) -> str:CODE
LOW…arch/Long_LLM/activation_beacon/main/infbench_utils.py134def get_score_one_kv_retrieval(pred, label, model_name: str) -> bool:CODE
LOW…arch/Long_LLM/activation_beacon/main/infbench_utils.py147def get_score_one_number_string(pred, label, model_name: str) -> bool:CODE
LOW…arch/Long_LLM/activation_beacon/main/infbench_utils.py230def get_score_one_longdialogue_qa_eng(pred, label, model_name: str) -> bool:CODE
LOW…arch/Long_LLM/activation_beacon/main/infbench_utils.py239def get_score_one_longbook_choice_eng(pred, label, model_name: str) -> bool:CODE
LOW…arch/Long_LLM/activation_beacon/main/infbench_utils.py280def get_score_one_longbook_qa_eng(pred, label, model_name: str) -> float:CODE
LOW…arch/Long_LLM/activation_beacon/main/infbench_utils.py284def get_score_one_longbook_sum_eng(CODE
LOW…arch/Long_LLM/activation_beacon/main/infbench_utils.py297def get_score_one_longbook_qa_chn(pred, label, model_name: str) -> float:CODE
LOWresearch/Long_LLM/activation_beacon/src/chat.py625 def extract_text_from_messages(self):CODE
LOWresearch/Long_LLM/activation_beacon/src/utils.py175def get_max_length_in_nested_lists(lst):CODE
LOWresearch/Long_LLM/activation_beacon/src/utils.py213def are_elements_of_same_length(lst: List):CODE
LOW…arch/Long_LLM/activation_beacon/src/modeling_beacon.py890 def _extract_beacon_and_raw_memory(CODE
LOWresearch/Long_LLM/activation_beacon/src/data.py28 def _process_language_modeling(data, indices, tokenizer, min_length, max_length):CODE
LOWresearch/Long_LLM/activation_beacon/src/data.py54 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.py1293 def prepare_inputs_for_generation(CODE
LOW…_LLM/activation_beacon/src/mistral/modeling_mistral.py1279 def prepare_inputs_for_generation(CODE
LOW…Long_LLM/activation_beacon/src/llama/modeling_llama.py1346 def prepare_inputs_for_generation(CODE
LOW…search/Long_LLM/longllm_qlora/data_pipeline/_openai.py110async def process_api_requests_from_file(CODE
LOW…search/Long_LLM/longllm_qlora/data_pipeline/_openai.py394def num_tokens_consumed_from_request(CODE
LOW…search/Long_LLM/longllm_qlora/data_pipeline/_openai.py453def task_id_generator_function():CODE
LOWresearch/Long_LLM/longllm_qlora/main/infbench_utils.py134def get_score_one_kv_retrieval(pred, label, model_name: str) -> bool:CODE
LOWresearch/Long_LLM/longllm_qlora/main/infbench_utils.py147def get_score_one_number_string(pred, label, model_name: str) -> bool:CODE
LOWresearch/Long_LLM/longllm_qlora/main/infbench_utils.py230def get_score_one_longdialogue_qa_eng(pred, label, model_name: str) -> bool:CODE
LOWresearch/Long_LLM/longllm_qlora/main/infbench_utils.py239def get_score_one_longbook_choice_eng(pred, label, model_name: str) -> bool:CODE
LOWresearch/Long_LLM/longllm_qlora/main/infbench_utils.py280def get_score_one_longbook_qa_eng(pred, label, model_name: str) -> float:CODE
LOWresearch/Long_LLM/longllm_qlora/main/infbench_utils.py284def get_score_one_longbook_sum_eng(CODE
LOWresearch/Long_LLM/longllm_qlora/main/infbench_utils.py295def get_score_one_longbook_qa_chn(pred, label, model_name: str) -> float:CODE
LOWresearch/Long_LLM/longllm_qlora/src/chat.py592 def extract_text_from_messages(self):CODE
LOWresearch/Long_LLM/longllm_qlora/src/utils.py175def get_max_length_in_nested_lists(lst):CODE
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Modern Structural Boilerplate143 hits · 143 pts
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LOWresearch/llm_reranker/finetune_for_instruction/run.py18logger = logging.getLogger(__name__)CODE
LOW…arch/llm_reranker/finetune_for_instruction/modeling.py10logger = logging.getLogger(__name__)CODE
LOWresearch/llm_reranker/finetune_for_layerwise/run.py18logger = logging.getLogger(__name__)CODE
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LOWresearch/reranker/run.py16logger = logging.getLogger(__name__)CODE
LOWresearch/reranker/trainer.py10logger = logging.getLogger(__name__)CODE
LOWresearch/reranker/modeling.py10logger = logging.getLogger(__name__)CODE
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LOWresearch/Reinforced_IR/finetune/retriever/dataset.py21logger = logging.getLogger(__name__)CODE
LOWresearch/Reinforced_IR/finetune/retriever/trainer.py8logger = logging.getLogger(__name__)CODE
LOWresearch/Reinforced_IR/finetune/retriever/modeling.py21logger = logging.getLogger(__name__)CODE
LOW…arch/Reinforced_IR/data_generation/prompts/__init__.py30__all__ = [CODE
LOWresearch/Long_LLM/activation_beacon/main/train.py17logger = logging.getLogger(__name__)CODE
LOWresearch/Long_LLM/activation_beacon/src/utils.py20logger = logging.getLogger(__name__)CODE
LOWresearch/Long_LLM/longllm_qlora/main/train.py18logger = logging.getLogger(__name__)CODE
LOWresearch/Long_LLM/longllm_qlora/src/utils.py20logger = logging.getLogger(__name__)CODE
LOWresearch/llm_embedder/run_ranker.py15logger = logging.getLogger(__name__)CODE
LOWresearch/llm_embedder/run_dense.py16logger = logging.getLogger(__name__)CODE
LOWresearch/llm_embedder/run_lm_score.py16logger = logging.getLogger(__name__)CODE
LOWresearch/llm_embedder/evaluation/eval_icl.py25logger = logging.getLogger(__name__)CODE
LOWresearch/llm_embedder/evaluation/icl_utils.py12logger = logging.getLogger(__name__)CODE
LOWresearch/llm_embedder/evaluation/eval_tool.py11logger = logging.getLogger(__name__)CODE
LOWresearch/llm_embedder/evaluation/eval_retrieval.py19logger = logging.getLogger(__name__)CODE
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LOWresearch/llm_embedder/src/retrieval/metrics.py10logger = logging.getLogger(__name__)CODE
LOWresearch/llm_embedder/src/retrieval/modeling_unified.py8logger = logging.getLogger(__name__)CODE
LOWresearch/llm_embedder/src/retrieval/trainer.py16logger = logging.getLogger(__name__)CODE
LOWresearch/old-examples/pretrain/retromae_pretrain/run.py24logger = logging.getLogger(__name__)CODE
LOW…rch/old-examples/pretrain/retromae_pretrain/trainer.py8logger = logging.getLogger(__name__)CODE
LOW…ch/old-examples/pretrain/retromae_pretrain/modeling.py12logger = logging.getLogger(__name__)CODE
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LOW…/Matroyshka_reranker/finetune/compensation/modeling.py15logger = logging.getLogger(__name__)CODE
LOW…/Matroyshka_reranker/finetune/self_distillation/run.py18logger = logging.getLogger(__name__)CODE
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LOWresearch/llm_dense_retriever/finetune/run.py19logger = logging.getLogger(__name__)CODE
LOWresearch/llm_dense_retriever/finetune/modeling.py15logger = logging.getLogger(__name__)CODE
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LOWresearch/LLARA/finetune/modeling.py13logger = logging.getLogger(__name__)CODE
LOWresearch/LLARA/pretrain/run.py18logger = logging.getLogger(__name__)CODE
LOWresearch/baai_general_embedding/finetune/run.py17logger = logging.getLogger(__name__)CODE
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LOWresearch/visual_bge/visual_bge/modeling.py15logger = logging.getLogger(__name__)CODE
LOWresearch/visual_bge/visual_bge/eva_clip/openai.py15__all__ = ["list_openai_models", "load_openai_model"]CODE
LOWresearch/BGE_VL/eval/eval_fashioniq.py19logger = logging.getLogger(__name__)CODE
LOWresearch/BGE_VL/eval/eval_Circo.py14logger = logging.getLogger(__name__)CODE
LOWresearch/BGE_M3/run.py25logger = logging.getLogger(__name__)CODE
LOWresearch/BGE_M3/modeling.py14logger = logging.getLogger(__name__)CODE
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AI Structural Patterns101 hits · 97 pts
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LOW…h/llm_reranker/merge/configuration_minicpm_reranker.py115CODE
LOW…search/llm_reranker/merge/modeling_minicpm_reranker.py990CODE
LOW…search/llm_reranker/merge/modeling_minicpm_reranker.py1210CODE
LOW…search/llm_reranker/merge/modeling_minicpm_reranker.py1473CODE
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LOW…er/finetune_for_layerwise/modeling_minicpm_reranker.py991CODE
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LOW…er/finetune_for_layerwise/modeling_minicpm_reranker.py1474CODE
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LOWresearch/BGE_Coder/data_generation/triplet_generator.py552CODE
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LOWresearch/Long_LLM/activation_beacon/src/data.py174CODE
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LOW…Long_LLM/activation_beacon/src/qwen2/modeling_qwen2.py1144CODE
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LOW…LLM/activation_beacon/src/llama/configuration_llama.py117CODE
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LOWresearch/Long_LLM/longllm_qlora/src/data.py88CODE
LOWresearch/Long_LLM/longllm_qlora/src/data.py157CODE
LOWresearch/llm_embedder/run_lm_score.py56CODE
LOWresearch/llm_embedder/src/lm/modeling_srlm.py25CODE
LOWresearch/llm_embedder/src/retrieval/modeling_bm25.py109CODE
LOWresearch/llm_embedder/src/retrieval/modeling_dense.py21CODE
LOWresearch/llm_embedder/src/retrieval/modeling_dense.py345CODE
LOWresearch/llm_embedder/src/retrieval/data.py122CODE
LOWresearch/llm_embedder/src/retrieval/data.py187CODE
LOWresearch/C_MTEB/MLDR/mteb_dense_eval/flag_dres_model.py40CODE
LOW…oyshka_reranker/finetune/compensation/mistral_model.py228CODE
LOW…oyshka_reranker/finetune/compensation/mistral_model.py517CODE
LOW…a_reranker/finetune/self_distillation/mistral_model.py228CODE
LOW…a_reranker/finetune/self_distillation/mistral_model.py517CODE
LOWresearch/Matroyshka_reranker/inference/rank_model.py49CODE
LOWresearch/Matroyshka_reranker/inference/rank_model.py151CODE
LOWresearch/Matroyshka_reranker/inference/mistral_model.py228CODE
LOWresearch/Matroyshka_reranker/inference/mistral_model.py517CODE
LOWresearch/llm_dense_retriever/finetune/modeling.py116CODE
LOWresearch/LLARA/pretrain/modeling.py23CODE
LOWresearch/LLARA/pretrain/modeling.py266CODE
LOW…arch/visual_bge/visual_bge/eva_clip/modified_resnet.py55CODE
LOWresearch/visual_bge/visual_bge/eva_clip/factory.py211CODE
LOWresearch/visual_bge/visual_bge/eva_clip/factory.py362CODE
LOWresearch/visual_bge/visual_bge/eva_clip/factory.py416CODE
LOWresearch/visual_bge/visual_bge/eva_clip/factory.py471CODE
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Slop Phrases43 hits · 86 pts
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MEDIUMresearch/llm_embedder/data/toy/lrlm.json7{"query_id": 11209, "query": "Because the meta element provides additional information about your web page, you have to CODE
LOWresearch/llm_embedder/data/toy/lrlm.json7{"query_id": 11209, "query": "Because the meta element provides additional information about your web page, you have to CODE
LOWresearch/llm_embedder/data/toy/tool.json3{"query": "I need to ensure that the URL 'https://friendwebsite.com' is safe. Please check if it is a known phishing attCODE
MEDIUMresearch/llm_embedder/data/toy/tool.json3{"query": "I need to ensure that the URL 'https://friendwebsite.com' is safe. Please check if it is a known phishing attCODE
LOWresearch/llm_embedder/data/toy/tool.json5{"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.py12 devices=["cuda:3"], # if you don't have GPUs, you can use ["cpu", "cpu"]CODE
MEDIUM…/inference/reranker/encoder_only/base_single_device.py12 devices=["cuda:3"], # if you don't have GPUs, you can use ["cpu", "cpu"]CODE
MEDIUM…/inference/reranker/encoder_only/base_multi_devices.py12 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.py12 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.py12 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.py12 devices=["cuda:3"], # if you don't have GPUs, you can use ["cpu", "cpu"]CODE
MEDIUM…rence/reranker/decoder_only/auto_base_single_device.py11 devices=["cuda:3"], # if you don't have GPUs, you can use ["cpu", "cpu"]CODE
MEDIUM…/inference/reranker/decoder_only/base_single_device.py11 devices=["cuda:3"], # if you don't have GPUs, you can use ["cpu", "cpu"]CODE
MEDIUM…nce/reranker/decoder_only/lightweight_multi_devices.py12 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.py12 devices=["cuda:3"], # if you don't have GPUs, you can use ["cpu", "cpu"]CODE
MEDIUM…/inference/reranker/decoder_only/base_multi_devices.py11 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.py11 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.py12 devices=["cuda:3"], # if you don't have GPUs, you can use ["cpu", "cpu"]CODE
MEDIUM…rence/reranker/decoder_only/layerwise_single_device.py12 devices=["cuda:3"], # if you don't have GPUs, you can use ["cpu", "cpu"]CODE
MEDIUM…/reranker/decoder_only/auto_layerwise_multi_devices.py12 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.py12 devices=["cuda:3"], # if you don't have GPUs, you can use ["cpu", "cpu"]CODE
MEDIUM…rence/embedder/encoder_only/auto_base_single_device.py9 devices="cuda:0", # if you don't have a GPU, you can use "cpu"CODE
MEDIUM…/inference/embedder/encoder_only/base_single_device.py10 devices="cuda:0", # if you don't have a GPU, you can use "cpu"CODE
MEDIUM…/inference/embedder/encoder_only/base_multi_devices.py10 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.py9 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.py8 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.py8 devices="cuda:0", # if you don't have a GPU, you can use "cpu"CODE
MEDIUM…ference/embedder/encoder_only/auto_m3_single_device.py8 devices="cuda:0", # if you don't have a GPU, you can use "cpu"CODE
MEDIUM…mbedder/encoder_only/m3_multi_devices_compute_score.py8 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.py8 devices="cuda:0", # if you don't have a GPU, you can use "cpu"CODE
MEDIUM…es/inference/embedder/encoder_only/m3_multi_devices.py8 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.py23 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.py23 devices="cuda:0", # if you don't have a GPU, you can use "cpu"CODE
MEDIUM…rence/embedder/decoder_only/auto_base_single_device.py9 devices="cuda:0", # if you don't have a GPU, you can use "cpu"CODE
MEDIUM…/inference/embedder/decoder_only/base_single_device.py10 devices="cuda:0", # if you don't have a GPU, you can use "cpu"CODE
MEDIUM…ence/embedder/decoder_only/pseudo_moe_single_device.py16 devices="cuda:0", # if you don't have a GPU, you can use "cpu"CODE
MEDIUM…/inference/embedder/decoder_only/base_multi_devices.py10 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.py16 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.py9 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.py24 devices="cuda:0", # if you don't have a GPU, you can use "cpu"CODE
MEDIUM…embedder/decoder_only/auto_pseudo_moe_multi_devices.py17 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.py24 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.py17 devices="cuda:0", # if you don't have a GPU, you can use "cpu"CODE
Over-Commented Block48 hits · 48 pts
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LOW…h/llm_reranker/merge/configuration_minicpm_reranker.py1# coding=utf-8COMMENT
LOW…search/llm_reranker/merge/modeling_minicpm_reranker.py1# coding=utf-8COMMENT
LOW…netune_for_layerwise/configuration_minicpm_reranker.py1# coding=utf-8COMMENT
LOW…er/finetune_for_layerwise/modeling_minicpm_reranker.py1# coding=utf-8COMMENT
LOWresearch/BGE_Coder/data_generation/run_generation.py281 # if os.path.exists(save_path) and not overwrite:COMMENT
LOWresearch/BGE_Coder/data_generation/constant.py241 # cs = 'Czech' # 捷克语COMMENT
LOWresearch/Reinforced_IR/finetune/retriever/trainer.py41 # if self.is_world_process_zero():COMMENT
LOW…Reinforced_IR/data_generation/prompts/train_prompts.py21# The query is a user query and should be short.COMMENT
LOWresearch/Long_LLM/activation_beacon/src/metrics.py101 # if labels is None:COMMENT
LOW…arch/Long_LLM/activation_beacon/src/modeling_beacon.py641 self._end_idx = end_idxCOMMENT
LOW…LLM/activation_beacon/src/qwen2/configuration_qwen2.py1# coding=utf-8COMMENT
LOW…Long_LLM/activation_beacon/src/qwen2/modeling_qwen2.py1# coding=utf-8COMMENT
LOW…activation_beacon/src/mistral/configuration_mistral.py1# coding=utf-8COMMENT
LOW…_LLM/activation_beacon/src/mistral/modeling_mistral.py1# coding=utf-8COMMENT
LOW…LLM/activation_beacon/src/llama/configuration_llama.py1# coding=utf-8COMMENT
LOW…Long_LLM/activation_beacon/src/llama/modeling_llama.py1# coding=utf-8COMMENT
LOW…Long_LLM/activation_beacon/src/llama/modeling_llama.py261 # ordinal_hidden_states = hidden_states[:, cur_beacon_indices == 0]COMMENT
LOW…Long_LLM/activation_beacon/src/llama/modeling_llama.py281 # key_states[:, cur_beacon_indices == 0] = ordinal_key_statesCOMMENT
LOW…Long_LLM/activation_beacon/src/llama/modeling_llama.py301COMMENT
LOWresearch/Long_LLM/longllm_qlora/src/metrics.py101 # if labels is None:COMMENT
LOWresearch/llm_embedder/src/lm/modeling_srlm.py481COMMENT
LOWresearch/llm_embedder/src/lm/modeling_srlm.py501 # # concatenate extra contextCOMMENT
LOWresearch/llm_embedder/src/lm/modeling_srlm.py521 # retrieved_chunks, retrieved_indices = self._dense_retrieval(query_embedding, key_embeddings, value_chunks)COMMENT
LOWresearch/llm_embedder/src/lm/modeling_srlm.py541 # fixed_context = torch.cat([fixed_context, outputs.unsqueeze(1)], dim=1) # batch_size, -, chunk_sizeCOMMENT
LOWresearch/llm_embedder/src/retrieval/metrics.py281 # delete pos, neg, and teacher scores because they do not comply with new keysCOMMENT
LOWresearch/Matroyshka_reranker/README.md261### COMMENT
LOW…atroyshka_reranker/finetune/compensation/load_model.py61 # peft_config = LoraConfig(COMMENT
LOW…yshka_reranker/finetune/compensation/mistral_config.py1# coding=utf-8COMMENT
LOW…oyshka_reranker/finetune/compensation/mistral_model.py1# coding=utf-8COMMENT
LOW…_reranker/finetune/self_distillation/mistral_config.py1# coding=utf-8COMMENT
LOW…a_reranker/finetune/self_distillation/mistral_model.py1# coding=utf-8COMMENT
LOW…search/Matroyshka_reranker/inference/mistral_config.py1# coding=utf-8COMMENT
LOWresearch/Matroyshka_reranker/inference/mistral_model.py1# coding=utf-8COMMENT
LOWresearch/visual_bge/visual_bge/eva_clip/transform.py41COMMENT
LOW…/MLVU/evaluation/generation_evaluation/evaluate_ssc.py221 # count += 1COMMENT
LOWresearch/BGE_VL/modeling_MMRet_CLIP.py1# coding=utf-8COMMENT
LOW…coder_only/layerwise/configuration_minicpm_reranker.py1# coding=utf-8COMMENT
LOW…ng/finetune/reranker/decoder_only/layerwise/trainer.py41COMMENT
LOW…er/decoder_only/layerwise/modeling_minicpm_reranker.py1# coding=utf-8COMMENT
LOW…bedding/finetune/reranker/decoder_only/base/trainer.py41COMMENT
LOW…Embedding/finetune/embedder/encoder_only/m3/trainer.py41 # save_ckpt_for_sentence_transformers(output_dir,COMMENT
LOW…bedding/finetune/embedder/encoder_only/base/trainer.py41 # save_ckpt_for_sentence_transformers(output_dir,COMMENT
LOW…mbedding/finetune/embedder/decoder_only/icl/trainer.py41 # if self.is_world_process_zero():COMMENT
LOW…bedding/finetune/embedder/decoder_only/base/trainer.py41 # if self.is_world_process_zero():COMMENT
LOW…/inference/reranker/decoder_only/models/gemma_model.py1# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨COMMENT
LOW…inference/reranker/decoder_only/models/gemma_config.py1# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨COMMENT
LOW…/decoder_only/models/configuration_minicpm_reranker.py1# coding=utf-8COMMENT
LOW…anker/decoder_only/models/modeling_minicpm_reranker.py1# coding=utf-8COMMENT
Excessive Try-Catch Wrapping40 hits · 42 pts
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LOWresearch/llm_reranker/evaluate.py112 except Exception as e:STRING
LOWresearch/llm_reranker/finetune_for_instruction/data.py32 except Exception as e:CODE
LOWresearch/llm_reranker/finetune_for_layerwise/data.py32 except Exception as e:CODE
LOW…_Coder/evaluation/coderag_eval/test/create/swebench.py162 except Exception as e:CODE
LOW…r/evaluation/coderag_eval/test/create/repoeval_repo.py50 except Exception as e:CODE
LOW…_Coder/evaluation/coderag_eval/test/create/repoeval.py50 except Exception as e:CODE
LOW…r/evaluation/coderag_eval/test/create/swebench_repo.py162 except Exception as e:CODE
LOW…r/evaluation/coderag_eval/test/create/swebench_repo.py223 except Exception as e:CODE
MEDIUM…r/evaluation/coderag_eval/test/create/swebench_repo.py224 print(f"Error processing item {item['instance_id']}: {str(e)}")CODE
LOWresearch/BGE_Coder/data_generation/triplet_generator.py465 except Exception as e:CODE
LOWresearch/BGE_Coder/data_generation/llm.py83 except Exception as e:CODE
MEDIUMresearch/BGE_Coder/data_generation/llm.py62def create_completion(results):CODE
MEDIUMresearch/BGE_Coder/data_generation/run_generation.py311 print(f'Error for loading examples from {examples_path}')CODE
LOWresearch/Reinforced_IR/inference/agent/gpt.py71 except Exception as e:CODE
LOWresearch/Reinforced_IR/inference/agent/gpt.py123 except Exception as e:CODE
LOWresearch/Reinforced_IR/data_generation/agent/gpt.py71 except Exception as e:CODE
LOWresearch/Reinforced_IR/data_generation/agent/gpt.py123 except Exception as e:CODE
LOW…arch/Long_LLM/activation_beacon/main/infbench_utils.py168 except Exception:CODE
LOWresearch/Long_LLM/activation_beacon/src/utils.py48 except Exception as e:CODE
LOWresearch/Long_LLM/longllm_qlora/main/infbench_utils.py168 except Exception:CODE
LOWresearch/Long_LLM/longllm_qlora/src/utils.py48 except Exception as e:CODE
LOWresearch/llm_embedder/src/utils/util.py43 except Exception as e:CODE
LOW…arch/Matroyshka_reranker/finetune/compensation/data.py45 except Exception as e:CODE
LOW…Matroyshka_reranker/finetune/self_distillation/data.py45 except Exception as e:CODE
LOWresearch/Matroyshka_reranker/inference/rank_model.py123 except Exception as e:CODE
LOWresearch/visual_bge/visual_bge/eva_clip/timm_model.py116 except Exception as e:CODE
MEDIUMresearch/visual_bge/visual_bge/eva_clip/timm_model.py113def set_grad_checkpointing(self, enable=True):CODE
LOW…/MLVU/evaluation/generation_evaluation/evaluate_ssc.py108 except Exception as e:STRING
MEDIUM…/MLVU/evaluation/generation_evaluation/evaluate_ssc.py109 print(f"Error processing file '{key}': {e}")STRING
LOW…/MLVU/evaluation/generation_evaluation/evaluate_ssc.py191 except Exception as e:STRING
MEDIUM…/MLVU/evaluation/generation_evaluation/evaluate_ssc.py192 print(f"Error: {e}")STRING
LOW…U/evaluation/generation_evaluation/evaluate_summary.py98 except Exception as e:STRING
MEDIUM…U/evaluation/generation_evaluation/evaluate_summary.py99 print(f"Error processing file '{key}': {e}")STRING
LOW…U/evaluation/generation_evaluation/evaluate_summary.py181 except Exception as e:STRING
MEDIUM…U/evaluation/generation_evaluation/evaluate_summary.py182 print(f"Error: {e}")STRING
LOWFlagEmbedding/abc/inference/AbsReranker.py17except Exception:CODE
LOWFlagEmbedding/abc/inference/AbsEmbedder.py18except Exception:CODE
LOWFlagEmbedding/utils/transformers_compat.py15 except Exception:CODE
LOWFlagEmbedding/utils/transformers_compat.py27 except Exception:CODE
MEDIUMFlagEmbedding/utils/transformers_compat.py10def is_torch_fx_available():CODE
Decorative Section Separators13 hits · 37 pts
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MEDIUMresearch/Long_LLM/activation_beacon/src/args.py260 # ==============================COMMENT
MEDIUMresearch/Long_LLM/activation_beacon/src/args.py262 # ==============================COMMENT
MEDIUMresearch/Long_LLM/activation_beacon/src/args.py293 # ==============================COMMENT
MEDIUMresearch/Long_LLM/activation_beacon/src/args.py295 # ==============================COMMENT
MEDIUMresearch/Long_LLM/longllm_qlora/src/args.py243 # ==============================COMMENT
MEDIUMresearch/Long_LLM/longllm_qlora/src/args.py245 # ==============================COMMENT
MEDIUMresearch/Long_LLM/longllm_qlora/src/args.py259 # ==============================COMMENT
MEDIUMresearch/Long_LLM/longllm_qlora/src/args.py261 # ==============================COMMENT
MEDIUMresearch/Long_LLM/longllm_qlora/src/args.py292 # ==============================COMMENT
MEDIUMresearch/Long_LLM/longllm_qlora/src/args.py294 # ==============================COMMENT
MEDIUM…search/visual_bge/visual_bge/eva_clip/eva_vit_model.py1# --------------------------------------------------------COMMENT
MEDIUM…search/visual_bge/visual_bge/eva_clip/eva_vit_model.py3# --------------------------------------------------------COMMENT
MEDIUMresearch/BGE_VL/modeling_MMRet_CLIP.py915 # ------------------------------------------------------------STRING
AI Slop Vocabulary10 hits · 22 pts
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LOW…arch/Long_LLM/activation_beacon/main/eval_multiturn.py147 # NOTE: unwrap because we just use the model for evaluationCOMMENT
LOWresearch/Long_LLM/activation_beacon/main/eval_lm.py63 # stride=0 indicates we just use one forward pass with max_length for each textCOMMENT
LOWresearch/Long_LLM/longllm_qlora/main/eval_generation.py89 # NOTE: unwrap because we just use the model for evaluationCOMMENT
LOWresearch/Long_LLM/longllm_qlora/main/eval_lm.py63 # stride=0 indicates we just use one forward pass with max_length for each textCOMMENT
MEDIUMresearch/llm_embedder/data/toy/convsearch.json4{"query": "How did Van Halen reunite with David Lee Roth? David Lee Roth called Eddie Van Halen to discuss what tracks wCODE
MEDIUMresearch/llm_embedder/data/toy/lrlm.json5{"query_id": 24236, "query": "that understand them.\n\nAnd as we'll discuss in greater length in the chapters to follow,CODE
MEDIUMresearch/llm_embedder/data/toy/lrlm.json5{"query_id": 24236, "query": "that understand them.\n\nAnd as we'll discuss in greater length in the chapters to follow,CODE
MEDIUMresearch/llm_embedder/data/toy/lrlm.json5{"query_id": 24236, "query": "that understand them.\n\nAnd as we'll discuss in greater length in the chapters to follow,CODE
LOWresearch/llm_embedder/data/toy/tool.json8{"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
MEDIUMresearch/visual_bge/visual_bge/eva_clip/tokenizer.py196 # adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuanceCOMMENT
Modern AI Meta-Vocabulary8 hits · 21 pts
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MEDIUMresearch/BGE_Coder/data_generation/run_generation.py309 min(30, len(examples_pool))) # sample 30 examples for few-shot generatioCODE
MEDIUMresearch/Long_LLM/activation_beacon/src/chat.py269 # The number of few shot examplesCOMMENT
MEDIUMresearch/Long_LLM/longllm_qlora/src/chat.py236 # The number of few shot examplesCOMMENT
MEDIUMresearch/llm_embedder/docs/fine-tune.md65 "key": Optional[List[str]], # Retrieval results of the query. Usually used for RAG or reranking.CODE
MEDIUMresearch/llm_embedder/evaluation/eval_popqa.py257 # popqa extracts few-shot examples from test dataCOMMENT
MEDIUMresearch/llm_embedder/data/toy/convsearch.json7{"query": "What is MMM? MMM is the debut mixtape by Puff Daddy, originally released on November 4, 2015 as a free mixtapCODE
MEDIUMresearch/C_MTEB/README.md44### Evaluate embedding modelCOMMENT
MEDIUMTutorials/README.md66## [RAG](./6_RAG/)COMMENT
Self-Referential Comments8 hits · 20 pts
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MEDIUM…search/Long_LLM/activation_beacon/main/eval_passkey.py240 # Create the heatmap with better aestheticsCOMMENT
MEDIUMresearch/Long_LLM/activation_beacon/main/eval_needle.py369 # Create the heatmap with better aestheticsCOMMENT
MEDIUMresearch/Long_LLM/longllm_qlora/main/eval_passkey.py249 # Create the heatmap with better aestheticsCOMMENT
MEDIUMresearch/Long_LLM/longllm_qlora/main/eval_needle.py373 # Create the heatmap with better aestheticsCOMMENT
MEDIUM…/MLVU/evaluation/generation_evaluation/evaluate_ssc.py134 # Create a new sample with the modified keySTRING
MEDIUM…rch/MLVU/evaluation/generation_evaluation/calculate.py9 # Define the keys to locate in the textCOMMENT
MEDIUM…MLVU/evaluation/generation_evaluation/calculate_sum.py8 # Define the keys to locate in the textCOMMENT
MEDIUM…U/evaluation/generation_evaluation/evaluate_summary.py124 # Create a new sample with the modified keySTRING
Hallucination Indicators2 hits · 20 pts
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CRITICAL…search/MLVU/evaluation/models/videochat2/open_bench.py109 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.py109 seg_embs = [model.llama_model.base_model.model.model.embed_tokens(seg_t) for seg_t in seg_tokens]CODE
AI Response Leakage1 hit · 8 pts
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HIGHresearch/llm_embedder/data/toy/lrlm.json7{"query_id": 11209, "query": "Because the meta element provides additional information about your web page, you have to CODE
Synthetic Comment Markers1 hit · 5 pts
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HIGHresearch/Long_LLM/longllm_qlora/README.md9We extend the context length of Llama-3-8B-Instruct from 8K to 80K via QLoRA fine-tuning. The entire training cycle is sCODE
Magic Placeholder Names1 hit · 5 pts
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HIGH…search/Long_LLM/longllm_qlora/data_pipeline/_openai.py469 parser.add_argument("--api_key", default="YOUR_API_KEY")CODE
Redundant / Tautological Comments2 hits · 4 pts
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LOWtests/conftest.py11# Check if we're using transformers v5+COMMENT
LOWtests/test_imports_v5.py15# Check if we're using transformers v5+COMMENT