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Functions3,355 in github.com/AFAC2024/AFAC2025

↓ 2 callersFunctionload_test_prediction_dataset
(release_version="release_v1")
方案分享/赛题三/双人成行/code/RL/code_eval/LiveCodeBench/lcb_runner/benchmarks/test_output_prediction.py:62
↓ 2 callersFunctionload_valuehead_params
r"""Load value head parameters from Hugging Face Hub or local disk. Returns: dict with keys `v_head.summary.weight` and `v_head.summary.bias`.
方案分享/赛题三/ljq13317046284/代码/LLaMA-Factory-main/src/llamafactory/model/model_utils/valuehead.py:33
↓ 2 callersMethodlocal_mkdir
(path)
方案分享/赛题三/双人成行/code/RL/verl/verl/utils/checkpoint/checkpoint_manager.py:87
↓ 2 callersMethodlog
(self, data, step)
方案分享/赛题三/双人成行/code/RL/verl/verl/utils/logger/aggregate_logger.py:40
↓ 2 callersFunctionlog_probs_from_logits_all_rmpad
Compute the log_probs from logits with rmpad input_ids and logits. Note that logits_rmpad = model(input_ids_rmpad). For each sentences, there is a
方案分享/赛题三/双人成行/code/RL/verl/verl/utils/torch_functional.py:361
↓ 2 callersFunctionlogprobs_from_logits_naive
(logits, labels)
方案分享/赛题三/双人成行/code/RL/verl/verl/utils/torch_functional.py:71
↓ 2 callersFunctionlogprobs_from_logits_v2
A memory efficient implementation of logprobs_from_logits
方案分享/赛题三/双人成行/code/RL/verl/verl/utils/torch_functional.py:77
↓ 2 callersFunctionmain
使用大语言模型生成基金分析摘要 参数: prompt (str): 输入提示词 返回: str: 模型生成的内容
方案分享/赛题一/allin/代码文件/Data_Acquire/summary_data.py:192
↓ 2 callersFunctionmake_map_fn
(split)
方案分享/赛题三/双人成行/code/RL/verl/examples/data_preprocess/geo3k.py:44
↓ 2 callersFunctionmake_map_fn
(split)
方案分享/赛题三/双人成行/code/RL/verl/examples/data_preprocess/gsm8k.py:51
↓ 2 callersFunctionmake_map_fn
Create a mapping function to process dataset examples. Args: split: Dataset split name ('train' or 'test') Returns: Function
方案分享/赛题三/双人成行/code/RL/verl/examples/data_preprocess/deepscaler_dataset.py:37
↓ 2 callersFunctionmake_map_fn
(split)
方案分享/赛题三/双人成行/code/RL/verl/examples/data_preprocess/math_dataset.py:50
↓ 2 callersMethodmake_minibatch_iterator
Make minibatch iterator for updating the actor Args: data (DataProto): a DataProto containing keys ``input_ids``
方案分享/赛题三/双人成行/code/RL/verl/verl/workers/actor/megatron_actor.py:203
↓ 2 callersFunctionmake_signature
Extracts function signature.
方案分享/赛题三/双人成行/code/RL/code_eval/MBPP_Humaneval/eval/Coding/human_eval/evaluate_human_eval.py:115
↓ 2 callersFunctionmasked_rloo
(reward_tensor_original, mask_tensor)
方案分享/赛题三/双人成行/code/RL/verl/recipe/prime/prime_core_algos.py:22
↓ 2 callersFunctionmath_equal
Exact match of math if and only if: 1. numerical equal: both can convert to float and are equal 2. symbolic equal: both can convert to sy
方案分享/赛题三/双人成行/code/RL/verl/verl/utils/reward_score/prime_math/grader.py:174
↓ 2 callersFunctionmath_equal
Exact match of math if and only if: 1. numerical equal: both can convert to float and are equal 2. symbolic equal: both can convert to sy
方案分享/赛题三/ljq13317046284/代码/TokenSkip-main/TokenSkip-main/eval/eval_utils.py:181
↓ 2 callersFunctionmathd_normalize_answer
(answer: Optional[str])
方案分享/赛题三/双人成行/code/RL/deepscaler/rewards/math_utils/utils.py:14
↓ 2 callersFunctionmd5_encode
(path: str)
方案分享/赛题三/双人成行/code/RL/verl/verl/utils/fs.py:36
↓ 2 callersFunctionnested_detach
r"""Detach `tensors` (even if it's a nested list/tuple/dict of tensors).
方案分享/赛题三/ljq13317046284/代码/LLaMA-Factory-main/src/llamafactory/train/trainer_utils.py:634
↓ 2 callersFunctionnormalize
(answer, pi)
方案分享/赛题三/双人成行/code/RL/verl/verl/utils/reward_score/prime_math/grader.py:119
↓ 2 callersFunctionnormalize
(answer, pi)
方案分享/赛题三/双人成行/code/RL/code_eval/MBPP_Humaneval/eval/utils/grader.py:109
↓ 2 callersFunctionnormalize_choices
提取并标准化选择题答案中的字母
方案分享/赛题三/双人成行/code/RL/verl/verl/workers/reward_manager/naive.py:303
↓ 2 callersFunctionnormalize_pp_vpp_params
Normalize the pp vpp params into a complete named parameters. This is useful when gather parameters from pp ranks and passed to a model witho
方案分享/赛题三/双人成行/code/RL/verl/verl/utils/model.py:198
↓ 2 callersFunctionnumber_split_2len
(num)
方案分享/赛题四/队伍名字不能为空/docker_image/app/mcps/common/chart_utils.py:101
↓ 2 callersMethodoffload_params_to_cpu
(self)
方案分享/赛题三/双人成行/code/RL/verl/verl/workers/reward_model/megatron/reward_model.py:254
↓ 2 callersFunctionparallel_compute_score_async
(evaluation_func, completions, r
方案分享/赛题三/双人成行/code/RL/verl/verl/workers/reward_manager/prime.py:46
↓ 2 callersFunctionparse_doc
( path_list: list[Path], output_dir, lang="ch", backend="pipeline", me
方案分享/赛题四/天池三轮车/docker_image/docker_image/app/agent_api/easy_demo.py:61
↓ 2 callersFunctionparse_response
(prompt: str, url: str, task: str="toc")
方案分享/赛题四/天池三轮车/docker_image/docker_image/app/agent_api/utils.py:80
↓ 2 callersMethodparse_yaml_response
解析YAML格式的响应
方案分享/赛题四/天池三轮车/docker_image/docker_image/app/data_analysis/utils/llm_helper.py:94
↓ 2 callersFunctionpatch_valuehead_model
()
方案分享/赛题三/ljq13317046284/代码/LLaMA-Factory-main/src/llamafactory/train/test_utils.py:113
↓ 2 callersFunctionprepare_4d_attention_mask
r"""Expand 2d attention mask to 4d attention mask. Expand the attention mask with indices from (batch_size, seq_len) to (batch_size, 1, seq_len,
方案分享/赛题三/ljq13317046284/代码/LLaMA-Factory-main/src/llamafactory/data/collator.py:41
↓ 2 callersFunctionpreprocess
(text)
方案分享/赛题三/双人成行/code/RL/verl/examples/data_preprocess/hellaswag.py:27
↓ 2 callersMethodpreprocess_data
AllGather data from sp region This is because the data is first sharded along the FSDP dimension as we utilize the DP_COMPUTE
方案分享/赛题三/双人成行/code/RL/verl/verl/workers/sharding_manager/fsdp_ulysses.py:57
↓ 2 callersMethodprime_norm
(self, token_level_scores)
方案分享/赛题三/双人成行/code/RL/verl/recipe/prime/prime_dp_rm.py:194
↓ 2 callersFunctionprocess_fn
(example: Dict[str, Any], idx: int)
方案分享/赛题三/双人成行/code/RL/verl/examples/data_preprocess/deepscaler_dataset.py:47
↓ 2 callersMethodprompts_to_outputs
( self, prompts: list[str | list[dict[str, str]]] )
方案分享/赛题三/双人成行/code/RL/code_eval/LiveCodeBench/lcb_runner/runner/base_runner.py:108
↓ 2 callersFunctionread_args
r"""Get arguments from the command line or a config file.
方案分享/赛题三/ljq13317046284/代码/LLaMA-Factory-main/src/llamafactory/hparams/parser.py:56
↓ 2 callersFunctionread_problems
(evalset_file: str = HUMAN_EVAL)
方案分享/赛题三/双人成行/code/RL/code_eval/MBPP_Humaneval/eval/utils/data.py:11
↓ 2 callersMethodregister_agent
Register an agent with the environment
方案分享/赛题四/天池三轮车/docker_image/docker_image/app/src/environment/base.py:33
↓ 2 callersMethodregister_all_tools
Register all tools with the server.
方案分享/赛题四/天池三轮车/docker_image/docker_image/app/src/mcp/server.py:152
↓ 2 callersMethodremove_previous_save_local_path
(self)
方案分享/赛题三/双人成行/code/RL/verl/verl/utils/checkpoint/checkpoint_manager.py:74
↓ 2 callersFunctionrepeat_kv
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, he
方案分享/赛题三/双人成行/code/RL/verl/verl/models/llama/megatron/layers/parallel_attention.py:163
↓ 2 callersFunctionreplace_model
r"""Replace the default/reward modules in the model. The model is already unwrapped.
方案分享/赛题三/ljq13317046284/代码/LLaMA-Factory-main/src/llamafactory/train/ppo/ppo_utils.py:43
↓ 2 callersMethodrun
Run the battle environment with the given research report.
方案分享/赛题四/天池三轮车/docker_image/docker_image/app/src/environment/battle.py:134
↓ 2 callersMethodrun
Execute the agent's main loop asynchronously. Args: request: Optional initial user request to process. Returns:
方案分享/赛题四/天池三轮车/docker_image/docker_image/app/src/agent/base.py:115
↓ 2 callersMethodrun_batch
(self, prompts: list[str | list[dict[str, str]]])
方案分享/赛题三/双人成行/code/RL/code_eval/LiveCodeBench/lcb_runner/runner/base_runner.py:67
↓ 2 callersFunctionrun_feature_extraction
( batch_file_path: str, rules_file_path: str, rule_extractor: RuleExtractorTool, definition_fi
方案分享/赛题二/蛋木/code/feature_extract_agent.py:158
↓ 2 callersFunctionrun_relevance_marking
处理单个batch文件,返回其与所有相关规则的相关性判断列表。
方案分享/赛题二/蛋木/code/relevance_marker_agent.py:105
↓ 2 callersMethodsample_str_prompts
(self)
方案分享/赛题三/双人成行/code/RL/verl/tests/e2e/envs/digit_completion/task.py:91
↓ 2 callersFunctionsave_args
r"""Save the training configuration to config path.
方案分享/赛题三/ljq13317046284/代码/LLaMA-Factory-main/src/llamafactory/webui/common.py:163
↓ 2 callersMethodsave_checkpoint
(self, local_path, hdfs_path=None, global_step=0, remove_previous_ckpt=False)
方案分享/赛题三/双人成行/code/RL/verl/recipe/prime/prime_fsdp_workers.py:323
↓ 2 callersMethodsave_checkpoint
(self, local_path, hdfs_path=None, global_step=0, remove_previous_ckpt=False)
方案分享/赛题三/双人成行/code/RL/verl/verl/workers/fsdp_workers.py:865
↓ 2 callersFunctionsave_compressed_dataset
保存压缩后的数据集
方案分享/赛题三/ljq13317046284/代码/TokenSkip-main/TokenSkip-main/simple_compress_afac.py:18
↓ 2 callersFunctionsave_financial_statements_to_csv
将财务报表保存为CSV文件 Args: financial_statements (Dict): 包含财务报表的字典 stock_code (str): 股票代码,用于文件命名 market (str): 股票市场,"HK"
方案分享/赛题四/天池三轮车/docker_image/docker_image/app/data_analysis/utils/get_financial_statements.py:166
↓ 2 callersMethodsave_html_report
保存HTML报告
方案分享/赛题四/天池三轮车/docker_image/docker_image/app/src/utils/report_manager.py:57
↓ 2 callersMethodsave_to_disk
(self, filepath)
方案分享/赛题三/双人成行/code/RL/verl/verl/protocol.py:224
↓ 2 callersFunctionsave_to_milvus
将数据保存到向量库中,会自动排除重复数据
方案分享/赛题四/队伍名字不能为空/docker_image/app/mcps/tools/store.py:99
↓ 2 callersFunctionsearch
(key_words, type="stock")
方案分享/赛题四/队伍名字不能为空/docker_image/app/mcps/spider/stock_bd.py:148
↓ 2 callersFunctionsearch_stock
(key_words)
方案分享/赛题四/队伍名字不能为空/docker_image/app/mcps/spider/stock_bd.py:193
↓ 2 callersMethodset_state
(self, state)
方案分享/赛题三/双人成行/code/RL/verl/tests/e2e/envs/digit_completion/task.py:64
↓ 2 callersFunctionsetup_fs
r"""Set up a filesystem object based on the path protocol.
方案分享/赛题三/ljq13317046284/代码/LLaMA-Factory-main/src/llamafactory/data/data_utils.py:145
↓ 2 callersFunctionslice_input_tensor
(x: Tensor, dim: int, padding: bool = True, group: ProcessGroup = None)
方案分享/赛题三/双人成行/code/RL/verl/verl/utils/ulysses.py:116
↓ 2 callersFunctionsmooth
r"""EMA implementation according to TensorBoard.
方案分享/赛题三/ljq13317046284/代码/LLaMA-Factory-main/src/llamafactory/extras/ploting.py:34
↓ 2 callersFunctionsort_and_extract_save_results
(scenario: Scenario, save_results: list[dict])
方案分享/赛题三/双人成行/code/RL/code_eval/LiveCodeBench/lcb_runner/runner/scenario_router.py:145
↓ 2 callersMethodspawn
spawn to a dictionary of worker groups, each with a subset of method with prefix.
方案分享/赛题三/双人成行/code/RL/verl/verl/single_controller/ray/base.py:312
↓ 2 callersFunctionsplit_chinese_english
(text)
方案分享/赛题四/队伍名字不能为空/docker_image/app/mcps/spider/data_rmyh.py:27
↓ 2 callersFunctionsplit_tuple
Split the elements in a tuple/interval, while handling well-formatted commas in large numbers
方案分享/赛题三/双人成行/code/RL/verl/verl/utils/reward_score/prime_math/__init__.py:254
↓ 2 callersFunctionsplit_tuple
Split the elements in a tuple/interval, while handling well-formatted commas in large numbers
方案分享/赛题三/双人成行/code/RL/deepscaler/rewards/math_utils/utils.py:368
↓ 2 callersFunctionstrip_string
(string)
方案分享/赛题三/双人成行/code/RL/verl/verl/utils/reward_score/math.py:165
↓ 2 callersFunctionstrip_string
(string)
方案分享/赛题三/双人成行/code/RL/verl/verl/workers/reward_manager/naive.py:455
↓ 2 callersFunctionstripped_string_compare
(s1, s2)
方案分享/赛题三/双人成行/code/RL/verl/verl/utils/reward_score/prime_code/testing_util.py:611
↓ 2 callersFunctionsync_model_parameters_global
(layer)
方案分享/赛题三/双人成行/code/RL/verl/tests/model/test_transformers_ulysses.py:38
↓ 2 callersFunctiontext_to_int
(text)
方案分享/赛题一/allin/代码文件/LLM_Train/rewards.py:9
↓ 2 callersMethodto_dict
(self)
方案分享/赛题三/双人成行/code/RL/code_eval/LiveCodeBench/lcb_runner/lm_styles.py:47
↓ 2 callersFunctionto_float
(val)
方案分享/赛题四/天池三轮车/docker_image/docker_image/app/src/tool/financial_deep_search/get_section_data.py:52
↓ 2 callersMethodto_param
Convert tool to function call format.
方案分享/赛题四/天池三轮车/docker_image/docker_image/app/src/tool/base.py:27
↓ 2 callersMethodtool_message
Create a tool message
方案分享/赛题四/天池三轮车/docker_image/docker_image/app/src/schema.py:127
↓ 2 callersMethodupdate_critic
(self, data: DataProto)
方案分享/赛题三/双人成行/code/RL/verl/verl/workers/fsdp_workers.py:829
↓ 2 callersMethodupdate_options
(self, options: Dict)
方案分享/赛题三/双人成行/code/RL/verl/verl/single_controller/ray/base.py:159
↓ 2 callersMethodupdate_policy
Update the policy with an iterator of DataProto Args: data (DataProto): an iterator over the DataProto that returns by
方案分享/赛题三/双人成行/code/RL/verl/verl/workers/actor/base.py:54
↓ 2 callersMethodupdate_sampling_params
(self, **kwargs)
方案分享/赛题三/双人成行/code/RL/verl/verl/workers/rollout/vllm_rollout/fire_vllm_rollout.py:86
↓ 2 callersMethodvalidation_step
(self, batch: TensorDict)
方案分享/赛题三/双人成行/code/RL/verl/verl/trainer/fsdp_sft_trainer.py:418
↓ 2 callersMethodverify
verify the batch and save as ``acc`` tensor
方案分享/赛题三/双人成行/code/RL/verl/verl/workers/reward_manager/prime.py:94
↓ 2 callersFunctionwmape
(pred:np.ndarray, label:np.ndarray)
方案分享/赛题一/allin/代码文件/Time_Series/codes/utils.py:38
↓ 2 callersFunctionwrite_chunk_to_file
将分块内容写入文件,并放入对应的子文件夹
方案分享/赛题二/蛋木/code/file_chunk_processor.py:63
↓ 2 callersFunctionwrite_jsonl
Writes an iterable of dictionaries to jsonl
方案分享/赛题三/双人成行/code/RL/code_eval/MBPP_Humaneval/eval/utils/data.py:32
↓ 1 callersFunctionAdapter
Adapts the dataset for evaluation using the specified model and parameters. Args: dataset: The input dataset to process
方案分享/赛题三/ljq13317046284/代码/TALE-main/TALE-EP.py:38
↓ 1 callersFunctionAvgLength
(sample_list, gt_list)
方案分享/赛题三/ljq13317046284/代码/TALE-main/TALE-PT.py:234
↓ 1 callersFunctionLLMLingua
Compress the CoT outputs with LLMLingua-2.
方案分享/赛题三/ljq13317046284/代码/TokenSkip-main/TokenSkip-main/LLMLingua.py:62
↓ 1 callersFunctionXiaoxiangdailiProxy
()
方案分享/赛题四/天池三轮车/docker_image/docker_image/app/tools/search/bing_search.py:39
↓ 1 callersFunctionXiaoxiangdailiProxy
()
方案分享/赛题四/天池三轮车/docker_image/docker_image/app/src/tool/search/bing_search.py:39
↓ 1 callersMethod__aenter__
(self)
方案分享/赛题四/天池三轮车/docker_image/docker_image/app/data_analysis/utils/fallback_openai_client.py:224
↓ 1 callersMethod__init__
(self, attn_layers, conv_layers=None, norm_layer=None)
方案分享/赛题一/allin/代码文件/Time_Series/codes/SimpleTM/layers/Transformer_Encoder.py:33
↓ 1 callersMethod__init__
( self, config_name: str = "default", llm_config: Optional[LLMSettings] = None )
方案分享/赛题四/天池三轮车/docker_image/docker_image/app/src/llm.py:183
↓ 1 callersMethod__init__
Initialize the volcengine TTS client. Args: appid: Platform application ID access_token: Access token for au
方案分享/赛题四/天池三轮车/docker_image/docker_image/app/src/tool/tts_tool.py:63
↓ 1 callersMethod__init__
(self, size)
方案分享/赛题三/双人成行/code/RL/verl/tests/ray/test_worker_group_torch.py:31
↓ 1 callersMethod__init__
(self)
方案分享/赛题三/双人成行/code/RL/verl/tests/ray/test_colocated_workers.py:27
↓ 1 callersMethod__init__
(self, numel: int, numel_padded: int, dtype: torch.dtype, source: Optional[torch.Tensor] = None)
方案分享/赛题三/双人成行/code/RL/verl/verl/utils/memory_buffer.py:30
↓ 1 callersMethod__init__
(self, resource_pool: RayResourcePool, ray_cls_with_init: RayClassWithInitAr
方案分享/赛题三/双人成行/code/RL/verl/verl/single_controller/ray/megatron.py:44
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