MCPcopy Create free account

hub / github.com/OpenBMB/AgentCPM-GUI / types & classes

Types & classes49 in github.com/OpenBMB/AgentCPM-GUI

↓ 4 callersClassMobileUse
eval/utils/utils_qwen/agent_function_call.py:7
↓ 3 callersClassRMSNorm
eval/utils/utils_odyssey/modeling_qwen.py:1334
↓ 2 callersClassStopWordsLogitsProcessor
:class:`transformers.LogitsProcessor` that enforces that when specified sequences appear, stop geration. Args: stop_words_ids (:
eval/utils/utils_odyssey/qwen_generation_utils.py:305
↓ 1 callersClassActionEvaluator
eval/utils/evaluator.py:152
↓ 1 callersClassActionType
Integer values for each supported action type in AndroidInTheWild.
eval/utils/action_type.py:22
↓ 1 callersClassAsyncRLGRPOTrainer
Trainer for the Group Relative Policy Optimization (GRPO) method. This algorithm was initially proposed in the paper [DeepSeekMath: Pushing t
rft/trainer/arl.py:70
↓ 1 callersClassCPMTrainer
sft/trainer.py:13
↓ 1 callersClassChineseDataset
eval/grounding_eval/code/Intern2.5-VL/evaluate_grounding_fun2bbox.py:42
↓ 1 callersClassChineseDataset
eval/grounding_eval/code/Intern2.5-VL/evaluate_grounding_bbox2text.py:42
↓ 1 callersClassChineseDataset
eval/grounding_eval/code/Intern2.5-VL/evaluate_grounding_text2bbox.py:42
↓ 1 callersClassEvalDataset
eval/run_eval_agent.py:23
↓ 1 callersClassGlobalDistributed0MQDataLoader
rft/trainer/utils/dataloader.py:11
↓ 1 callersClassGlobalSyncManager
管理全局任务同步信号并分配优势值。
rft/trainer/zmq.py:52
↓ 1 callersClassHisResampler
eval/utils/utils_odyssey/modeling_qwen.py:183
↓ 1 callersClassInferenceSampler
eval/grounding_eval/code/Intern2.5-VL/evaluate_grounding_fun2bbox.py:90
↓ 1 callersClassInferenceSampler
eval/grounding_eval/code/Intern2.5-VL/evaluate_grounding_bbox2text.py:89
↓ 1 callersClassInferenceSampler
eval/grounding_eval/code/Intern2.5-VL/evaluate_grounding_text2bbox.py:85
↓ 1 callersClassLocalBalanceManager
平衡本地机器创建的数据和任务,并与全局同步。
rft/trainer/zmq.py:247
↓ 1 callersClassQWenAttention
eval/utils/utils_odyssey/modeling_qwen.py:249
↓ 1 callersClassQWenBlock
eval/utils/utils_odyssey/modeling_qwen.py:480
↓ 1 callersClassQWenConfig
eval/utils/utils_odyssey/configuration_qwen.py:9
↓ 1 callersClassQWenLMHeadModel
eval/utils/utils_odyssey/modeling_qwen.py:929
↓ 1 callersClassQWenMLP
eval/utils/utils_odyssey/modeling_qwen.py:461
↓ 1 callersClassQWenModel
eval/utils/utils_odyssey/modeling_qwen.py:582
↓ 1 callersClassResampler
A 2D perceiver-resampler network with one cross attention layers by (grid_size**2) learnable queries and 2d sincos pos_emb Outputs
eval/utils/utils_odyssey/visual.py:92
↓ 1 callersClassRotaryEmbedding
eval/utils/utils_odyssey/modeling_qwen.py:1265
↓ 1 callersClassSyncAdvantagesRequest
rft/trainer/zmq.py:33
↓ 1 callersClassTaskAndContent
rft/trainer/zmq.py:28
↓ 1 callersClassTaskStatus
rft/trainer/zmq.py:20
↓ 1 callersClassTransformerBlock
eval/utils/utils_odyssey/visual.py:300
↓ 1 callersClassVisImage
eval/utils/utils_odyssey/tokenization_qwen.py:495
↓ 1 callersClassVisionTransformer
eval/utils/utils_odyssey/visual.py:332
↓ 1 callersClassVisualAttention
self-attention layer class. Self-attention layer takes input with size [s, b, h] and returns output of the same size.
eval/utils/utils_odyssey/visual.py:159
↓ 1 callersClassVisualAttentionBlock
eval/utils/utils_odyssey/visual.py:247
↓ 1 callersClassVisualizer
eval/utils/utils_odyssey/tokenization_qwen.py:537
↓ 1 callersClass_IterWithLen
rft/trainer/arl.py:584
↓ 1 callersClasswrappeddict
rft/trainer/arl.py:543
ClassComputerUse
eval/utils/utils_qwen/agent_function_call.py:147
ClassDataArguments
sft/finetune.py:32
ClassGRPOScriptArguments
Script arguments for the GRPO training script. Args: reward_funcs (`list[str]`): List of reward functions. Possible valu
rft/configs.py:22
ClassGRPOTrainingConfig
r""" Configuration class for the [`GRPOTrainer`]. Only the parameters specific to GRPO training are listed here. For details on other paramet
rft/configs.py:46
ClassGUIMTRFTDataset
Multiturn RFT Dataset
rft/trainer/utils/dataset.py:123
ClassGUIRFTDataset
rft/trainer/utils/dataset.py:40
ClassLoraArguments
sft/finetune.py:61
ClassModelArguments
sft/finetune.py:27
ClassQWenPreTrainedModel
eval/utils/utils_odyssey/modeling_qwen.py:544
ClassQWenTokenizer
QWen tokenizer.
eval/utils/utils_odyssey/tokenization_qwen.py:100
ClassSupervisedDataset
Dataset for supervised fine-tuning.
sft/dataset.py:23
ClassTrainingArguments
sft/finetune.py:43