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Functions1,082 in github.com/cxcscmu/AutoGEO

↓ 1 callersFunction_get_omni_inputs
(processor: "ProcessorMixin")
LLaMA-Factory/tests/data/test_mm_plugin.py:91
↓ 1 callersFunction_get_openai_client
Get or create OpenAI client. Returns: OpenAI client instance Raises: ValueError: If OPENAI_API_KEY not found in
autogeo/utils/openai.py:17
↓ 1 callersFunction_get_package_version
(name: str)
LLaMA-Factory/src/llamafactory/extras/packages.py:34
↓ 1 callersFunction_get_paligemma_token_type_ids
r"""Get paligemma token type ids for computing loss. It is slightly different with the original token type ids where the prompt part is 0. R
LLaMA-Factory/src/llamafactory/data/mm_plugin.py:93
↓ 1 callersFunction_get_quantization_dataset
r"""Prepare the tokenized dataset to perform AutoGPTQ. Do not use tensor output for JSON serialization.
LLaMA-Factory/src/llamafactory/model/model_utils/quantization.py:43
↓ 1 callersMethod_get_run_script
Get the run script content.
open-r1/src/open_r1/utils/competitive_programming/morph_client.py:485
↓ 1 callersFunction_hierarchical_merge
Hierarchically merge rules by splitting into chunks if needed. Args: rules_list: List of rules to merge llm_args: Diction
autogeo/rules/merger.py:151
↓ 1 callersFunction_load_single_dataset
r"""Load a single dataset and aligns it to the standard format.
LLaMA-Factory/src/llamafactory/data/loader.py:51
↓ 1 callersFunction_make_batched_images
r"""Make nested list of images.
LLaMA-Factory/src/llamafactory/data/mm_plugin.py:128
↓ 1 callersFunction_parse_eval_args
(args: Optional[Union[dict[str, Any], list[str]]] = None)
LLaMA-Factory/src/llamafactory/hparams/parser.py:195
↓ 1 callersFunction_parse_infer_args
(args: Optional[Union[dict[str, Any], list[str]]] = None)
LLaMA-Factory/src/llamafactory/hparams/parser.py:189
↓ 1 callersFunction_parse_train_args
(args: Optional[Union[dict[str, Any], list[str]]] = None)
LLaMA-Factory/src/llamafactory/hparams/parser.py:183
↓ 1 callersMethod_prepare_files
Process files, determine problem ID, and prepare configuration. Args: data: Dictionary containing file information
open-r1/src/open_r1/utils/competitive_programming/morph_client.py:82
↓ 1 callersFunction_read_json_with_fs
r"""Helper function to read JSON/JSONL files using fsspec.
LLaMA-Factory/src/llamafactory/data/data_utils.py:161
↓ 1 callersMethod_regularize_videos
( self, videos: list["VideoInput"], **kwargs )
LLaMA-Factory/src/llamafactory/data/mm_plugin.py:1388
↓ 1 callersMethod_run_async
(self, scripts: List[str], languages: List[str], num_parallel: int)
open-r1/src/open_r1/utils/code_providers.py:125
↓ 1 callersMethod_run_async
Run multiple scripts concurrently with limited parallelism. Args: scripts: List of scripts to execute language: Progr
open-r1/src/open_r1/utils/code_providers.py:253
↓ 1 callersMethod_run_async_from_sync
Function wrapping the `_run_async` function.
open-r1/src/open_r1/utils/code_providers.py:115
↓ 1 callersMethod_run_script
(self, script: str, languages: List[str], semaphore: asyncio.Semaphore)
open-r1/src/open_r1/utils/code_providers.py:135
↓ 1 callersMethod_run_script
Execute a single script in a MorphCloud Sandbox. Args: script: The script to execute language: Programming language
open-r1/src/open_r1/utils/code_providers.py:273
↓ 1 callersMethod_run_tests
Run tests and evaluate results. Args: instance: The MorphCloud instance data: Dictionary containing runtime
open-r1/src/open_r1/utils/competitive_programming/morph_client.py:186
↓ 1 callersMethod_save_results
(self, category_corrects: dict[str, "NDArray"], results: dict[str, dict[int, str]])
LLaMA-Factory/src/llamafactory/eval/evaluator.py:139
↓ 1 callersFunction_setup_freeze_tuning
( model: "PreTrainedModel", finetuning_args: "FinetuningArguments", is_trainable: bool, cast_t
LLaMA-Factory/src/llamafactory/model/adapter.py:57
↓ 1 callersFunction_setup_full_tuning
( model: "PreTrainedModel", finetuning_args: "FinetuningArguments", is_trainable: bool, cast_t
LLaMA-Factory/src/llamafactory/model/adapter.py:38
↓ 1 callersFunction_setup_lora_tuning
( config: "PretrainedConfig", model: "PreTrainedModel", model_args: "ModelArguments", finetuni
LLaMA-Factory/src/llamafactory/model/adapter.py:141
↓ 1 callersMethod_sorted_checkpoints
(self, use_mtime=False, output_dir=None)
open-r1/src/grpo.py:121
↓ 1 callersMethod_stream_chat
( model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer", processor: Optional["Pro
LLaMA-Factory/src/llamafactory/chat/hf_engine.py:267
↓ 1 callersFunction_training_function
(config: dict[str, Any])
LLaMA-Factory/src/llamafactory/train/tuner.py:52
↓ 1 callersMethod_upload_files
Upload all necessary files to the instance. Args: instance: The MorphCloud instance local_files: Dictionary
open-r1/src/open_r1/utils/competitive_programming/morph_client.py:139
↓ 1 callersMethod_wait_for_endpoint
(self)
open-r1/src/open_r1/utils/competitive_programming/piston_client.py:94
↓ 1 callersFunctionabort_process
r"""Abort the processes recursively in a bottom-up way.
LLaMA-Factory/src/llamafactory/webui/common.py:46
↓ 1 callersFunctionadd_includes
Fix common compilation errors for IOI problems.
open-r1/src/open_r1/utils/competitive_programming/ioi_utils.py:7
↓ 1 callersMethodadd_thought
r"""Add empty thought to assistant message.
LLaMA-Factory/src/llamafactory/data/template.py:97
↓ 1 callersFunctionadd_z3_leaf_module
r"""Set module as a leaf module to skip partitioning in deepspeed zero3.
LLaMA-Factory/src/llamafactory/model/model_utils/moe.py:36
↓ 1 callersFunctionalign_dataset
r"""Align the dataset to a specific format. Aligned dataset: _prompt: [{"role": "user", "content": "..."}] * (2T - 1) _response: [{"role"
LLaMA-Factory/src/llamafactory/data/converter.py:252
↓ 1 callersFunctionapi_rewrite_documents
Rewrite documents using AutoGEO API. Args: num_examples: Number of examples to process data_dir: Directory containing data ch
autogeo/rewriters/api.py:72
↓ 1 callersMethodapply
(self, **kwargs)
LLaMA-Factory/src/llamafactory/data/formatter.py:57
↓ 1 callersMethodapply
(self, **kwargs)
LLaMA-Factory/src/llamafactory/data/formatter.py:73
↓ 1 callersFunctionapply_liger_kernel
( config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool, require_logits
LLaMA-Factory/src/llamafactory/model/model_utils/liger_kernel.py:30
↓ 1 callersFunctionautocast_projector_dtype
r"""Cast projector output to half precision for fine-tuning quantized VLMs.
LLaMA-Factory/src/llamafactory/model/model_utils/visual.py:129
↓ 1 callersMethodbatch_inference
(self, batch_input: dict[str, "torch.Tensor"])
LLaMA-Factory/src/llamafactory/eval/evaluator.py:72
↓ 1 callersFunctionbuild_distilabel_pipeline
( model: str, base_url: str = "http://localhost:8000/v1", prompt_column: Optional[str] = None,
open-r1/src/open_r1/generate.py:23
↓ 1 callersFunctionbuild_ngram_lookup
Build ngram lookup for documents.
open-r1/scripts/decontaminate.py:51
↓ 1 callersFunctionbuild_ngram_single
(document: str, ngram_size: int = 8)
open-r1/scripts/decontaminate.py:64
↓ 1 callersFunctioncalculate_citation_quality
Calculate citation quality metrics (precision and recall). Args: response: Generated answer text with citations documents: Li
autogeo/evaluation/metrics/geu_score.py:329
↓ 1 callersFunctioncalculate_keypoint_coverage
Calculate keypoint coverage metrics (KPR and KPC). Args: keypoint_list: List of keypoint dictionaries with point_number and point_con
autogeo/evaluation/metrics/geu_score.py:413
↓ 1 callersFunctioncalculate_quality_dimensions
Calculate quality dimension scores for a response. Args: query: User query text response: Generated answer text
autogeo/evaluation/metrics/geu_score.py:384
↓ 1 callersFunctioncalculate_rule_following_score
(text: str, client: OpenAI)
open-r1/src/open_r1/rewards.py:226
↓ 1 callersFunctioncall_claude
Call Anthropic (Claude) API with retry logic. Args: user_prompt: User prompt text model_name: Claude model name (default: "cl
autogeo/utils/anthropic.py:35
↓ 1 callersFunctioncall_hf_model
Call HuggingFace model for inference. Args: user_prompt: User prompt text model_path: Path to HuggingFace model (can be local
autogeo/utils/hf_model.py:14
↓ 1 callersFunctioncall_openai
Call OpenAI API with retry logic. Args: user_prompt: User prompt text model_name: OpenAI model name (default: "gpt-4o")
autogeo/utils/openai.py:35
↓ 1 callersFunctioncheck_dependencies
r"""Check the version of the required packages.
LLaMA-Factory/src/llamafactory/extras/misc.py:95
↓ 1 callersFunctionclean_rewritten_text_with_gemini
Clean rewritten text by removing introductory phrases. Args: text_to_clean: Text that may contain introductory phrases R
autogeo/loader/cold_start_data.py:69
↓ 1 callersFunctioncleanup
(dataset: Dataset)
open-r1/scripts/decontaminate.py:127
↓ 1 callersFunctioncompute_device_flops
r"""Calculate the FLOPs of the device capability per second.
LLaMA-Factory/scripts/stat_utils/cal_mfu.py:86
↓ 1 callersFunctioncompute_model_flops
r"""Calculate the FLOPs of model per forward/backward pass.
LLaMA-Factory/scripts/stat_utils/cal_mfu.py:29
↓ 1 callersMethodcompute_preference_loss
r"""Compute loss for preference learning.
LLaMA-Factory/src/llamafactory/train/dpo/trainer.py:154
↓ 1 callersMethodcompute_reference_log_probs
r"""Compute log probabilities of the reference model.
LLaMA-Factory/src/llamafactory/train/kto/trainer.py:187
↓ 1 callersMethodcompute_reference_log_probs
r"""Compute log probabilities of the reference model.
LLaMA-Factory/src/llamafactory/train/dpo/trainer.py:208
↓ 1 callersFunctionconfigure_attn_implementation
(config: "PretrainedConfig", model_args: "ModelArguments")
LLaMA-Factory/src/llamafactory/model/model_utils/attention.py:32
↓ 1 callersFunctionconfigure_kv_cache
(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool)
LLaMA-Factory/src/llamafactory/model/model_utils/kv_cache.py:29
↓ 1 callersFunctionconfigure_longlora
(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool)
LLaMA-Factory/src/llamafactory/model/model_utils/longlora.py:359
↓ 1 callersFunctionconfigure_moe
(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool)
LLaMA-Factory/src/llamafactory/model/model_utils/moe.py:101
↓ 1 callersFunctionconfigure_packing
(model_args: "ModelArguments", is_trainable: bool)
LLaMA-Factory/src/llamafactory/model/model_utils/packing.py:110
↓ 1 callersFunctionconfigure_quantization
r"""Priority: PTQ-quantized (train/infer) > AutoGPTQ (export) > On-the-fly quantization (train/infer).
LLaMA-Factory/src/llamafactory/model/model_utils/quantization.py:82
↓ 1 callersFunctionconfigure_rope
(config: "PretrainedConfig", model_args: "ModelArguments")
LLaMA-Factory/src/llamafactory/model/model_utils/rope.py:35
↓ 1 callersFunctionconfigure_visual_model
r"""Patch VLMs before loading them.
LLaMA-Factory/src/llamafactory/model/model_utils/visual.py:148
↓ 1 callersFunctionconvert_pretrained_model_to_mod
( model: "PreTrainedModel", config: "PretrainedConfig", model_args: "ModelArguments" )
LLaMA-Factory/src/llamafactory/model/model_utils/mod.py:32
↓ 1 callersFunctioncount_tags
(text: str)
open-r1/src/open_r1/rewards.py:470
↓ 1 callersFunctioncreate_app
Creates and configures a FastAPI application instance. Args: args: An object containing configuration parameters for the application.
open-r1/scripts/e2b_router.py:66
↓ 1 callersFunctioncreate_app
Creates and configures a FastAPI application instance for the MorphCloud router. Args: args: An object containing configuration
open-r1/scripts/morph_router.py:59
↓ 1 callersFunctioncreate_batch_prompt_for_rules
(simplified_rules: list, text: str)
open-r1/src/open_r1/rewards.py:183
↓ 1 callersFunctioncreate_batch_prompt_judge
(simplified_key_points: list, answer: str)
open-r1/src/open_r1/rewards.py:88
↓ 1 callersFunctioncreate_chat_completion_response
( request: "ChatCompletionRequest", chat_model: "ChatModel" )
LLaMA-Factory/src/llamafactory/api/chat.py:184
↓ 1 callersFunctioncreate_ds_config
r"""Create deepspeed config in the current directory.
LLaMA-Factory/src/llamafactory/webui/common.py:223
↓ 1 callersFunctioncreate_eval_tab
(engine: "Engine")
LLaMA-Factory/src/llamafactory/webui/components/eval.py:33
↓ 1 callersFunctioncreate_export_tab
(engine: "Engine")
LLaMA-Factory/src/llamafactory/webui/components/export.py:118
↓ 1 callersFunctioncreate_infer_tab
(engine: "Engine")
LLaMA-Factory/src/llamafactory/webui/components/infer.py:32
↓ 1 callersMethodcreate_optimizer
( self, model: "AutoModelForCausalLMWithValueHead", training_args: "Seq2SeqTrainingArg
LLaMA-Factory/src/llamafactory/train/ppo/trainer.py:298
↓ 1 callersFunctioncreate_reward_model
r"""Create reward model for PPO training.
LLaMA-Factory/src/llamafactory/train/trainer_utils.py:146
↓ 1 callersMethodcreate_scheduler
( self, training_args: "Seq2SeqTrainingArguments", num_training_steps: int, optimizer: "torch.optim.Op
LLaMA-Factory/src/llamafactory/train/ppo/trainer.py:325
↓ 1 callersFunctioncreate_score_evaluation_response
( request: "ScoreEvaluationRequest", chat_model: "ChatModel" )
LLaMA-Factory/src/llamafactory/api/chat.py:277
↓ 1 callersFunctioncreate_stream_chat_completion_response
( request: "ChatCompletionRequest", chat_model: "ChatModel" )
LLaMA-Factory/src/llamafactory/api/chat.py:238
↓ 1 callersFunctioncreate_top
()
LLaMA-Factory/src/llamafactory/webui/components/top.py:32
↓ 1 callersFunctioncreate_train_tab
(engine: "Engine")
LLaMA-Factory/src/llamafactory/webui/components/train.py:37
↓ 1 callersFunctioncreate_web_demo
()
LLaMA-Factory/src/llamafactory/webui/interface.py:73
↓ 1 callersFunctiondownload_github_folder
Download JSON files from a GitHub repository folder. Args: repo_owner: GitHub repository owner username repo_name: GitHub
autogeo/loader/github_loader.py:7
↓ 1 callersFunctiondump_layernorm
r"""Dump the layernorm parameters in the model. The model is already unwrapped (and gathered).
LLaMA-Factory/src/llamafactory/train/ppo/ppo_utils.py:65
↓ 1 callersFunctionecn
(sentence)
autogeo/evaluation/metrics/geo_score.py:26
↓ 1 callersFunctionecn
(sentence)
open-r1/src/open_r1/GEO/evaluation_metrics.py:14
↓ 1 callersFunctionevaluate_example
Evaluate a single example to extract rules. Args: example: Example dictionary with query, documents, and winner llm_args: Dic
autogeo/extract_rules.py:31
↓ 1 callersFunctionevaluate_ge_utility
( data: dict, question_id_list: list[str], metrics_to_run: List[str], max_workers: int = 5,
autogeo/evaluation/metrics/geu_score.py:523
↓ 1 callersFunctionexecute_ioi
Requests to the IOI package return the score as a float in the stdout, as well as optional feedback/errors in stderr. Returns a tuple of (sco
open-r1/src/open_r1/utils/competitive_programming/ioi_scoring.py:302
↓ 1 callersMethodexecute_scripts
Execute scripts using E2B sandboxes. If e2b_router_url is provided, uses the RoutedSandbox for batch processing. Otherwise, uses dire
open-r1/src/open_r1/utils/code_providers.py:82
↓ 1 callersMethodextract
r"""Extract a list of tuples from the response message if using tools. Each tuple consists of function name and function arguments.
LLaMA-Factory/src/llamafactory/data/formatter.py:37
↓ 1 callersFunctionextract_citations_new
(text)
open-r1/src/open_r1/GEO/evaluation_metrics.py:13
↓ 1 callersFunctionextract_formal_text
(raw: str)
open-r1/src/open_r1/rewards.py:78
↓ 1 callersFunctionfilter_records
Filter records that meet quality thresholds. Args: data: Dictionary of records to filter rewrite_method_name: Name of the rew
autogeo/loader/cold_start_data.py:25
↓ 1 callersFunctionfind_available_port
r"""Find an available port on the local machine.
LLaMA-Factory/src/llamafactory/extras/misc.py:295
↓ 1 callersFunctionfind_diff
(short_str: str, long_str: str)
LLaMA-Factory/src/llamafactory/data/template.py:533
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