SYMBOL INDEX (151 symbols across 28 files)

FILE: Evol_Instruct/breadth.py
  function createBreadthPrompt (line 10) | def createBreadthPrompt(instruction):

FILE: Evol_Instruct/depth.py
  function createConstraintsPrompt (line 11) | def createConstraintsPrompt(instruction):
  function createDeepenPrompt (line 17) | def createDeepenPrompt(instruction):
  function createConcretizingPrompt (line 23) | def createConcretizingPrompt(instruction):
  function createReasoningPrompt (line 30) | def createReasoningPrompt(instruction):

FILE: Evol_Instruct/openai_access.py
  function get_oai_completion (line 7) | def get_oai_completion(prompt):
  function call_chatgpt (line 49) | def call_chatgpt(ins):

FILE: WizardCoder/src/humaneval_gen.py
  function generate_prompt (line 22) | def generate_prompt(input):
  function get_model (line 33) | def get_model(
  function main (line 67) | def main():

FILE: WizardCoder/src/humaneval_gen_vllm.py
  function generate_prompt (line 25) | def generate_prompt(input):
  function main (line 37) | def main():

FILE: WizardCoder/src/inference_wizardcoder.py
  function evaluate (line 22) | def evaluate(
  function generate_prompt (line 59) | def generate_prompt(instruction, input=None):
  function main (line 68) | def main(

FILE: WizardCoder/src/mbpp_gen.py
  function read_mbpp (line 23) | def read_mbpp(path):
  function extract_text (line 30) | def extract_text(prompt, remove_lines=True):
  function generate_prompt (line 45) | def generate_prompt(input):
  function get_model (line 55) | def get_model(
  function main (line 89) | def main():

FILE: WizardCoder/src/mbppplus_gen.py
  function generate_prompt (line 24) | def generate_prompt(input):
  function get_model (line 34) | def get_model(
  function main (line 68) | def main():

FILE: WizardCoder/src/mbppplus_gen_vllm.py
  function generate_prompt (line 27) | def generate_prompt(input):
  function get_model (line 37) | def get_model(
  function main (line 71) | def main():

FILE: WizardCoder/src/process_mbpp.py
  function read_mbpp (line 8) | def read_mbpp(path):

FILE: WizardCoder/src/train_wizardcoder.py
  class ModelArguments (line 49) | class ModelArguments:
  class DataArguments (line 54) | class DataArguments:
  class TrainingArguments (line 59) | class TrainingArguments(transformers.TrainingArguments):
  function safe_save_model_for_hf_trainer (line 68) | def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output...
  function smart_tokenizer_and_embedding_resize (line 77) | def smart_tokenizer_and_embedding_resize(
  function _tokenize_fn (line 100) | def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrai...
  function preprocess (line 124) | def preprocess(
  class DataCollatorForSupervisedDataset (line 140) | class DataCollatorForSupervisedDataset(object):
    method __call__ (line 145) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
  function train_tokenize_function (line 159) | def train_tokenize_function(examples, tokenizer):
  function train (line 177) | def train():

FILE: WizardLM/src/infer_wizardlm13b.py
  function main (line 27) | def main(

FILE: WizardLM/src/inference_wizardlm.py
  function evaluate (line 24) | def evaluate(
  function generate_prompt (line 57) | def generate_prompt(instruction, input=None):
  function main (line 64) | def main(

FILE: WizardLM/src/train_freeform.py
  class ModelArguments (line 43) | class ModelArguments:
  class DataArguments (line 48) | class DataArguments:
  class TrainingArguments (line 54) | class TrainingArguments(transformers.TrainingArguments):
  function safe_save_model_for_hf_trainer (line 63) | def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output...
  function smart_tokenizer_and_embedding_resize (line 72) | def smart_tokenizer_and_embedding_resize(
  function _tokenize_fn (line 95) | def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrai...
  function preprocess (line 119) | def preprocess(
  class SupervisedDataset (line 134) | class SupervisedDataset(Dataset):
    method __init__ (line 137) | def __init__(self, data_path: str, tokenizer: transformers.PreTrainedT...
    method __len__ (line 157) | def __len__(self):
    method __getitem__ (line 160) | def __getitem__(self, i) -> Dict[str, torch.Tensor]:
  class SupervisedComplexDataset (line 163) | class SupervisedComplexDataset(Dataset):
    method __init__ (line 166) | def __init__(self, data_path: str, tokenizer: transformers.PreTrainedT...
    method __len__ (line 193) | def __len__(self):
    method __getitem__ (line 196) | def __getitem__(self, i) -> Dict[str, torch.Tensor]:
  class DataCollatorForSupervisedDataset (line 200) | class DataCollatorForSupervisedDataset(object):
    method __call__ (line 205) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
  function make_supervised_data_module (line 218) | def make_supervised_data_module(tokenizer: transformers.PreTrainedTokeni...
  function train (line 228) | def train():

FILE: WizardLM/src/weight_diff_wizard.py
  function make_diff (line 25) | def make_diff(
  function recover (line 73) | def recover(
  function main (line 158) | def main(task, **kwargs):

FILE: WizardMath/inference/MATH_inference.py
  function remove_boxed (line 13) | def remove_boxed(s):
  function process_results (line 22) | def process_results(doc, completion, answer):
  function batch_data (line 41) | def batch_data(data_list, batch_size=1):
  function test_hendrycks_math (line 54) | def test_hendrycks_math(model, data_path, start=0, end=MAX_INT, batch_si...
  function parse_args (line 103) | def parse_args():

FILE: WizardMath/inference/grader.py
  function is_digit (line 14) | def is_digit(s):
  function math_equal (line 21) | def math_equal(prediction: Union[bool, float, str],
  function math_equal_process (line 94) | def math_equal_process(param):
  function symbolic_equal (line 98) | def symbolic_equal(a, b):
  function symbolic_equal_process (line 123) | def symbolic_equal_process(a, b, output_queue):
  function call_with_timeout (line 128) | def call_with_timeout(func, *args, timeout=1, **kwargs):

FILE: WizardMath/inference/gsm8k_inference.py
  function is_number (line 11) | def is_number(s):
  function extract_answer_number (line 25) | def extract_answer_number(completion):
  function batch_data (line 53) | def batch_data(data_list, batch_size=1):
  function gsm8k_test (line 67) | def gsm8k_test(model, data_path, start=0, end=MAX_INT, batch_size=1, ten...
  function parse_args (line 124) | def parse_args():

FILE: WizardMath/inference/util.py
  function last_boxed_only (line 4) | def last_boxed_only(sample):
  function last_boxed_only_string (line 11) | def last_boxed_only_string(string):
  function only_until_first_boxed_from_tokens (line 38) | def only_until_first_boxed_from_tokens(string, tokens):
  function clean_numbers (line 55) | def clean_numbers(sample):
  function _clean_numbers (line 64) | def _clean_numbers(string):
  function fix_fracs (line 96) | def fix_fracs(string):
  function fix_a_slash_b (line 127) | def fix_a_slash_b(string):
  function remove_right_units (line 141) | def remove_right_units(string):
  function fix_sqrt (line 150) | def fix_sqrt(string):
  function strip_string (line 165) | def strip_string(string):
  function is_equiv (line 230) | def is_equiv(str1, str2, verbose=False):
  class NotEqual (line 251) | class NotEqual:
    method __eq__ (line 252) | def __eq__(self, other):

FILE: WizardMath/train/train_wizardmath.py
  class ModelArguments (line 49) | class ModelArguments:
  class DataArguments (line 54) | class DataArguments:
  class TrainingArguments (line 59) | class TrainingArguments(transformers.TrainingArguments):
  function safe_save_model_for_hf_trainer (line 68) | def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output...
  function smart_tokenizer_and_embedding_resize (line 77) | def smart_tokenizer_and_embedding_resize(
  function _tokenize_fn (line 100) | def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrai...
  function preprocess (line 124) | def preprocess(
  class DataCollatorForSupervisedDataset (line 140) | class DataCollatorForSupervisedDataset(object):
    method __call__ (line 145) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
  function train_tokenize_function (line 159) | def train_tokenize_function(examples, tokenizer):
  function train (line 176) | def train():

FILE: demo/wizardLM_demo.py
  function parse_args (line 8) | def parse_args():
  function predict (line 14) | def predict(message, history, system_prompt, temperature, max_tokens):

FILE: demo/wizardcoder_demo.py
  function main (line 9) | def main(

FILE: demo/wizardmath_demo.py
  function main (line 7) | def main(

FILE: training/src/conversation.py
  class SeparatorStyle (line 10) | class SeparatorStyle(Enum):
  class Conversation (line 25) | class Conversation:
    method get_prompt (line 53) | def get_prompt(self) -> str:
    method append_message (line 139) | def append_message(self, role: str, message: str):
    method to_gradio_chatbot (line 143) | def to_gradio_chatbot(self):
    method to_openai_api_messages (line 153) | def to_openai_api_messages(self):
    method copy (line 165) | def copy(self):
    method dict (line 181) | def dict(self):
  function register_conv_template (line 197) | def register_conv_template(template: Conversation, override: bool = False):
  function get_conv_template (line 204) | def get_conv_template(name: str) -> Conversation:

FILE: training/src/generate.py
  function main (line 26) | def main(
  function generate_prompt (line 122) | def generate_prompt(instruction, input=None):

FILE: training/src/train.py
  class ModelArguments (line 49) | class ModelArguments:
  class DataArguments (line 54) | class DataArguments:
  class TrainingArguments (line 59) | class TrainingArguments(transformers.TrainingArguments):
  function safe_save_model_for_hf_trainer (line 68) | def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output...
  function smart_tokenizer_and_embedding_resize (line 77) | def smart_tokenizer_and_embedding_resize(
  function _tokenize_fn (line 100) | def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrai...
  function preprocess (line 124) | def preprocess(
  class DataCollatorForSupervisedDataset (line 140) | class DataCollatorForSupervisedDataset(object):
    method __call__ (line 145) | def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
  function train_tokenize_function (line 159) | def train_tokenize_function(examples, tokenizer):
  function train (line 176) | def train():

FILE: training/src/train_freeform_multiturn.py
  class ModelArguments (line 50) | class ModelArguments:
  class DataArguments (line 55) | class DataArguments:
  class TrainingArguments (line 61) | class TrainingArguments(transformers.TrainingArguments):
  function safe_save_model_for_hf_trainer (line 70) | def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output...
  function smart_tokenizer_and_embedding_resize (line 79) | def smart_tokenizer_and_embedding_resize(
  function _tokenize_fn (line 102) | def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrai...
  function rank0_print (line 129) | def rank0_print(*args):
  function preprocess (line 134) | def preprocess(
  class SupervisedDataset (line 220) | class SupervisedDataset(Dataset):
    method __init__ (line 223) | def __init__(self, data_path: str, tokenizer: transformers.PreTrainedT...
    method __len__ (line 235) | def __len__(self):
    method __getitem__ (line 238) | def __getitem__(self, i) -> Dict[str, torch.Tensor]:
  function make_supervised_data_module (line 246) | def make_supervised_data_module(tokenizer: transformers.PreTrainedTokeni...
  function train (line 254) | def train():

FILE: training/src/utils.py
  class OpenAIDecodingArguments (line 25) | class OpenAIDecodingArguments(object):
  function openai_completion (line 39) | def openai_completion(
  function _make_w_io_base (line 133) | def _make_w_io_base(f, mode: str):
  function _make_r_io_base (line 142) | def _make_r_io_base(f, mode: str):
  function jdump (line 148) | def jdump(obj, f, mode="w", indent=4, default=str):
  function jload (line 168) | def jload(f, mode="r"):