SYMBOL INDEX (850 symbols across 127 files)

FILE: contrib/langchain/retrieval_chatbot.py
  class LangchainChatbot (line 26) | class LangchainChatbot:
    method __init__ (line 27) | def __init__(self, model_name_or_path: str, provider: str):
    method check_valid_provider (line 51) | def check_valid_provider(self):
    method set_retriever_url (line 69) | def set_retriever_url(self, url, chunk_size, chunk_overlap):
    method set_retriever_file (line 77) | def set_retriever_file(self, file, chunk_size, chunk_overlap):
    method get_model (line 85) | def get_model(self):
    method chat_with_chatbot (line 101) | def chat_with_chatbot(self, human_input, session_id):
    method retrieve_by_url (line 113) | def retrieve_by_url(self, query):
    method retrieve_by_file (line 116) | def retrieve_by_file(self, query):
    method get_session_history (line 119) | def get_session_history(self, session_id: str) -> BaseChatMessageHistory:
  function get_cli (line 125) | def get_cli() -> argparse.ArgumentParser:
  function main (line 143) | def main(

FILE: contrib/long-context/sft_summarizer.py
  class UserArguments (line 35) | class UserArguments:
  class WandbCallback (line 128) | class WandbCallback(TrainerCallback):
    method __init__ (line 129) | def __init__(self, trainer):
    method on_save (line 137) | def on_save(self, args: TrainingArguments, state: TrainerState, contro...

FILE: contrib/rlhflow/reward_modeling.py
  function build_dataset (line 72) | def build_dataset(tokenizer, config):
  function compute_metrics (line 109) | def compute_metrics(eval_pred):
  class DataCollatorReward (line 118) | class DataCollatorReward:
    method __init__ (line 119) | def __init__(self, tokenizer):
    method __call__ (line 122) | def __call__(self, data):
  class RMTrainer (line 143) | class RMTrainer(Trainer):
    method compute_loss (line 144) | def compute_loss(self, model, inputs, return_outputs=False):

FILE: contrib/text2image/diffuser_args.py
  class T2IDatasetArguments (line 9) | class T2IDatasetArguments(DatasetArguments):
    method __post_init__ (line 22) | def __post_init__(self):
  class DiffuserModelArguments (line 54) | class DiffuserModelArguments:
  class DiffuserTunerArguments (line 98) | class DiffuserTunerArguments:

FILE: contrib/text2image/diffuser_finetuner.py
  function log_validation (line 31) | def log_validation(
  class DiffuserModelTuner (line 66) | class DiffuserModelTuner(BaseTuner):
    method __init__ (line 87) | def __init__(
    method tune (line 99) | def tune(

FILE: contrib/text2image/finetune_t2i.py
  function main (line 20) | def main():

FILE: contrib/text2image/t2i_dataset.py
  class CustomT2IDataset (line 18) | class CustomT2IDataset(Dataset):
    method __init__ (line 21) | def __init__(self, data_args: T2IDatasetArguments):
    method __len__ (line 31) | def __len__(self):
    method __getitem__ (line 34) | def __getitem__(self, idx):
  class EncodePreprocessor (line 46) | class EncodePreprocessor:
    method __init__ (line 47) | def __init__(self, data_args: T2IDatasetArguments, kind: str = "simple...
    method register_simple_func (line 63) | def register_simple_func(self, tokenizer, text_encoder, vae):
    method __call__ (line 90) | def __call__(self, data_item):
  class PreprocessedT2IDataset (line 94) | class PreprocessedT2IDataset(Dataset):
    method __init__ (line 97) | def __init__(self, raw_dataset: Dataset, data_args: T2IDatasetArgument...
    method __len__ (line 104) | def __len__(self):
    method __getitem__ (line 107) | def __getitem__(self, idx):
  function build_t2i_dataset (line 111) | def build_t2i_dataset(data_args: T2IDatasetArguments, tokenizer, text_en...

FILE: contrib/tool-finetune/function_call_finetune.py
  class HFDecoderModelForTool (line 34) | class HFDecoderModelForTool(HFDecoderModel):
    method tokenize (line 35) | def tokenize(self, dataset, add_special_tokens=True, *args, **kwargs) ...
  function conversation_tokenize_function (line 158) | def conversation_tokenize_function(
  function train (line 245) | def train():

FILE: examples/benchmarking.py
  function is_lmflow_local_benchmarking (line 115) | def is_lmflow_local_benchmarking(dataset_name):
  function is_lm_evaluation_benchmarking (line 142) | def is_lm_evaluation_benchmarking(dataset_name):
  function run_lmflow_local_benchmarking (line 149) | def run_lmflow_local_benchmarking(
  function run_lm_evaluation_benchmarking (line 191) | def run_lm_evaluation_benchmarking(dataset_name, model_name):
  function main (line 214) | def main():

FILE: examples/chatbot.py
  class ChatbotArguments (line 27) | class ChatbotArguments:
  function main (line 42) | def main():

FILE: examples/chatbot_gradio.py
  class ChatbotArguments (line 80) | class ChatbotArguments:
  function hist2context (line 141) | def hist2context(hist):
  function chat_stream (line 150) | def chat_stream(query: str, history=None, **kwargs):
  function predict (line 178) | def predict(input, history=None):

FILE: examples/dpov2_train.py
  function main (line 28) | def main():

FILE: examples/finetune.py
  function main (line 32) | def main():

FILE: examples/finetune_multi_modal.py
  function main (line 34) | def main():

FILE: examples/inference.py
  function main (line 24) | def main():

FILE: examples/iterative_dpo_train.py
  function main (line 35) | def main():

FILE: examples/merge_lora.py
  class MergeLoraArguments (line 24) | class MergeLoraArguments:
  function main (line 43) | def main():

FILE: examples/multistage_finetune.py
  class MultistageFinetuneArgs (line 37) | class MultistageFinetuneArgs:
  function setup_logger (line 45) | def setup_logger():
  function generate_new_seed (line 56) | def generate_new_seed(seed):
  function shuffle_and_split_data (line 60) | def shuffle_and_split_data(dataset, num_split=None, seed=None):
  function main (line 75) | def main():

FILE: examples/raft_align.py
  class RewardArguments (line 25) | class RewardArguments:
  function get_reward_function (line 53) | def get_reward_function(reward_args, pipeline_args):
  function main (line 92) | def main():

FILE: examples/reward_modeling.py
  function main (line 23) | def main():

FILE: examples/rm_inference.py
  function main (line 21) | def main():

FILE: examples/sglang_inference.py
  function main (line 21) | def main():

FILE: examples/tool_inference.py
  function main (line 9) | def main():

FILE: examples/vis_chatbot.py
  class ChatbotArguments (line 33) | class ChatbotArguments:
  function main (line 63) | def main():

FILE: examples/vis_chatbot_gradio.py
  class ChatbotArguments (line 69) | class ChatbotArguments:
  function gradio_reset (line 89) | def gradio_reset(chat_state, img_list):
  function upload_image (line 103) | def upload_image(image_file, history, text_input, chat_state, image_list):
  function read_img (line 133) | def read_img(image):
  function gradio_ask (line 143) | def gradio_ask(user_message, chatbot, chat_state):
  function gradio_answer (line 155) | def gradio_answer(chatbot, chat_state, image_list, num_beams=1, temperat...
  function start_inferencer (line 214) | def start_inferencer(

FILE: examples/vllm_inference.py
  function main (line 21) | def main():

FILE: experimental/LISA-diffusion/diffusion_dpo/train_diffusion_dpo.py
  function import_model_class_from_model_name_or_path (line 69) | def import_model_class_from_model_name_or_path(pretrained_model_name_or_...
  function log_validation (line 85) | def log_validation(args, unet, accelerator, weight_dtype, epoch, is_fina...
  function parse_args (line 150) | def parse_args(input_args=None):
  function tokenize_captions (line 443) | def tokenize_captions(tokenizer, examples):
  function encode_prompt (line 455) | def encode_prompt(text_encoder, input_ids):
  function main (line 465) | def main(args):

FILE: experimental/LISA-diffusion/diffusion_dpo/train_diffusion_dpo_lisa.py
  function freeze_all_layers (line 67) | def freeze_all_layers(model):
  function random_activate_layers (line 72) | def random_activate_layers(model, p):
  function lisa (line 85) | def lisa(model, p=0.25):
  function import_model_class_from_model_name_or_path (line 90) | def import_model_class_from_model_name_or_path(pretrained_model_name_or_...
  function update_ema (line 107) | def update_ema(target_params, source_params, rate=0.99):
  function log_validation (line 121) | def log_validation(args, unet, accelerator, weight_dtype, epoch, is_fina...
  function parse_args (line 185) | def parse_args(input_args=None):
  function tokenize_captions (line 472) | def tokenize_captions(tokenizer, examples):
  function encode_prompt (line 484) | def encode_prompt(text_encoder, input_ids):
  function main (line 494) | def main(args):

FILE: experimental/LISA-diffusion/instruct_pix2pix/test_instruct_pix2pix.py
  class InstructPix2Pix (line 30) | class InstructPix2Pix(ExamplesTestsAccelerate):
    method test_instruct_pix2pix_checkpointing_checkpoints_total_limit (line 31) | def test_instruct_pix2pix_checkpointing_checkpoints_total_limit(self):
    method test_instruct_pix2pix_checkpointing_checkpoints_total_limit_removes_multiple_checkpoints (line 54) | def test_instruct_pix2pix_checkpointing_checkpoints_total_limit_remove...

FILE: experimental/LISA-diffusion/instruct_pix2pix/train_instruct_pix2pix_lisa.py
  function parse_args (line 63) | def parse_args():
  function convert_to_np (line 369) | def convert_to_np(image, resolution):
  function download_image (line 374) | def download_image(url):
  function freeze_all_layers (line 381) | def freeze_all_layers(model):
  function random_activate_layers (line 386) | def random_activate_layers(model, p):
  function lisa (line 399) | def lisa(model, p=0.25):
  function main (line 404) | def main():

FILE: experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lisa.py
  function freeze_all_layers (line 76) | def freeze_all_layers(model):
  function random_activate_layers (line 81) | def random_activate_layers(model, p):
  function lisa (line 94) | def lisa(model, p=0.25):
  function filter_keys (line 99) | def filter_keys(key_set):
  function group_by_keys_nothrow (line 106) | def group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes...
  function tarfile_to_samples_nothrow (line 134) | def tarfile_to_samples_nothrow(src, handler=wds.warn_and_continue):
  class WebdatasetFilter (line 142) | class WebdatasetFilter:
    method __init__ (line 143) | def __init__(self, min_size=1024, max_pwatermark=0.5):
    method __call__ (line 147) | def __call__(self, x):
  class SDText2ImageDataset (line 162) | class SDText2ImageDataset:
    method __init__ (line 163) | def __init__(
    method train_dataset (line 233) | def train_dataset(self):
    method train_dataloader (line 237) | def train_dataloader(self):
  function log_validation (line 241) | def log_validation(vae, unet, args, accelerator, weight_dtype, step, nam...
  function guidance_scale_embedding (line 337) | def guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32):
  function append_dims (line 366) | def append_dims(x, target_dims):
  function scalings_for_boundary_conditions (line 375) | def scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_...
  function get_predicted_original_sample (line 383) | def get_predicted_original_sample(model_output, timesteps, sample, predi...
  function get_predicted_noise (line 402) | def get_predicted_noise(model_output, timesteps, sample, prediction_type...
  function extract_into_tensor (line 420) | def extract_into_tensor(a, t, x_shape):
  class DDIMSolver (line 426) | class DDIMSolver:
    method __init__ (line 427) | def __init__(self, alpha_cumprods, timesteps=1000, ddim_timesteps=50):
    method to (line 440) | def to(self, device):
    method ddim_step (line 446) | def ddim_step(self, pred_x0, pred_noise, timestep_index):
  function update_ema (line 454) | def update_ema(target_params, source_params, rate=0.99):
  function import_model_class_from_model_name_or_path (line 468) | def import_model_class_from_model_name_or_path(
  function parse_args (line 488) | def parse_args():
  function encode_prompt (line 849) | def encode_prompt(prompt_batch, text_encoder, tokenizer, proportion_empt...
  function main (line 874) | def main(args):

FILE: experimental/LISA-diffusion/latent_consistency_model/train_lcm_distill_sd_wds_lora.py
  function get_module_kohya_state_dict (line 77) | def get_module_kohya_state_dict(module, prefix: str, dtype: torch.dtype,...
  function filter_keys (line 94) | def filter_keys(key_set):
  function group_by_keys_nothrow (line 101) | def group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes...
  function tarfile_to_samples_nothrow (line 129) | def tarfile_to_samples_nothrow(src, handler=wds.warn_and_continue):
  class WebdatasetFilter (line 137) | class WebdatasetFilter:
    method __init__ (line 138) | def __init__(self, min_size=1024, max_pwatermark=0.5):
    method __call__ (line 142) | def __call__(self, x):
  class SDText2ImageDataset (line 157) | class SDText2ImageDataset:
    method __init__ (line 158) | def __init__(
    method train_dataset (line 228) | def train_dataset(self):
    method train_dataloader (line 232) | def train_dataloader(self):
  function log_validation (line 236) | def log_validation(vae, unet, args, accelerator, weight_dtype, step):
  function guidance_scale_embedding (line 318) | def guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32):
  function append_dims (line 347) | def append_dims(x, target_dims):
  function scalings_for_boundary_conditions (line 356) | def scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_...
  function get_predicted_original_sample (line 364) | def get_predicted_original_sample(model_output, timesteps, sample, predi...
  function get_predicted_noise (line 383) | def get_predicted_noise(model_output, timesteps, sample, prediction_type...
  function extract_into_tensor (line 401) | def extract_into_tensor(a, t, x_shape):
  class DDIMSolver (line 407) | class DDIMSolver:
    method __init__ (line 408) | def __init__(self, alpha_cumprods, timesteps=1000, ddim_timesteps=50):
    method to (line 421) | def to(self, device):
    method ddim_step (line 427) | def ddim_step(self, pred_x0, pred_noise, timestep_index):
  function update_ema (line 435) | def update_ema(target_params, source_params, rate=0.99):
  function import_model_class_from_model_name_or_path (line 448) | def import_model_class_from_model_name_or_path(
  function parse_args (line 468) | def parse_args():
  function encode_prompt (line 842) | def encode_prompt(prompt_batch, text_encoder, tokenizer, proportion_empt...
  function main (line 867) | def main(args):

FILE: experimental/LISA-diffusion/single_lisa.py
  class LISADiffusion (line 5) | class LISADiffusion:
    method __init__ (line 6) | def __init__(self, model, rate=None):
    method freeze_all_layers (line 11) | def freeze_all_layers(self, model):
    method random_activate_layers (line 15) | def random_activate_layers(self, model, p):
    method lisa (line 26) | def lisa(self, model, p=0.25):
    method lisa_recall (line 30) | def lisa_recall(self):
    method initialize (line 35) | def initialize(self):
    method register (line 40) | def register(
    method insert_hook (line 65) | def insert_hook(

FILE: experimental/RAFT-diffusion/train_text_to_image_lora.py
  function save_model_card (line 53) | def save_model_card(repo_name, images=None, base_model=str, dataset_name...
  function parse_args (line 82) | def parse_args():
  function get_full_repo_name (line 346) | def get_full_repo_name(model_id: str, organization: Optional[str] = None...
  function main (line 361) | def main():

FILE: scripts/archive/convert_llama_weights_to_hf.py
  function compute_intermediate_size (line 59) | def compute_intermediate_size(n):
  function read_json (line 63) | def read_json(path):
  function write_json (line 68) | def write_json(text, path):
  function write_model (line 73) | def write_model(model_path, input_base_path, model_size):
  function write_tokenizer (line 232) | def write_tokenizer(tokenizer_path, input_tokenizer_path):
  function main (line 249) | def main():

FILE: scripts/archive/export_llama_state_dict_checkpoint.py
  function permute (line 11) | def permute(w):
  function unpermute (line 15) | def unpermute(w):
  function translate_state_dict_key (line 19) | def translate_state_dict_key(k):  # noqa: C901

FILE: src/lmflow/args.py
  class OptimizerNames (line 34) | class OptimizerNames:
  class ModelArguments (line 58) | class ModelArguments:
    method __post_init__ (line 331) | def __post_init__(self):
  class VisModelArguments (line 368) | class VisModelArguments(ModelArguments):
  class DatasetArguments (line 418) | class DatasetArguments:
    method __post_init__ (line 601) | def __post_init__(self):
  class MultiModalDatasetArguments (line 617) | class MultiModalDatasetArguments(DatasetArguments):
  class FinetunerArguments (line 626) | class FinetunerArguments(TrainingArguments):
  class RewardModelTunerArguments (line 684) | class RewardModelTunerArguments(FinetunerArguments):
  class EvaluatorArguments (line 693) | class EvaluatorArguments:
    method __post_init__ (line 843) | def __post_init__(self):
  class InferencerArguments (line 853) | class InferencerArguments:
    method __post_init__ (line 1070) | def __post_init__(self):
  class RaftAlignerArguments (line 1135) | class RaftAlignerArguments(TrainingArguments):
  class BenchmarkingArguments (line 1195) | class BenchmarkingArguments:
  class DPOAlignerArguments (line 1227) | class DPOAlignerArguments:
  class DPOv2AlignerArguments (line 1309) | class DPOv2AlignerArguments(FinetunerArguments):
  class IterativeAlignerArguments (line 1333) | class IterativeAlignerArguments(InferencerArguments):
  class IterativeDPOAlignerArguments (line 1347) | class IterativeDPOAlignerArguments(IterativeAlignerArguments, DPOv2Align...
  class AutoArguments (line 1384) | class AutoArguments:
    method get_pipeline_args_class (line 1389) | def get_pipeline_args_class(pipeline_name: str):
  function split_args (line 1393) | def split_args(args):

FILE: src/lmflow/datasets/dataset.py
  class Dataset (line 53) | class Dataset:
    method __init__ (line 72) | def __init__(self, data_args: DatasetArguments = None, backend: str = ...
    method __len__ (line 112) | def __len__(self):
    method _check_instance_format (line 115) | def _check_instance_format(self):
    method _check_hf_json_format (line 125) | def _check_hf_json_format(self, data_files: list[str]):
    method from_dict (line 150) | def from_dict(self, dict_obj: dict, *args, **kwargs):
    method create_from_dict (line 238) | def create_from_dict(cls, dict_obj, *args, **kwargs):
    method to_dict (line 249) | def to_dict(self):
    method to_list (line 298) | def to_list(self):
    method map (line 310) | def map(self, *args, **kwargs):
    method get_backend (line 336) | def get_backend(self) -> Optional[str]:
    method get_backend_dataset (line 345) | def get_backend_dataset(self):
    method get_fingerprint (line 354) | def get_fingerprint(self):
    method get_data_args (line 363) | def get_data_args(self):
    method get_type (line 372) | def get_type(self) -> str:
    method save (line 381) | def save(self, file_path: str, format: str = "json"):
    method sample (line 398) | def sample(self, n: int, seed: int = 42):
    method train_test_split (line 428) | def train_test_split(self, test_size: float = 0.2, shuffle: bool = Tru...
    method drop_instances (line 470) | def drop_instances(self, indices: list):
    method sanity_check (line 484) | def sanity_check(
    method hf_dataset_sanity_check (line 496) | def hf_dataset_sanity_check(

FILE: src/lmflow/datasets/multi_modal_dataset.py
  class CustomMultiModalDataset (line 26) | class CustomMultiModalDataset(Dataset):
    method __init__ (line 29) | def __init__(self, dataset_path: str, data_args: DatasetArguments):
    method __len__ (line 37) | def __len__(self):
    method register_tokenizer (line 40) | def register_tokenizer(self, tokenizer, image_processor=None):
    method __getitem__ (line 44) | def __getitem__(self, i):
  function preprocess_multimodal_llava (line 94) | def preprocess_multimodal_llava(sources, data_args):
  function tokenizer_image_token (line 116) | def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOK...
  function preprocess_llama_from_llava_plain (line 138) | def preprocess_llama_from_llava_plain(sources, tokenizer: transformers.P...
  function preprocess_llama_from_llava_v1 (line 166) | def preprocess_llama_from_llava_v1(sources, tokenizer: transformers.PreT...
  class DataCollatorForSupervisedDataset (line 254) | class DataCollatorForSupervisedDataset:
    method __call__ (line 259) | def __call__(self, instances):

FILE: src/lmflow/models/auto_model.py
  class AutoModel (line 10) | class AutoModel:
    method get_model (line 12) | def get_model(self, model_args, *args, **kwargs):

FILE: src/lmflow/models/base_model.py
  class BaseModel (line 5) | class BaseModel:

FILE: src/lmflow/models/decoder_model.py
  class DecoderModel (line 18) | class DecoderModel(BaseModel):
    method __init__ (line 19) | def __init__(self, *args, **kwargs):

FILE: src/lmflow/models/encoder_decoder_model.py
  class EncoderDecoderModel (line 18) | class EncoderDecoderModel(BaseModel):
    method __init__ (line 19) | def __init__(self, *args, **kwargs):

FILE: src/lmflow/models/hf_decoder_model.py
  class HFDecoderModel (line 62) | class HFDecoderModel(DecoderModel, HFModelMixin, Tunable):
    method __init__ (line 83) | def __init__(self, model_args, do_train=True, device="gpu", **kwargs):
    method tokenize (line 86) | def tokenize(self, dataset: Dataset, add_special_tokens=True, *args, *...
    method encode (line 239) | def encode(self, input: Union[str, list[str]], *args, **kwargs) -> Uni...
    method decode (line 273) | def decode(self, input, **kwargs) -> Union[str, list[str]]:
    method inference (line 315) | def inference(
    method __inference (line 387) | def __inference(self, inputs, *args, **kwargs):
    method __vllm_inference (line 425) | def __vllm_inference(
    method __sglang_inference (line 468) | def __sglang_inference(
    method prepare_inputs_for_inference (line 493) | def prepare_inputs_for_inference(
    method merge_lora_weights (line 597) | def merge_lora_weights(self):
    method get_peft_without_qlora (line 610) | def get_peft_without_qlora(self):
    method save (line 649) | def save(self, dir, save_full_model=False, *args, **kwargs):

FILE: src/lmflow/models/hf_model_mixin.py
  class HFModelMixin (line 43) | class HFModelMixin(BaseModel):
    method __init__ (line 44) | def __init__(
    method __prepare_tokenizer (line 94) | def __prepare_tokenizer(
    method __prepare_dtype (line 133) | def __prepare_dtype(
    method __prepare_model_config (line 159) | def __prepare_model_config(
    method __prepare_quant_config (line 203) | def __prepare_quant_config(
    method __prepare_peft_config (line 248) | def __prepare_peft_config(
    method __model_module_inject (line 304) | def __model_module_inject(
    method __prepare_model_for_training (line 323) | def __prepare_model_for_training(
    method __prepare_model_for_inference (line 386) | def __prepare_model_for_inference(
    method __prepare_model_for_vllm_inference (line 446) | def __prepare_model_for_vllm_inference(
    method __prepare_model_for_sglang_inference (line 466) | def __prepare_model_for_sglang_inference(
    method __fix_special_tokens (line 489) | def __fix_special_tokens(self):
    method activate_model_for_inference (line 511) | def activate_model_for_inference(
    method deactivate_model_for_inference (line 545) | def deactivate_model_for_inference(
    method get_max_length (line 575) | def get_max_length(self):
    method get_tokenizer (line 581) | def get_tokenizer(self):
    method get_backend_model (line 587) | def get_backend_model(self):

FILE: src/lmflow/models/hf_text_regression_model.py
  class HFTextRegressionModel (line 45) | class HFTextRegressionModel(TextRegressionModel, HFModelMixin, Tunable):
    method __init__ (line 66) | def __init__(self, model_args: ModelArguments, do_train: bool = False,...
    method tokenize (line 80) | def tokenize(self, dataset: Dataset, add_special_tokens=True, *args, *...
    method inference (line 222) | def inference(
    method __inference (line 266) | def __inference(self, inputs, **kwargs):
    method __vllm_inference (line 310) | def __vllm_inference(
    method prepare_inputs_for_inference (line 330) | def prepare_inputs_for_inference(
    method postprocess_inference_outputs (line 366) | def postprocess_inference_outputs(
    method postprocess_distributed_inference_outputs (line 398) | def postprocess_distributed_inference_outputs(
    method save (line 411) | def save(self, dir, *args, **kwargs):

FILE: src/lmflow/models/interfaces/tunable.py
  class Tunable (line 5) | class Tunable:

FILE: src/lmflow/models/regression_model.py
  class RegressionModel (line 7) | class RegressionModel(BaseModel):
    method __init__ (line 8) | def __init__(self, *args, **kwargs):

FILE: src/lmflow/models/text_regression_model.py
  class TextRegressionModel (line 10) | class TextRegressionModel(RegressionModel):
    method __init__ (line 27) | def __init__(self, model_args, *args, **kwargs):
    method register_inference_function (line 34) | def register_inference_function(self, inference_func):
    method inference (line 40) | def inference(self, inputs: Dataset):

FILE: src/lmflow/models/vision2seq_model.py
  class CustomAutoVision2SeqModel (line 31) | class CustomAutoVision2SeqModel(Blip2ForConditionalGeneration, BaseModel):
    method __init__ (line 32) | def __init__(
    method get_backend_model (line 111) | def get_backend_model(self):
    method vision_model_from_pretrained (line 114) | def vision_model_from_pretrained(self, pretrained_path):
    method qformer_from_pretrained (line 117) | def qformer_from_pretrained(self, pretrained_path):
    method language_model_from_pretrained (line 120) | def language_model_from_pretrained(self, pretrained_path, low_resource...
    method vision_feature_select (line 136) | def vision_feature_select(self, image_forward_outs):
    method register_prompt_cache (line 146) | def register_prompt_cache(self, prompt_ids, prompt_keys_values):
    method save_prompt_cache (line 161) | def save_prompt_cache(self, path):
    method load_prompt_cache (line 174) | def load_prompt_cache(self, path):
    method get_tokenizer (line 186) | def get_tokenizer(self):
    method forward (line 189) | def forward(
    method processor_image_token_in_minigpt4 (line 307) | def processor_image_token_in_minigpt4(
    method generate (line 352) | def generate(

FILE: src/lmflow/models/vision_encoder/clip_encoder.py
  function build_vision_tower (line 8) | def build_vision_tower(vision_tower_cfg, **kwargs):
  class CLIPVisionTower (line 17) | class CLIPVisionTower(nn.Module):
    method __init__ (line 18) | def __init__(self, vision_tower, args, delay_load=False):
    method load_model (line 31) | def load_model(self):
    method encode_images (line 37) | def encode_images(self, images, language_projection):
    method feature_select (line 45) | def feature_select(self, image_forward_outs):
    method forward (line 56) | def forward(self, images):
    method dummy_feature (line 74) | def dummy_feature(self):
    method dtype (line 78) | def dtype(self):
    method device (line 82) | def device(self):
    method config (line 86) | def config(self):
    method hidden_size (line 93) | def hidden_size(self):
    method num_patches (line 97) | def num_patches(self):
    method prepare_inputs_labels_for_multimodal (line 100) | def prepare_inputs_labels_for_multimodal(

FILE: src/lmflow/optim/adabelief.py
  class AdaBelief (line 9) | class AdaBelief(Optimizer):
    method __init__ (line 14) | def __init__(
    method __setstate__ (line 66) | def __setstate__(self, state):
    method reset (line 71) | def reset(self):
    method step (line 88) | def step(self, closure=None):

FILE: src/lmflow/optim/adabound.py
  class AdaBound (line 9) | class AdaBound(Optimizer):
    method __init__ (line 19) | def __init__(
    method __setstate__ (line 56) | def __setstate__(self, state) -> None:
    method step (line 61) | def step(self, closure=None):

FILE: src/lmflow/optim/adadelta.py
  class Adadelta (line 7) | class Adadelta(Optimizer):
    method __init__ (line 8) | def __init__(self, params, lr=1.0, rho=0.95, eps=1e-6):
    method step (line 12) | def step(self, closure=None):

FILE: src/lmflow/optim/adagrad.py
  class AdaGrad (line 6) | class AdaGrad(torch.optim.Optimizer):
    method __init__ (line 7) | def __init__(self, params, lr=0.001, eps=1e-8, weight_decay=0):
    method step (line 11) | def step(self, closure=None):

FILE: src/lmflow/optim/adam.py
  class Adam (line 7) | class Adam(Optimizer):
    method __init__ (line 8) | def __init__(self, params, lr=0.001, betas=(0.9, 0.999), eps=1e-8):
    method step (line 12) | def step(self, closure=None):

FILE: src/lmflow/optim/adamax.py
  class Adamax (line 7) | class Adamax(Optimizer):
    method __init__ (line 8) | def __init__(self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8, weig...
    method __setstate__ (line 22) | def __setstate__(self, state):
    method step (line 25) | def step(self, closure=None):

FILE: src/lmflow/optim/adamp.py
  class AdamP (line 9) | class AdamP(Optimizer):
    method __init__ (line 20) | def __init__(
    method _channel_view (line 58) | def _channel_view(x):
    method _layer_view (line 62) | def _layer_view(x):
    method _cosine_similarity (line 66) | def _cosine_similarity(x, y, eps, view_func):
    method _projection (line 76) | def _projection(self, p, grad, perturb, delta, wd_ratio, eps):
    method step (line 91) | def step(self, closure=None):

FILE: src/lmflow/optim/adamw_schedule_free.py
  class AdamWScheduleFree (line 15) | class AdamWScheduleFree(torch.optim.Optimizer):
    method __init__ (line 27) | def __init__(
    method eval (line 58) | def eval(self):
    method train (line 70) | def train(self):
    method step (line 82) | def step(self, closure=None):

FILE: src/lmflow/optim/adan.py
  class Adan (line 10) | class Adan(Optimizer):
    method __init__ (line 19) | def __init__(
    method __setstate__ (line 53) | def __setstate__(self, state):
    method restart_opt (line 59) | def restart_opt(self):
    method step (line 75) | def step(self):
  function _single_tensor_adan (line 166) | def _single_tensor_adan(
  function _multi_tensor_adan (line 217) | def _multi_tensor_adan(

FILE: src/lmflow/optim/dummy.py
  class Dummy (line 12) | class Dummy(Optimizer):
    method __init__ (line 23) | def __init__(
    method step (line 40) | def step(self, closure: Callable = None):

FILE: src/lmflow/optim/lamb.py
  class Lamb (line 9) | class Lamb(Optimizer):
    method __init__ (line 20) | def __init__(
    method step (line 51) | def step(self, closure=None):

FILE: src/lmflow/optim/lars.py
  class LARS (line 7) | class LARS(Optimizer):
    method __init__ (line 39) | def __init__(
    method __setstate__ (line 77) | def __setstate__(self, state) -> None:
    method step (line 84) | def step(self, closure=None):

FILE: src/lmflow/optim/muon.py
  function zeropower_via_newtonschulz5 (line 8) | def zeropower_via_newtonschulz5(G: Tensor, steps: int) -> Tensor:
  class Muon (line 41) | class Muon(torch.optim.Optimizer):
    method __init__ (line 46) | def __init__(self, params, lr=0.001, betas=(0.9, 0.999), eps=1e-8, wei...
    method step (line 51) | def step(self, closure=None):

FILE: src/lmflow/optim/nadam.py
  class NAdam (line 8) | class NAdam(torch.optim.Optimizer):
    method __init__ (line 9) | def __init__(self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8, weig...
    method __setstate__ (line 25) | def __setstate__(self, state):
    method step (line 28) | def step(self, closure=None):

FILE: src/lmflow/optim/novograd.py
  class NovoGrad (line 7) | class NovoGrad(optim.Optimizer):
    method __init__ (line 8) | def __init__(
    method __setstate__ (line 26) | def __setstate__(self, state):
    method step (line 31) | def step(self, closure=None):

FILE: src/lmflow/optim/radam.py
  class RAdam (line 10) | class RAdam(Optimizer):
    method __init__ (line 24) | def __init__(
    method __setstate__ (line 62) | def __setstate__(self, state):
    method step (line 65) | def step(self, closure=None):

FILE: src/lmflow/optim/sgd_schedule_free.py
  class SGDScheduleFree (line 13) | class SGDScheduleFree(torch.optim.Optimizer):
    method __init__ (line 25) | def __init__(
    method eval (line 61) | def eval(self):
    method train (line 73) | def train(self):
    method step (line 85) | def step(self, closure=None):

FILE: src/lmflow/optim/sgdp.py
  class SGDP (line 9) | class SGDP(Optimizer):
    method __init__ (line 20) | def __init__(
    method _channel_view (line 60) | def _channel_view(x):
    method _layer_view (line 64) | def _layer_view(x):
    method _cosine_similarity (line 68) | def _cosine_similarity(x, y, eps, view_func):
    method _projection (line 78) | def _projection(self, p, grad, perturb, delta, wd_ratio, eps):
    method step (line 93) | def step(self, closure=None):

FILE: src/lmflow/optim/sophia.py
  class SophiaG (line 7) | class SophiaG(Optimizer):
    method __init__ (line 13) | def __init__(
    method __setstate__ (line 39) | def __setstate__(self, state):
    method update_hessian (line 51) | def update_hessian(self):
    method step (line 74) | def step(self, closure=None, bs=5120):

FILE: src/lmflow/optim/utils.py
  function create_customized_optimizer (line 10) | def create_customized_optimizer(base_trainer_class, model_args):

FILE: src/lmflow/optim/yogi.py
  class Yogi (line 10) | class Yogi(Optimizer):
    method __init__ (line 20) | def __init__(
    method step (line 49) | def step(self, closure=None):

FILE: src/lmflow/pipeline/auto_pipeline.py
  class AutoPipeline (line 57) | class AutoPipeline:
    method get_pipeline (line 63) | def get_pipeline(self, pipeline_name, model_args, data_args, pipeline_...

FILE: src/lmflow/pipeline/base_aligner.py
  class BaseAligner (line 9) | class BaseAligner(BasePipeline):
    method __init__ (line 12) | def __init__(self, *args, **kwargs):
    method _check_if_alignable (line 15) | def _check_if_alignable(self, model, dataset, reward_model):
    method align (line 21) | def align(self, model, dataset, reward_model):

FILE: src/lmflow/pipeline/base_pipeline.py
  class BasePipeline (line 5) | class BasePipeline:

FILE: src/lmflow/pipeline/base_tuner.py
  class BaseTuner (line 9) | class BaseTuner(BasePipeline):
    method __init__ (line 12) | def __init__(self, *args, **kwargs):
    method _check_if_tunable (line 15) | def _check_if_tunable(self, model, dataset):
    method tune (line 20) | def tune(self, model, dataset):

FILE: src/lmflow/pipeline/dpo_aligner.py
  function get_paired_dataset (line 23) | def get_paired_dataset(
  class DPOAligner (line 70) | class DPOAligner(BaseAligner):
    method __init__ (line 71) | def __init__(self, model_args, data_args, aligner_args):
    method _initialize_trainer (line 78) | def _initialize_trainer(self, model, tokenizer):
    method _load_dataset (line 132) | def _load_dataset(self):
    method align (line 151) | def align(self, model, dataset, reward_model):

FILE: src/lmflow/pipeline/dpov2_aligner.py
  class DPOv2Aligner (line 30) | class DPOv2Aligner(BaseAligner):
    method __init__ (line 31) | def __init__(
    method align (line 43) | def align(
    method __prepare_training_args (line 114) | def __prepare_training_args(
    method convert_to_paired_dataset (line 143) | def convert_to_paired_dataset(
    method _calc_response_lengths (line 184) | def _calc_response_lengths(
    method _calc_reward_with_length_penalty (line 198) | def _calc_reward_with_length_penalty(
    method sampling_paired_idx_from_rewards (line 210) | def sampling_paired_idx_from_rewards(
    method _sampling_paired_idx_from_rewards (line 228) | def _sampling_paired_idx_from_rewards(
    method _sampling_paired_idx_from_rewards_fast (line 250) | def _sampling_paired_idx_from_rewards_fast(
  class MemorySafeDPOv2Aligner (line 273) | class MemorySafeDPOv2Aligner:
    method __init__ (line 274) | def __init__(
    method align (line 287) | def align(self):

FILE: src/lmflow/pipeline/evaluator.py
  class Evaluator (line 41) | class Evaluator(BasePipeline):
    method __init__ (line 59) | def __init__(
    method create_dataloader (line 103) | def create_dataloader(self, dataset: Dataset):
    method _match (line 122) | def _match(self, predicted_answer, groundtruth, answer_type=None):
    method evaluate (line 137) | def evaluate(
    method _evaluate_acc_with_accelerate (line 174) | def _evaluate_acc_with_accelerate(self, model, dataset, verbose=True):
    method _evaluate_acc_with_deepspeed (line 282) | def _evaluate_acc_with_deepspeed(self, model, dataset, verbose=True):
    method _evaluate_ppl (line 386) | def _evaluate_ppl(self, model, dataset: Dataset, verbose=True):
    method _evaluate_nll (line 435) | def _evaluate_nll(

FILE: src/lmflow/pipeline/finetuner.py
  class Finetuner (line 40) | class Finetuner(BaseTuner):
    method __init__ (line 63) | def __init__(
    method group_text (line 131) | def group_text(self, tokenized_datasets, model_max_length):
    method tune (line 222) | def tune(

FILE: src/lmflow/pipeline/inferencer.py
  function rstrip_partial_utf8 (line 32) | def rstrip_partial_utf8(string):
  class Inferencer (line 44) | class Inferencer(BasePipeline):
    method __init__ (line 62) | def __init__(
    method create_dataloader (line 98) | def create_dataloader(self, dataset: Dataset):
    method inference (line 127) | def inference(
    method stream_inference (line 269) | def stream_inference(
  class SpeculativeInferencer (line 317) | class SpeculativeInferencer(Inferencer):
    method __init__ (line 338) | def __init__(self, model_args, draft_model_args, data_args, inferencer...
    method score_to_prob (line 350) | def score_to_prob(
    method sample (line 399) | def sample(prob: torch.Tensor, num_samples: int = 1) -> dict:
    method predict_next_token (line 409) | def predict_next_token(model: HFDecoderModel, input_ids: torch.Tensor,...
    method autoregressive_sampling (line 421) | def autoregressive_sampling(
    method inference (line 437) | def inference(
    method stream_inference (line 560) | def stream_inference(self):
  class ToolInferencer (line 564) | class ToolInferencer(Inferencer):
    method __init__ (line 582) | def __init__(self, model_args, data_args, inferencer_args):
    method inference (line 587) | def inference(
    method code_exec (line 627) | def code_exec(self, code):

FILE: src/lmflow/pipeline/iterative_dpo_aligner.py
  class IterativeDPOAligner (line 27) | class IterativeDPOAligner:
    method __init__ (line 28) | def __init__(
    method align (line 44) | def align(self, dataset_list: list[Dataset]):
    method _align_single_iteration (line 64) | def _align_single_iteration(
    method _do_target_model_inference (line 118) | def _do_target_model_inference(
    method _do_reward_model_inference (line 146) | def _do_reward_model_inference(
    method _do_single_dpo_align (line 171) | def _do_single_dpo_align(
    method _parse_target_model_inference_args (line 191) | def _parse_target_model_inference_args(
    method _parse_reward_model_inference_args (line 205) | def _parse_reward_model_inference_args(
    method _parse_dpo_aligner_args (line 216) | def _parse_dpo_aligner_args(
    method __filter_args (line 231) | def __filter_args(

FILE: src/lmflow/pipeline/raft_aligner.py
  class RaftAligner (line 34) | class RaftAligner(BaseAligner):
    method __init__ (line 57) | def __init__(self, model_args, data_args, aligner_args, *args, **kwargs):
    method _initialize_trainer (line 80) | def _initialize_trainer(self, model, tokenizer, training_args):
    method _load_dataset (line 96) | def _load_dataset(
    method _load_input_dataset (line 218) | def _load_input_dataset(self, dataset, tokenizer):
    method _clean_text (line 244) | def _clean_text(self, text):
    method _discard_sample (line 250) | def _discard_sample(self, text):
    method _get_batch_dataset_top (line 257) | def _get_batch_dataset_top(
    method _get_batch_dataset_local (line 390) | def _get_batch_dataset_local(
    method align (line 545) | def align(self, model, dataset, reward_model):

FILE: src/lmflow/pipeline/rm_inferencer.py
  class RewardModelInferencer (line 41) | class RewardModelInferencer(BasePipeline):
    method __init__ (line 57) | def __init__(
    method inference (line 84) | def inference(
    method _inference (line 132) | def _inference(
    method __inference (line 157) | def __inference(
    method __distributed_inference (line 200) | def __distributed_inference(
    method __vllm_inference (line 300) | def __vllm_inference(
    method __post_process_model_output (line 308) | def __post_process_model_output(
    method flatten_list (line 316) | def flatten_list(self, list_of_list: list[list]) -> tuple[list, list[i...
    method compress_list (line 321) | def compress_list(self, list_to_compress: list, sublist_lengths: list[...

FILE: src/lmflow/pipeline/rm_tuner.py
  class RewardModelTuner (line 16) | class RewardModelTuner(Finetuner):
    method __init__ (line 37) | def __init__(self, model_args, data_args, finetuner_args, *args, **kwa...
    method tune (line 40) | def tune(

FILE: src/lmflow/pipeline/sglang_inferencer.py
  class SGLangInferencer (line 29) | class SGLangInferencer(BasePipeline):
    method __init__ (line 30) | def __init__(
    method _parse_args_to_sampling_params (line 43) | def _parse_args_to_sampling_params(
    method inference (line 62) | def inference(
    method save_inference_results (line 99) | def save_inference_results(
    method load_inference_results (line 110) | def load_inference_results(

FILE: src/lmflow/pipeline/utils/dpov2_dataprocessor.py
  class PreferenceDataCollatorWithPadding (line 16) | class PreferenceDataCollatorWithPadding:
    method tokenize_batch_element (line 29) | def tokenize_batch_element(
    method collate (line 133) | def collate(self, batch):
    method __call__ (line 174) | def __call__(self, features: list[dict[str, Any]]) -> dict[str, Any]:

FILE: src/lmflow/pipeline/utils/dpov2_trainer.py
  class DPOv2Trainer (line 29) | class DPOv2Trainer(DPOTrainer):
    method __init__ (line 30) | def __init__(
    method dpo_loss (line 102) | def dpo_loss(
    method get_batch_loss_metrics (line 193) | def get_batch_loss_metrics(
    method get_batch_metrics (line 201) | def get_batch_metrics(

FILE: src/lmflow/pipeline/utils/lisa_trainer.py
  class DynamicLayerActivationCallback (line 8) | class DynamicLayerActivationCallback(TrainerCallback):
    method __init__ (line 9) | def __init__(
    method freeze_all_layers (line 43) | def freeze_all_layers(self):
    method on_step_begin (line 49) | def on_step_begin(self, args, state, control, **kwargs):
    method switch_active_layers (line 54) | def switch_active_layers(self):

FILE: src/lmflow/pipeline/utils/memory_safe_dpov2_align.py
  function main (line 26) | def main():

FILE: src/lmflow/pipeline/utils/memory_safe_vllm_inference.py
  function main (line 27) | def main():

FILE: src/lmflow/pipeline/utils/raft_trainer.py
  class RaftTrainer (line 209) | class RaftTrainer:
    method __init__ (line 282) | def __init__(
    method add_callback (line 655) | def add_callback(self, callback):
    method pop_callback (line 665) | def pop_callback(self, callback):
    method remove_callback (line 678) | def remove_callback(self, callback):
    method _move_model_to_device (line 688) | def _move_model_to_device(self, model, device):
    method _set_signature_columns_if_needed (line 694) | def _set_signature_columns_if_needed(self):
    method _remove_unused_columns (line 702) | def _remove_unused_columns(self, dataset: "datasets.Dataset", descript...
    method _get_collator_with_removed_columns (line 728) | def _get_collator_with_removed_columns(
    method _get_train_sampler (line 746) | def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
    method get_train_dataloader (line 817) | def get_train_dataloader(self) -> DataLoader:
    method _get_eval_sampler (line 865) | def _get_eval_sampler(self, eval_dataset: Dataset) -> Optional[torch.u...
    method get_eval_dataloader (line 894) | def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) ...
    method get_test_dataloader (line 942) | def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader:
    method create_optimizer_and_scheduler (line 988) | def create_optimizer_and_scheduler(self, num_training_steps: int):
    method create_optimizer (line 1003) | def create_optimizer(self):
    method get_optimizer_cls_and_kwargs (line 1059) | def get_optimizer_cls_and_kwargs(args: TrainingArguments) -> tuple[Any...
    method create_scheduler (line 1147) | def create_scheduler(self, num_training_steps: int, optimizer: torch.o...
    method num_examples (line 1166) | def num_examples(self, dataloader: DataLoader) -> int:
    method _hp_search_setup (line 1180) | def _hp_search_setup(self, trial: Union["optuna.Trial", dict[str, Any]]):
    method _report_to_hp_search (line 1221) | def _report_to_hp_search(self, trial: Union["optuna.Trial", dict[str, ...
    method _tune_save_checkpoint (line 1239) | def _tune_save_checkpoint(self):
    method call_model_init (line 1252) | def call_model_init(self, trial=None):
    method torch_jit_model_eval (line 1266) | def torch_jit_model_eval(self, model, dataloader, training=False):
    method ipex_optimize_model (line 1304) | def ipex_optimize_model(self, model, training=False, dtype=torch.float...
    method _wrap_model (line 1327) | def _wrap_model(self, model, training=True, dataloader=None):
    method train (line 1517) | def train(
    method _one_train (line 1613) | def _one_train(
    method _inner_training_loop (line 1950) | def _inner_training_loop(
    method _get_output_dir (line 2053) | def _get_output_dir(self, trial):
    method _load_from_checkpoint (line 2073) | def _load_from_checkpoint(self, resume_from_checkpoint, model=None):
    method _load_best_model (line 2131) | def _load_best_model(self):
    method _issue_warnings_after_load (line 2191) | def _issue_warnings_after_load(self, load_result):
    method _maybe_log_save_evaluate (line 2202) | def _maybe_log_save_evaluate(self, tr_loss, model, trial, epoch, ignor...
    method _load_rng_state (line 2241) | def _load_rng_state(self, checkpoint):
    method _save_checkpoint (line 2282) | def _save_checkpoint(self, model, trial, metrics=None):
    method _load_optimizer_and_scheduler (line 2391) | def _load_optimizer_and_scheduler(self, checkpoint):
    method hyperparameter_search (line 2448) | def hyperparameter_search(
    method log (line 2546) | def log(self, logs: dict[str, float]) -> None:
    method _prepare_input (line 2561) | def _prepare_input(self, data: Union[torch.Tensor, Any]) -> Union[torc...
    method _prepare_inputs (line 2579) | def _prepare_inputs(self, inputs: dict[str, Union[torch.Tensor, Any]])...
    method compute_loss_context_manager (line 2595) | def compute_loss_context_manager(self):
    method autocast_smart_context_manager (line 2601) | def autocast_smart_context_manager(self, cache_enabled: Optional[bool]...
    method training_step (line 2620) | def training_step(self, model: nn.Module, inputs: dict[str, Union[torc...
    method compute_loss (line 2664) | def compute_loss(self, model, inputs, return_outputs=False):
    method is_local_process_zero (line 2695) | def is_local_process_zero(self) -> bool:
    method is_world_process_zero (line 2702) | def is_world_process_zero(self) -> bool:
    method save_model (line 2714) | def save_model(self, output_dir: Optional[str] = None, _internal_call:...
    method _save_tpu (line 2777) | def _save_tpu(self, output_dir: Optional[str] = None):
    method _save (line 2805) | def _save(self, output_dir: Optional[str] = None, state_dict=None):
    method store_flos (line 2830) | def store_flos(self):
    method _sorted_checkpoints (line 2841) | def _sorted_checkpoints(
    method _rotate_checkpoints (line 2865) | def _rotate_checkpoints(self, use_mtime=False, output_dir=None) -> None:
    method evaluate (line 2890) | def evaluate(
    method predict (line 2957) | def predict(
    method evaluation_loop (line 3012) | def evaluation_loop(
    method _nested_gather (line 3220) | def _nested_gather(self, tensors, name=None):
    method _pad_across_processes (line 3238) | def _pad_across_processes(self, tensor, pad_index=-100):
    method prediction_step (line 3270) | def prediction_step(
    method floating_point_ops (line 3371) | def floating_point_ops(self, inputs: dict[str, Union[torch.Tensor, Any...
    method init_git_repo (line 3387) | def init_git_repo(self, at_init: bool = False):
    method create_model_card (line 3433) | def create_model_card(
    method _push_from_checkpoint (line 3488) | def _push_from_checkpoint(self, checkpoint_folder):
    method push_to_hub (line 3530) | def push_to_hub(self, commit_message: Optional[str] = "End of training...
    method prediction_loop (line 3588) | def prediction_loop(
    method _gather_and_numpify (line 3733) | def _gather_and_numpify(self, tensors, name):
    method _add_sm_patterns_to_gitignore (line 3749) | def _add_sm_patterns_to_gitignore(self) -> None:

FILE: src/lmflow/pipeline/utils/rm_dataprocessor.py
  class RewardDataCollatorWithPadding (line 12) | class RewardDataCollatorWithPadding:
    method __call__ (line 19) | def __call__(self, features: list[dict[str, Any]]) -> dict[str, Any]:

FILE: src/lmflow/pipeline/utils/rm_trainer.py
  function compute_metrics (line 7) | def compute_metrics(eval_pred):
  function rm_loss (line 16) | def rm_loss(model, inputs, return_outputs=False):
  class RewardTrainer (line 29) | class RewardTrainer(Trainer):
    method compute_loss (line 30) | def compute_loss(self, model, inputs, return_outputs=False):

FILE: src/lmflow/pipeline/vllm_inferencer.py
  class InferencerWithOffloading (line 46) | class InferencerWithOffloading(BasePipeline):
    method __init__ (line 47) | def __init__(
    method inference (line 58) | def inference(self):
    method save_inference_results (line 61) | def save_inference_results(self):
    method load_inference_results (line 64) | def load_inference_results(self):
  class VLLMInferencer (line 68) | class VLLMInferencer(InferencerWithOffloading):
    method __init__ (line 69) | def __init__(
    method parse_to_sampling_params (line 79) | def parse_to_sampling_params(
    method inference (line 94) | def inference(
    method _inference (line 171) | def _inference(
    method _distributed_inference (line 189) | def _distributed_inference(
    method save_inference_results (line 277) | def save_inference_results(
    method load_inference_results (line 287) | def load_inference_results(
  class MemorySafeVLLMInferencer (line 297) | class MemorySafeVLLMInferencer(VLLMInferencer):
    method __init__ (line 298) | def __init__(
    method inference (line 308) | def inference(self) -> list[VLLMInferenceResultWithInput]:

FILE: src/lmflow/tokenization/hf_decoder_model.py
  function blocking (line 19) | def blocking(
  function tokenize_function (line 57) | def tokenize_function(
  function conversation_tokenize_function (line 111) | def conversation_tokenize_function(

FILE: src/lmflow/tokenization/hf_text_regression_model.py
  function blocking_paired (line 19) | def blocking_paired(
  function blocking (line 61) | def blocking(
  function blocking_text_to_textlist (line 99) | def blocking_text_to_textlist(
  function paired_conversation_tokenize_function (line 140) | def paired_conversation_tokenize_function(
  function conversation_tokenize_function (line 214) | def conversation_tokenize_function(
  function tokenize_function (line 283) | def tokenize_function(
  function text_to_textlist_tokenize_function (line 337) | def text_to_textlist_tokenize_function(

FILE: src/lmflow/utils/common.py
  function make_shell_args_from_dataclass (line 13) | def make_shell_args_from_dataclass(
  function create_copied_dataclass (line 76) | def create_copied_dataclass(original_dataclass, field_prefix: str, class...
  function remove_dataclass_attr_prefix (line 133) | def remove_dataclass_attr_prefix(data_instance, prefix: str) -> dict:
  function add_dataclass_attr_prefix (line 156) | def add_dataclass_attr_prefix(data_instance, prefix: str) -> dict:
  function print_banner (line 179) | def print_banner(message: str):

FILE: src/lmflow/utils/conversation_template/base.py
  class TemplateComponent (line 18) | class TemplateComponent:
    method __post_init__ (line 56) | def __post_init__(self):
    method __repr__ (line 74) | def __repr__(self) -> str:
    method __str__ (line 77) | def __str__(self) -> str:
  class Formatter (line 82) | class Formatter(ABC):
    method format (line 86) | def format(self, **kwargs) -> list[TemplateComponent]: ...
    method has_placeholder (line 88) | def has_placeholder(self):
  class EmptyFormatter (line 99) | class EmptyFormatter(Formatter):
    method __post_init__ (line 100) | def __post_init__(self):
    method format (line 104) | def format(self, **kwargs) -> list:
  class StringFormatter (line 117) | class StringFormatter(Formatter):
    method __post_init__ (line 118) | def __post_init__(self):
    method format (line 122) | def format(self, **kwargs) -> list:
  class ConversationTemplate (line 156) | class ConversationTemplate:
    method __post_init__ (line 171) | def __post_init__(self):
    method encode_conversation (line 182) | def encode_conversation(
    method _encode (line 231) | def _encode(
    method _encode_template (line 264) | def _encode_template(
    method post_process_pairs (line 302) | def post_process_pairs(self, encoded_pairs, tokenizer):
    method remove_last_separator (line 326) | def remove_last_separator(
    method add_special_starter (line 349) | def add_special_starter(
    method add_special_stopper (line 372) | def add_special_stopper(
    method _ensure_id_list (line 395) | def _ensure_id_list(self, obj: Union[int, list[int]]) -> list[int]:
  class ConversationTemplateForTool (line 406) | class ConversationTemplateForTool(ConversationTemplate):
    method encode_conversation (line 407) | def encode_conversation(
    method _encode (line 456) | def _encode(
    method _encode_template (line 508) | def _encode_template(
    method _handle_tools (line 546) | def _handle_tools(self, tools: Optional[list[str]]) -> str:

FILE: src/lmflow/utils/conversation_template/gemma.py
  class GemmaConversationTemplate (line 12) | class GemmaConversationTemplate(ConversationTemplate):
    method encode_conversation (line 13) | def encode_conversation(self, *args, **kwargs):

FILE: src/lmflow/utils/conversation_template/hymba.py
  class HymbaConversationTemplate (line 37) | class HymbaConversationTemplate(ConversationTemplateForTool):
    method _handle_tools (line 38) | def _handle_tools(self, tools: Optional[list[str]]) -> str:

FILE: src/lmflow/utils/conversation_template/llama.py
  class Llama2ConversationTemplate (line 16) | class Llama2ConversationTemplate(ConversationTemplate):
    method _encode (line 17) | def _encode(
  class Llama2ConversationTemplateForTool (line 54) | class Llama2ConversationTemplateForTool(Llama2ConversationTemplate):
    method _encode (line 55) | def _encode(

FILE: src/lmflow/utils/conversation_template/zephyr.py
  class ZephyrConversationTemplate (line 14) | class ZephyrConversationTemplate(ConversationTemplate):
    method _encode (line 15) | def _encode(

FILE: src/lmflow/utils/data_utils.py
  function set_random_seed (line 15) | def set_random_seed(seed: int):
  function load_data (line 32) | def load_data(file_name: str):
  function batchlize (line 66) | def batchlize(examples: list, batch_size: int, random_shuffle: bool):
  function preview_file (line 99) | def preview_file(file_path: str, chars: int = 100):
  function get_dataset_type_fast (line 129) | def get_dataset_type_fast(file_path: str, max_chars: int = 100) -> Union...
  function check_dataset_instances_key_fast (line 144) | def check_dataset_instances_key_fast(file_path: str, instances_key: str,...
  function answer_extraction (line 155) | def answer_extraction(response, answer_type=None):  # use this funtion t...
  function process_image_flag (line 276) | def process_image_flag(text, image_flag="<ImageHere>"):
  class VLLMInferenceResultWithInput (line 288) | class VLLMInferenceResultWithInput(TypedDict):
  class RewardModelInferenceResultWithInput (line 293) | class RewardModelInferenceResultWithInput(TypedDict):

FILE: src/lmflow/utils/debug/profiler.py
  class Timer (line 5) | class Timer:
    method __init__ (line 6) | def __init__(self, name):
    method start (line 11) | def start(self, tag):
    method end (line 14) | def end(self, tag):
    method get_runtime (line 18) | def get_runtime(self, tag):
    method show (line 21) | def show(self):
    method _to_readable (line 25) | def _to_readable(self):

FILE: src/lmflow/utils/deprecated.py
  function deprecated_args (line 13) | def deprecated_args(**deprecated_params: dict[str, Any]):

FILE: src/lmflow/utils/envs.py
  function is_accelerate_env (line 15) | def is_accelerate_env():
  function get_device_name (line 22) | def get_device_name() -> str:
  function get_torch_device (line 33) | def get_torch_device() -> any:

FILE: src/lmflow/utils/llava_conversation_lib.py
  class SeparatorStyle (line 6) | class SeparatorStyle(Enum):
  class Conversation (line 17) | class Conversation:
    method get_prompt (line 31) | def get_prompt(self):
    method append_message (line 109) | def append_message(self, role, message):
    method get_images (line 112) | def get_images(self, return_pil=False):
    method to_gradio_chatbot (line 165) | def to_gradio_chatbot(self):
    method copy (line 199) | def copy(self):
    method dict (line 211) | def dict(self):

FILE: src/lmflow/utils/model.py
  function check_homogeneity (line 12) | def check_homogeneity(model_args_list: list[ModelArguments]) -> bool:

FILE: src/lmflow/utils/multimodal.py
  function update_custom_config (line 8) | def update_custom_config(config, model_args):
  function load_llava_pretrain_model (line 23) | def load_llava_pretrain_model(model, checkpoint_path):
  function adapt_llava_model_to_lmflow_type (line 38) | def adapt_llava_model_to_lmflow_type(state_dict):

FILE: src/lmflow/utils/position_interpolation/llama_rope_scaled_monkey_patch.py
  class CondenseRotaryEmbedding (line 8) | class CondenseRotaryEmbedding(torch.nn.Module):
    method __init__ (line 9) | def __init__(self, dim, pi_ratio, ntk_ratio, max_position_embeddings=2...
    method forward (line 31) | def forward(self, x, seq_len=None):
  function replace_llama_with_condense (line 50) | def replace_llama_with_condense(pi_ratio, ntk_ratio):

FILE: src/lmflow/utils/protocol.py
  function union_python_dict (line 34) | def union_python_dict(dict1: dict, dict2: dict):
  function union_tensor_dict (line 52) | def union_tensor_dict(tensor_dict1: TensorDict, tensor_dict2: TensorDict...
  function _array_equal (line 68) | def _array_equal(array1: np.ndarray, array2: np.ndarray, visited: Set[in...
  function _deep_equal (line 95) | def _deep_equal(a: Any, b: Any, visited: Set[int]) -> bool:
  function union_numpy_dict (line 131) | def union_numpy_dict(tensor_dict1: Dict[str, np.ndarray], tensor_dict2: ...
  function list_of_dict_to_dict_of_list (line 145) | def list_of_dict_to_dict_of_list(list_of_dict: List[dict]):
  function collate_fn (line 157) | def collate_fn(x: List["DataProtoItem"]):
  function get_tensordict (line 170) | def get_tensordict(tensor_dict: Dict[str, Union[torch.Tensor, list]], no...
  class DataProtoItem (line 250) | class DataProtoItem:
  class DataProto (line 257) | class DataProto:
    method __post_init__ (line 269) | def __post_init__(self):
    method __len__ (line 273) | def __len__(self):
    method __getitem__ (line 282) | def __getitem__(self, item):
    method __getstate__ (line 316) | def __getstate__(self):
    method __setstate__ (line 326) | def __setstate__(self, data):
    method save_to_disk (line 336) | def save_to_disk(self, filepath):
    method load_from_disk (line 341) | def load_from_disk(filepath) -> "DataProto":
    method print_size (line 346) | def print_size(self, prefix=""):
    method check_consistency (line 364) | def check_consistency(self):
    method from_single_dict (line 390) | def from_single_dict(cls, data: Dict[str, Union[torch.Tensor, np.ndarr...
    method from_dict (line 406) | def from_dict(
    method from_tensordict (line 453) | def from_tensordict(
    method to (line 493) | def to(self, device) -> "DataProto":
    method select (line 507) | def select(self, batch_keys=None, non_tensor_batch_keys=None, meta_inf...
    method select_idxs (line 542) | def select_idxs(self, idxs):
    method slice (line 582) | def slice(self, start=None, end=None, step=None):
    method pop (line 628) | def pop(self, batch_keys=None, non_tensor_batch_keys=None, meta_info_k...
    method rename (line 661) | def rename(self, old_keys=None, new_keys=None) -> "DataProto":
    method union (line 688) | def union(self, other: "DataProto") -> "DataProto":
    method make_iterator (line 707) | def make_iterator(self, mini_batch_size, epochs, seed=None, dataloader...
    method padding (line 747) | def padding(self, padding_size, padding_candidate=""):
    method chunk (line 762) | def chunk(self, chunks: int) -> list["DataProto"]:
    method split (line 803) | def split(self, split_size: int) -> list["DataProto"]:
    method concat (line 815) | def concat(data: list["DataProto"]) -> "DataProto":
    method reorder (line 861) | def reorder(self, indices):
    method repeat (line 869) | def repeat(self, repeat_times=2, interleave=True):
    method unfold_column_chunks (line 913) | def unfold_column_chunks(self, n_split: int, split_keys: Optional[list...
    method sample_level_repeat (line 952) | def sample_level_repeat(self, repeat_times):
    method to_tensordict (line 1000) | def to_tensordict(self) -> TensorDict:
    method get_data_info (line 1024) | def get_data_info(self) -> str:
    method _get_type_info (line 1051) | def _get_type_info(self, value):

FILE: src/lmflow/utils/test_utils.py
  function compare_model (line 7) | def compare_model(

FILE: src/lmflow/utils/versioning.py
  function get_python_version (line 13) | def get_python_version():
  function _is_package_available (line 17) | def _is_package_available(package_name: str, skippable: bool = False):
  function _is_packages_available (line 33) | def _is_packages_available(packages: Union[List[str], List[Tuple[str, bo...
  function is_package_version_at_least (line 42) | def is_package_version_at_least(package_name, min_version):
  function is_gradio_available (line 52) | def is_gradio_available():
  function is_ray_available (line 56) | def is_ray_available():
  function is_vllm_available (line 60) | def is_vllm_available():
  function is_sglang_available (line 64) | def is_sglang_available():
  function is_flash_attn_available (line 68) | def is_flash_attn_available():
  function is_flask_available (line 72) | def is_flask_available():
  function is_trl_available (line 76) | def is_trl_available():
  function is_multimodal_available (line 80) | def is_multimodal_available():
  function is_deepspeed_available (line 84) | def is_deepspeed_available():
  function get_lmflow_dir (line 88) | def get_lmflow_dir(return_src_dir: bool = False) -> Path:

FILE: tests/conftest.py
  function pytest_configure (line 4) | def pytest_configure(config: pytest.Config):

FILE: tests/datasets/conftest.py
  function dataset_inference_conversation (line 8) | def dataset_inference_conversation() -> Dataset:
  function dataset_inference_conversation_batch (line 17) | def dataset_inference_conversation_batch() -> Dataset:

FILE: tests/datasets/test_dataset.py
  class DatasetTest (line 24) | class DatasetTest(unittest.TestCase):
    method test_init (line 25) | def test_init(self):
    method test_create_from_dict (line 36) | def test_create_from_dict(self):
    method test_create_from_dict_bad_type (line 47) | def test_create_from_dict_bad_type(self):

FILE: tests/models/test_auto_model.py
  class AutoModelTest (line 12) | class AutoModelTest(unittest.TestCase):
    method test_get_decoder_model (line 13) | def test_get_decoder_model(self):
    method test_get_unsupported_model (line 36) | def test_get_unsupported_model(self):

FILE: tests/models/test_hf_decoder_model.py
  function make_gt_from_conversation_ids (line 286) | def make_gt_from_conversation_ids(conversation_ids):
  function make_gt_from_conversation_ids_batch (line 299) | def make_gt_from_conversation_ids_batch(batched_conversation_ids):
  class HFDecoderModelTest (line 309) | class HFDecoderModelTest(unittest.TestCase):
    method _test_tokenize (line 311) | def _test_tokenize(
    method test_tokenize_text_only (line 341) | def test_tokenize_text_only(self):
    method test_tokenize_text_only_multiple (line 363) | def test_tokenize_text_only_multiple(self):
    method test_tokenize_text2text (line 384) | def test_tokenize_text2text(self):
    method test_tokenize_conversation (line 407) | def test_tokenize_conversation(self):
    method test_tokenize_conversation_multiple (line 512) | def test_tokenize_conversation_multiple(self):
    method test_encode (line 629) | def test_encode(self):
    method test_decode (line 640) | def test_decode(self):
    method test_inference (line 651) | def test_inference(self):

FILE: tests/models/test_tool_inferencer.py
  class ToolInferencerTest (line 15) | class ToolInferencerTest(unittest.TestCase):
    method set_up (line 16) | def set_up(self):
    method test_code_exec_1 (line 22) | def test_code_exec_1(self, code=CODE_1, expected_output=RES_1):
    method test_code_exec_2 (line 26) | def test_code_exec_2(self, code=CODE_2):

FILE: tests/pipeline/test_auto_pipeline.py
  class AutoPipelineTest (line 12) | class AutoPipelineTest(unittest.TestCase):
    method test_get_evaluator_pipeline (line 13) | def test_get_evaluator_pipeline(self):
    method test_get_finetuner_pipeline (line 21) | def test_get_finetuner_pipeline(self):
    method test_get_inferencer_pipeline (line 29) | def test_get_inferencer_pipeline(self):
    method test_get_unsupported_pipeline (line 37) | def test_get_unsupported_pipeline(self):

FILE: tests/pipeline/test_finetuner_distributed_loss.py
  class AccelerateBackend (line 123) | class AccelerateBackend(Enum):
  class DeepSpeedZeroStage (line 128) | class DeepSpeedZeroStage(Enum):
  class PeftMethod (line 132) | class PeftMethod(Enum):
  class TestDtype (line 139) | class TestDtype(Enum):
  class TestFinetunerBase (line 158) | class TestFinetunerBase(TestCasePlus):
    method setUp (line 161) | def setUp(self):
    method _make_cmd (line 167) | def _make_cmd(self, run_name: str, args: list[list[str]]) -> list[str]:
    method _log_std (line 185) | def _log_std(self, res: _RunOutput):
    method _load_trainer_state (line 194) | def _load_trainer_state(self, output_dir: str) -> dict:
    method _run_with_accelerate (line 199) | def _run_with_accelerate(
    method _run_with_deepspeed (line 234) | def _run_with_deepspeed(
    method _compare_loss (line 269) | def _compare_loss(self, trainer_state1: dict, trainer_state2: dict):
    method test_loss_accelerate_dsz3_vs_fsdp (line 285) | def test_loss_accelerate_dsz3_vs_fsdp(self):
    method test_loss_deepspeed_z3_vs_accelerate_dsz3 (line 298) | def test_loss_deepspeed_z3_vs_accelerate_dsz3(self):
  class TestFinetunerLora (line 308) | class TestFinetunerLora(TestFinetunerBase):
    method test_loss_accelerate_dsz3_vs_fsdp (line 313) | def test_loss_accelerate_dsz3_vs_fsdp(self):
    method test_loss_deepspeed_z3_vs_accelerate_dsz3 (line 330) | def test_loss_deepspeed_z3_vs_accelerate_dsz3(self):
  class TestFinetunerQlora (line 344) | class TestFinetunerQlora(TestFinetunerBase):
    method test_loss_accelerate_dsz3_vs_fsdp (line 351) | def test_loss_accelerate_dsz3_vs_fsdp(self):
    method test_loss_deepspeed_z3_vs_accelerate_dsz3 (line 370) | def test_loss_deepspeed_z3_vs_accelerate_dsz3(self):
  class TestFinetunerCustomOptim (line 386) | class TestFinetunerCustomOptim(TestFinetunerBase):
    method test_loss_accelerate_dsz3_vs_fsdp (line 391) | def test_loss_accelerate_dsz3_vs_fsdp(self):
    method test_loss_deepspeed_z3_vs_accelerate_dsz3 (line 404) | def test_loss_deepspeed_z3_vs_accelerate_dsz3(self):

FILE: tests/pipeline/test_memory_safe_vllm_inferencer.py
  class MemorySafeVLLMInferencerTest (line 40) | class MemorySafeVLLMInferencerTest:
    method test_init (line 41) | def test_init(self):
    method test_inference (line 51) | def test_inference(self):
    method test_inference_detokenize (line 64) | def test_inference_detokenize(self):
    method summary (line 82) | def summary(self):

FILE: tests/pipeline/test_sglang_infernecer.py
  function sglang_test_model_args (line 14) | def sglang_test_model_args() -> ModelArguments:
  function sglang_test_inferencer_args (line 19) | def sglang_test_inferencer_args() -> InferencerArguments:
  function test_sglang_inferencer (line 30) | def test_sglang_inferencer(

FILE: tests/utils/test_conversation_formatter.py
  class StringFormatterTest (line 6) | class StringFormatterTest(unittest.TestCase):
    method test_format_string_component (line 7) | def test_format_string_component(self):

FILE: tests/utils/test_conversation_template.py
  class EmptyConversationTemplateTest (line 103) | class EmptyConversationTemplateTest(unittest.TestCase):
    method setUp (line 104) | def setUp(self):
    method test_encode_conversation_singleturn_llama2 (line 109) | def test_encode_conversation_singleturn_llama2(self):
    method test_encode_conversation_multiturn_llama2 (line 115) | def test_encode_conversation_multiturn_llama2(self):
  class Llama2ConversationTemplateTest (line 122) | class Llama2ConversationTemplateTest(unittest.TestCase):
    method setUp (line 123) | def setUp(self):
    method test_encode_conversation_singleturn (line 128) | def test_encode_conversation_singleturn(self):
    method test_encode_conversation_multiturn (line 137) | def test_encode_conversation_multiturn(self):
  class Qwen2ConversationTemplateTest (line 147) | class Qwen2ConversationTemplateTest(unittest.TestCase):
    method setUp (line 148) | def setUp(self):
    method test_encode_conversation_singleturn (line 153) | def test_encode_conversation_singleturn(self):
    method test_encode_conversation_multiturn (line 162) | def test_encode_conversation_multiturn(self):

FILE: tests/utils/test_data_utils.py
  class DataUtilsTest (line 56) | class DataUtilsTest(unittest.TestCase):
    method test_load_data (line 57) | def test_load_data(self):
    method test_batchlize (line 70) | def test_batchlize(self):
    method test_answer_extraction (line 80) | def test_answer_extraction(self):