Upload modelforseminat_v5.py with huggingface_hub

modelforseminat_v5.py

@@ -20,36 +20,12 @@ from transformers.utils import LossKwargs
 from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
 
 from torch.nn.functional import cosine_similarity
-import time
-import os
-import sys
-import json
 import pdb
-import torch.distributed as dist
-from tqdm import tqdm
-from torch.utils.data.distributed import DistributedSampler
-import transformers
-import pickle
 from dataset import *
-# from peft import (get_peft_model, PeftModel)
-import random
-from config import *
-from datasets import Dataset, DatasetDict, load_dataset
-import wandb
-import argparse
 import torch
-import torch.nn as nn
 import torch.nn.functional as F
-import torch.optim as optim
 import functools
-from torch.optim.lr_scheduler import StepLR
-import torch.nn.functional as F
 import torch.distributed as dist
-import torch.multiprocessing as mp
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.utils.data.distributed import DistributedSampler
-from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
-    checkpoint_wrapper, CheckpointImpl)
 from torch.distributed.fsdp import (
     FullyShardedDataParallel as FSDP,
     MixedPrecision,
@@ -1524,13 +1500,15 @@ class Olmo2ForCausalLMForSemiNAT(Olmo2ForCausalLM):
     ) -> Union[GenerateOutput, torch.LongTensor]:
 
         # 1. Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
-        self._validate_model_class()
+        self._validate_model_class() #能进行generate的模型
         tokenizer = kwargs.pop(
             "tokenizer",
             None)  # Pull this out first, we only use it for stopping criteria
         assistant_tokenizer = kwargs.pop(
             "assistant_tokenizer", None)  # only used for assisted generation
 
+
+        pdb.set_trace()
         generation_config, model_kwargs = self._prepare_generation_config(
             generation_config, **kwargs)