# download the training dataset (FineWeb-Edu 100B token) .bin data shards # note: this is a total of 1001 data shards. If you only want to test things # out and don't want to do an actual run, feel free to append the number of # training shards to download (e.g. for just 1...
Total number of parameters in billions: 0.35 Number of parameters in most loaded shard in billions: 0.3536 Theoretical memory footprints: weight andoptimizer=6069.97 MB [2024-09-12 16:38:32] iteration 10/ 2000 | consumed samples: 640 | elapsed time per iteration (ms): 2792.4 | learning rate...
查看GPT2 模型的细节: # 打印模型结构print(model)# 计算模型的总参数数量total_params=sum(p.numel()forpinmodel.parameters())print("Total number of parameters: ",total_params)# 计算可训练的参数数量trainable_params=sum(p.numel()forpinmodel.parameters()ifp.requires_grad)print("Number of trainable ...
> number of parameters on model parallel rank 0: 354871296 Optimizer = FusedAdam learning rate decaying cosine WARNING: could not find the metadata file checkpoints/gpt2_345m/latest_checkpointed_iteration.txt will not load any checkpoints and will start from random Partition Activations False and ...
base_model.num_parameters # (wte): Embedding(50262,768) # (wpe): Embedding(1024,768) 输出 <bound method ModuleUtilsMixin.num_parameters of GPT2LMHeadModel( (transformer): GPT2Model( (wte): Embedding(50257,768) (wpe): Embedding(1024,768) ...
> number of parameters on (tensor, pipeline) model parallel rank (0, 0): 354871296 loading release checkpoint from /home/ma-user/work/Megatron-LM/work/checkpoint/gpt2_345m Warning: since the loaded file is not a zipfile, only "torch.device" and "str" type parameters are currently ...
print('number of parameters: {}'.format(num_parameters))multi_gpu = False full_len = 0 print('calculating total steps') for i in tqdm(range(num_pieces)): with open(tokenized_data_path + 'tokenized_train_{}.txt'.format(i), 'r') as f: ...
The original Transformer Model had around 110 million parameters. GPT-1 adopted the size and with GPT-2 the number of parameters was enhanced to 1.5 billion. With GPT-3, the number of parameters was boosted to 175 billion, making it the largest neural network....
len=400, warmup_steps=200, gpt2_type="gpt2", output_dir=".", output_prefix="wreckgar", test_mode=False,save_model_on_epoch=False,): acc_steps = 100 device=torch.device("cuda") model = model.cuda() model.train() optimizer = AdamW(model.parameters(), lr=lr...
print('number of parameters: {}'.format(num_parameters)) multi_gpu = False full_line = '' full_len = 0 print('calculating total steps') for i in tqdm(range(num_pieces)): with open(tokenized_data_path + 'tokenized_train_{}.txt'.format(i), 'r') as f: full_line += f.read(...