model=GPT2LMHeadModel(config)model_size=sum(t.numel()fortinmodel.parameters())print(f"GPT-2 size: {model_size/1000**2:.1f}M parameters")GPT-2size:124.2Mparameters 我们的模型有124M参数。在进行训练之前,需要设置一个data collator来把数据处理为模型输入格式。Huggingface也提供了DataCollatorForLangua...
defprint_trainable_parameters(model):trainable_params=0all_param=0for_,paraminmodel.named_parameters():all_param+=param.numel()ifparam.requires_grad:trainable_params+=param.numel()print(f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params ...
optimizer=AdamW(model.parameters(),lr=5e-5)num_epochs=3num_training_steps=num_epochs*len(train_dataloader)# numofbatches*numofepochs lr_scheduler=get_scheduler('linear',optimizer=optimizer,# scheduler是针对optimizer的lr的 num_warmup_steps=0,num_training_steps=num_training_steps)print(num_training...
from diffusersimportDDPMScheduler, UNet2DModel from matplotlibimportpyplot as plt device = torch.device("cuda"iftorch.cuda.is_available()else"cpu") print(f'Using device: {device}') 数据 在这里,我们将使用一个非常小的经典数据集 mnist 来进行测试。如果您想在不改变任何其他内容的情况下给模型一个稍...
optimizer=AdamW(model.parameters())loss=model(**batch).loss #这里的 loss 是直接根据 batch 中提供的 labels 来计算的,回忆:前面章节查看 model 的输出的时候,有loss这一项 loss.backward()optimizer.step() 从Huggingface Hub中加载数据集 这里,我们使用MRPC数据集,它的全称是Microsoft Research Paraphrase Corp...
tokenizer = AutoTokenizer.from_pretrained(model_id) print(f"Train dataset size:{len(dataset['train'])}") print(f"Test dataset size:{len(dataset['test'])}") # Train dataset size: 287113 # Test dataset size: 11490 我们在配置文件中定义了一个prompt_template,其可用于来构建指令提示,以提高我...
no_grad(): for param in mha.parameters(): nn.init.normal_(param, std=0.1) # Initialize weights to be non-negligibleoutput, _ = mha(W_q(embeddings), W_k(embeddings), W_v(embeddings))dog1_out = output[0, 2]dog2_out = output[0, 5]print(f"Dog output identical?: {torch....
# modules_to_save=modules_to_save, bias="none", task_type="CAUSAL_LM", ) model = get_peft_model(model, config) model.print_trainable_parameters() # Be more transparent about the % of trainable params. print(model.get_nb_trainable_parameters()) print(model.num_parameters(only_trainable=...
parameters for [['model.layers.0.self_attn.q_proj.weight', 'model.layers.0.self_attn.k_proj.weight', 'model.layers.0.self_attn.v_proj.weight', 'model.layers.0.self_attn.o_proj.weight', 'model.layers.0.mlp.gate_proj.weight', 'model.layers.0.mlp.up_proj.weight', 'model.layers...
AutoModel架构的输出 from transformers import AutoModelForSequenceClassification Model heads: Making sense out of numbers 处理model的输出,转化为概率 models 加载model 保存model Tokenizers word-based character-based subword-based tokenizer 处理多个sequences tokenizer处理完整示例 3. [Fine-tuning a pretrained ...