model.to(device) optimizer = Adam(model.parameters(), lr=1e-3) model, optimizer, train_dataloader = accelerator.prepare( model, optimizer, train_dataloader, ) forepochinrange(40): model.train() forbatchintrain_dataloader: optimizer.zero_grad() outputs = model( static_categorical_features=batc...
model = Detr(lr=1e-4, lr_backbone=1e-5, weight_decay=1e-4) outputs = model(pixel_values=batch['pixel_values'], pixel_mask=batch['pixel_mask']) print(outputs.logits.shape) from pytorch_lightning import Trainer trainer = Trainer(max_steps=300, gradient_clip_val=0.1) trainer.fit(model...
该类是Trainer的继承类,允许我们在合适的处理验证操作,即使用generate()函数来根据输入预测输出。当讨论指标计算的时候,会深入聊下这个新类。 首先,我们需要加载和缓存一个实际模型,使用AutoModelAPI,如下: fromtransformersimportAutoModelForSeq2SeqLMmodel=AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint) ...
model.print_trainable_parameters() # trainable params: 18874368 || all params: 11154206720 || trainable%: 0.16921300163961817 如你所见,这里我们只训练了模型参数的 0.16%!这个巨大的内存增益让我们安心地微调模型,而不用担心内存问题。 接下来需要创建一个 DataCollator,负责对输入和标签进行填充,我们使用 🤗...
model.transformer generate commentedApr 26, 2023• edited In that case, I am not sure how either approaches 1 or 2 work. For 1, the root FSDP instance does not free its parameters after forward, so you can technically still run forward computation with them. (This is not part of FSDP...
摘要 评估和比较大语言模型 (LLMs) 是一项艰巨的任务。我们 RLHF 团队在一年前就意识到了这一点,...
What I need A way to run this code to any LLM by programmatically setting model parameters, settings, etc instead on RunPod webui
Parameters: eval_preds (tuple): A tuple containing the predicted logits and the true labels. Returns: A dictionary containing the precision, recall, F1 score and accuracy. """ pred_logits, labels = eval_preds pred_logits = np.argmax(pred_logits, axis=2) # the logits and ...
指定工具的name、description、和parameters,注意 @register_tool('my_image_gen') 中的 'my_image_...
Fine-tuning follows pre-training and adapts the model to specific tasks. During this supervised learning phase, models are trained on task-specific datasets, adjusting their parameters to make predictions aligned with the task’s requirements. The ability to fine-tune diverse tasks stems from the ...