Transformer的Trainer API训练出现learning_rate一直为0,以及保存权重未更新? xihuichen all in llm版本情况: transformers 4.33.1 deepspeed 0.9.3 详细问题1: 出现如下warning: tried to get lr value before scheduler/optimizer started stepping, returning lr=0 #134 详细问题2: 保存的lora权重一直是初始值...
大家会好奇我们用的是什么模型,好像想要解这个问题,需要一个很 fancy 的模型架构。其实我们用的模型非常小而且朴实,参数加起来才1.5B,一张 T4就能跑,无 lora 的 training 才用了 13G 的 vram。 模型的架构是一个 freeze 的 CLIP 配一个要 tune 的 T5,之前一个大哥 pre-train 的,模型名字叫做 AutoUI。模型...
特别是online RL,所有data都是边训边收集的,如果model需要成倍的数据,那么collect也会需要成倍的数据,这需要很多机器一起跑collection。如果想偷懒少收集一点,那得调learning rate,可能更痛苦。试想你跑一轮实验需要一个星期,还要不要毕业了。另一个原因是穷。这个就不太需要解释了。作为一名合格的科研乞丐,我深刻...
It's impossible to understand where we're going without first understanding how we got here - and it's impossible to understand how we got here without understanding these constraints, which have always governed the rate of progress. By understanding them, we can also explore a few related que...
from transformers import TrainingArguments from peft import LoraConfig from trl import RewardTrainer training_args = TrainingArguments( output_dir="./train_logs", max_steps=1000, per_device_train_batch_size=4, gradient_accumulation_steps=1, learning_rate=1.41e-5, optim="adamw_torch", save_...
Learning to Modulate pre-trained Models in RL (Decision Transformer, LoRA, Fine-tuning) - ml-jku/L2M
(args.train_batch_size),gradient_accumulation_steps=1,learning_rate=float(args.learning_rate),warmup_steps=args.warmup_steps,num_train_epochs=args.num_train_epochs,evaluation_strategy="epoch",fp16=True,per_device_eval_batch_size=args.eval_batch_size,generation_max_length=128,logging_steps=25,...
The proposed autoencoder model is trained and evaluated with LoRa samples generated by simulation, and it is shown to be a high performer by comparing with the traditional LoRaWAN in terms of Bit Error Rate (BER) and Packet Success Rate (PSR) measurements....
230417 A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Instruction Following Large Language Model #instruct 230417 Low-code LLM #prompt 230418 UniMax #multilingual 230419 A Theory on Adam Instability in Large-Scale Machine Learning #optimizer 230421...
f'--learning_rate=0.0001', '--seed=42', '--use_lora', f'--rank=4', f'--cfg', f'--allow_tf32', f'--num_epochs=200', f'--save_freq=1', f'--reward_fn=faceid_retina', f'--target_image_dir={os.path.relpath(images_save_path,pwd)}', ...