够快！爆火的ChatGPT等价开源项目来了，网友：我担心跑不起来

$ pip install palm-rlhf-pytorch

import torchfrom palm_rlhf_pytorch import PaLMpalm = PaLM(    num_tokens = 20000,    dim = 512,    depth = 12).cuda()seq = torch.randint(0, 20000, (1, 2048)).cuda()loss = palm(seq, return_loss = True)loss.backward()# after much training, you can now generate sequencesgenerated = palm.generate(2048) # (1, 2048)

import torchfrom palm_rlhf_pytorch import PaLM, RewardModelpalm = PaLM(    num_tokens = 20000,    dim = 512,    depth = 12,    causal = False)reward_model = RewardModel(    palm,    num_binned_output = 5 # say rating from 1 to 5).cuda()# mock dataseq = torch.randint(0, 20000, (1, 1024)).cuda()prompt_mask = torch.zeros(1, 1024).bool().cuda() # which part of the sequence is prompt, which part is responselabels = torch.randint(0, 5, (1,)).cuda()# trainloss = reward_model(seq, prompt_mask = prompt_mask, labels = labels)loss.backward()# after much trainingreward = reward_model(seq, prompt_mask = prompt_mask)

import torchfrom palm_rlhf_pytorch import PaLM, RewardModel, RLHFTrainer# load your pretrained palmpalm = PaLM(    num_tokens = 20000,    dim = 512,    depth = 12).cuda()palm.load('./path/to/pretrained/palm.pt')# load your pretrained reward modelreward_model = RewardModel(    palm,    num_binned_output = 5).cuda()reward_model.load('./path/to/pretrained/reward_model.pt')# ready your list of prompts for reinforcement learningprompts = torch.randint(0, 256, (50000, 512)).cuda() # 50k prompts# pass it all to the trainer and traintrainer = RLHFTrainer(    palm = palm,    reward_model = reward_model,    prompt_token_ids = prompts)trainer.train(num_episodes = 50000)# then, if it succeeded...# generate say 10 samples and use the reward model to return the best oneanswer = trainer.generate(2048, prompt = prompts[0], num_samples = 10) # (<= 2048,)