在右侧的列表中,您将看到已安装的Python解释器。选择您要为其安装rl_utils命令的解释器。在底部的 “Packages”(包)选项卡中,点击 “+” 按钮以添加新包。在弹出的对话框中,输入 “rl_utils” 并点击 “Install Package”(安装包)按钮。PyCharm将自动下载并安装rl_utils包。安装完成后,您将在 “Packages” 选...
added makefile and example module Sep 12, 2019 LICENSE Initial commit Sep 11, 2019 Makefile added command args parsing Sep 15, 2019 README.md Initial commit Sep 11, 2019 RLUtils Utils library for redis modules Packages No packages published...
dset = FeedbackDataset(df, tokenizer, max_len=512) train_loader = torch.utils.data.DataLoader(dset, batch_size=batch_size, shuffle=True) optimizer = AdamW(model.parameters(), lr=1e-5) total_steps = len(train_loader) * epochs scheduler = get_linear_schedule_with_warmup(optimizer, num_w...
wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py python collect_env.py should display OS: macOS *** (arm64) and not OS: macOS *** (x86_64) Versioning issues can cause error message of the type undefined symbol and such. For these, refer...
from transformers import ( AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer,)from trl.trainer.rloo_trainer import RLOOConfig, RLOOTrainerfrom trl.trainer.utils import SIMPLE_QUERY_CHAT_TEMPLATEbase_model_name = "EleutherAI/pythia-1b-deduped"tokenizer = AutoTokenizer.from_...
sudo apt-get install libglew-dev glew-utils 坑4:如果是FileNotFoundError: [Errno 2] No such file or directory: 'patchelf': 'patchelf', 那就安装patchelf 解决办法: 代码语言:javascript 代码运行次数:0 复制Cloud Studio 代码运行 sudo apt-get -y install patchelf 安装成功是这样的效果 image-2022...
具体的数据处理在training/utils/data/data_utils.py中,下面的代码展示了三个阶段使用的输入是什么?在第一步,即监督微调大模型,使用prompt + chosen;在第二步,即训练奖励模型时,需要使用prompt + chosen 和 prompt + rejected;在第三步,即训练RL模型,只使用prompt。
Loading Saved Graphs (Tensorflow Only) Plotter MPI Tools Core MPI Utilities MPI + PyTorch Utilities MPI + Tensorflow Utilities Run Utils ExperimentGrid Calling Experiments Etc. Acknowledgements About the Author Indices and tables¶ Index Module Index Search Page...
nn.utils.convert_parameters.vector_to_parameters(new_parameters, new_actor.parameters()) mu, std = new_actor(states) new_action_dists = torch.distributions.Normal(mu, std) ### 参数更新前后,策略的kl散度的期望(均值) ### kl_div = torch.mean(torch.distributions.kl.kl_divergence(old_action...
nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm) optimizer.step() # 7. 计算新旧策略网络的概率分布的KL散度:当KL散度大于特定阈值时,跳出该更新周期。这是为了防止策略更新过快,导致训练不稳定。 if args.target_kl is not None: ...