Comments (4)
@Ashura5 就是这个?https://github.com/OpenRLHF/OpenRLHF/blob/main/examples/scripts/train_iterative_dpo_llama.sh
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这个是在训练之前从reference model中推理数据,reward model打分
我想实现的是在训练时,每训练一步policy model,都从policy model中重新采样数据
from openrlhf.
这个是在训练之前从reference model中推理数据,reward model打分 我想实现的是在训练时,每训练一步policy model,都从policy model中重新采样数据
这个实现的就是每轮用最新的checkpoint 推理数据打分,只不过batch size 默认设置的比较大。如果要实现PPO那种完全online的会复杂一点,可以参考train_ppo_ray.py的设计
from openrlhf.
这个是在训练之前从reference model中推理数据,reward model打分 我想实现的是在训练时,每训练一步policy model,都从policy model中重新采样数据
这个实现的就是每轮用最新的checkpoint 推理数据打分,只不过batch size 默认设置的比较大。如果要实现PPO那种完全online的会复杂一点,可以参考train_ppo_ray.py的设计
好的,谢谢,我再仔细看看这一段
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Related Issues (20)
- DPO Finetuning constantly gives preference loss as 0.6931 HOT 8
- Difference between `DeepSpeedEngine.save_checkpoint()` and `DeepSpeedStrategy.save_model()` HOT 2
- DPO后的模型推理出的结果都是无序符号 HOT 1
- Support training from breakpoint HOT 3
- llama3 70B DPO example script
- where is gradient_accumulation HOT 1
- Support RLOO HOT 1
- 现在Train_PPO_llama_ray 过程中会把Actor Model切分到不同卡上吗 HOT 4
- ConnectionRefusedError: [Errno 111] Connection refused HOT 5
- packing的问题 HOT 2
- "right" padding hardcoded HOT 3
- Error while saving the model under 4bit lora HOT 2
- multinode ppo training extremely slow HOT 15
- 使用ray的时候Request Entity Too Large HOT 3
- dpo 训练显存 OOM HOT 1
- Feature: add DPO-P
- Zero stage 3 error HOT 1
- Performance of Iterative DPO? HOT 1
- Why multiplying rstd instead of dividing by rstd? HOT 1
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