Comments (5)
TODO:
- Make data loading work with RLElements & RolloutStorage
- Copy-paste ILQL loss & QVModel heads but using GPT2Model from NeoX
- Copy-paste PPO loss & inferencing from accelerate impl but using GPT2Model from neox
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We need to make sure that the top level API remains the same or near the same. I do not know how feasible this is.
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We need to make sure that the top level API remains the same or near the same. I do not know how feasible this is.
Which top level API are you referring to? neox's? or TRLX's?
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TRLX's
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Closed in favor of NeMo
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Related Issues (20)
- multigpu support for summarization ppo example HOT 3
- Support parallel reward_fn in PPO training
- resume_from_checkpoint doesn't work HOT 1
- Issue since most recent transformers update HOT 1
- Multi-GPU training errors with peft HOT 1
- Attention mask when calculating log ratio for PPO
- when i use trlx ppotrainer train a model llama 13b model, but saved huggingface mode ,but when it inference , it has some strange keys ,and the inference result did not show ,it also have no error , it seems the result disapper HOT 1
- MPT is not working
- Runtime error when running examples (ilql_sentiments_t5.py) HOT 2
- Integration of Self-Play Fine-Tuning (SPIN) Method for Enhancing Large Language Models
- RLHF text summarization diverges
- [New Feature Request] Add KTO
- Issue of tensors share memory HOT 2
- TRLX Environment customization
- Why train dataloader is not prepared by Accelerator
- Data Loader Bug when running t5_summarization_daily_cnn.py
- Crash when using save_state with deepspeed: `model.state_dict` functions incompatible with new deepspeed.
- Error when running Ray Tune to launch hyperparameter sweep HOT 1
- maybe bug in prepare & load's order HOT 1
- cannot import name 'flatten_dataclass' from 'trlx.data.ilql_types'
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