* [chat] strategy refactor unwrap model
* [chat] strategy refactor save model
* [chat] add docstr
* [chat] refactor trainer save model
* [chat] fix strategy typing
* [chat] refactor trainer save model
* [chat] update readme
* [chat] fix unit test
* run the base
* working on dist ppo
* sync
* detached trainer
* update detached trainer. no maker update function
* facing init problem
* 1 maker 1 trainer detached run. but no model update
* facing cuda problem
* fix save functions
* verified maker update
* nothing
* add ignore
* analyize loss issue
* remove some debug codes
* facing 2m1t stuck issue
* 2m1t verified
* do not use torchrun
* working on 2m2t
* working on 2m2t
* initialize strategy in ray actor env
* facing actor's init order issue
* facing ddp model update issue (need unwarp ddp)
* unwrap ddp actor
* checking 1m2t stuck problem
* nothing
* set timeout for trainer choosing. It solves the stuck problem!
* delete some debug output
* rename to sync with upstream
* rename to sync with upstream
* coati rename
* nothing
* I am going to detach the replaybuffer from trainer and make it a Ray Actor. Two benefits: 1. support TP trainer. 2. asynchronized buffer operations
* experience_maker_holder performs target-revolving _send_experience() instead of length comparison.
* move code to ray subfolder
* working on pipeline inference
* apply comments
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Co-authored-by: csric <richcsr256@gmail.com>
* Update ppo.py
Fix the bug of fetching wrong batch data
* Add peft model support in SFT and Prompts training
In stage-1 and stage-3, the peft model supports are added. So the trained artifacts will be only a small lora additions instead of the whole bunch of files.
* Delete test_prompts.txt
* Delete test_pretrained.txt
* Move the peft stuffs to a community folder.
* Move the demo sft to community
* delete dirty files
* Add instructions to install peft using source
* Remove Chinese comments
* remove the Chinese comments
Hongxin Liu