* fix for parallelism config from trainer
* fix handling of parallelism_config w accelerate
* add todo for removal
* update to latest axolotl-contribs-mit for optimizer fix too
* synchronize training after checkpoint save
* dir spelling
* use latest accelerate main
* fix to not use partial state parallelism_config
* more fixeS
* use most recent accelerate fix
* fix cpu_ram_efficient_loading to meta devices from rank 0 to prevent CPU RAM oom
* improve handling of broadcasting fsdp2 state dict
* support for openai chat template with thinking key as the reasoning trace
* address PR feedback
* refactor to remove dependency on PartialState for parallelism config
* bump accelerate, gptoss fixes
* limit meta fixes to fsdp2 for now
* fixes for gpt oss
* fixup examples, don't use cpu-ram-efficient-loading for now
* remove problematic barrier
* patch parallelism config
* reorder comparison
* device mesh fixes
* make pure CP work
* lint
* add kernels for gpt oss models
* add support for gpt-oss
* typo incorrect package
* fix: layout for configs and added wandb/epochs
* add gptoss example w offload and set moe leaf for z3
* add support for Mxfp4Config from yaml
* update yaml to use official model
* fix lora and don't allow triton to go above 3.3.1
* fix lr and tweak vram use
* fix range for triton since pinned wasn't compatible with toch 2.6.0
* update cce with gpt oss patches
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Co-authored-by: NanoCode012 <nano@axolotl.ai>
* Add support for Dion optimizer
* dion training kwargs
* fix var names
* no dion 8bit for now
* use updated axolotl-contribs-mit for dion optimizer
* add smoke test for dion optimizer
* add docs
* fix typo during edits
* fix test to not remove load in 8bit
* fix: deepcopy lr in RexLR scheduler.
This fixes a problem where when the lr is a scalar tensor, the base_lrs in the get_lr function end up being references to the current learning rate, rather than the correct initial learning rate.
See also related pytorch PR https://github.com/pytorch/pytorch/pull/127190/
* fix: add missing torch.Tensor import
* jagged lr restart scheudler
var name fix
make sure to create scheduler first
* wire things together
* more fixes
* fix for nesting scheduler and first anneal phase
* no need for relora trainer anymore since we've generalized the relora scheduler
* remove redundant relora scheduler and lint
* update relora e2e test for updated params
* need restart steps for relora test
* update quarto docs for dropped relora trainer
* update example yaml
* drop verbose arg
* min lr scale support for jagged lr
* don't let min_lr be nonetype
* cleanup args
* feat(doc): add vastai link
* feat: add cloud providers to readme for more visibility
* add prime intellect, remove Modal as sponsor
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Co-authored-by: Wing Lian <wing@axolotl.ai>
* make TiledMLP work with FSDP
* cleanup/gc at start of train to prevent large VRAM spike
* chore: lint
* generic function for non-deepspeed training
* unify patch to fix imports
* update readme for ALST and add examples
* make deepspeed attribute on params check more robust
* update with new info from PR review
