* add phi modeling from hf
* update for packing and use new modeling class for phi
* update e2e tests for phi to use new model name
* update example phi to also use new phi model name
* use AutoModelForCausalLM for phi lora since sample packing isn't supported
* allow zero len dataset
* better handling and warning of small eval splits
* raise error if eval split is too small
* don't mess with calculating total num steps in distributed context
* fix eval_sample_packing training args logic
* isolate torch from the requirements.txt
* fix typo for removed line ending
* pin transformers and accelerate to latest releases
* try w auto-gptq==0.5.1
* update README to remove manual peft install
* pin xformers to 0.0.22
* bump flash-attn to 2.3.3
* pin flash attn to exact version
* allow overriding of model_config parameters from the YML
* remove old logging, update readme
* move the updating of model config to the load_model_config function
* add warning for deprecated rope_scaling in the root of the YML config
* use tensorboard to see if resume from checkpoint works
* make sure e2e test is either fp16 or bf16
* set max_steps and save limit so we have the checkpoint when testing resuming
* fix test parameters
* Update data.py
Change of conversation formatting type should also trigger updating the preprocessed dataset, so it should be part of the signature.
* chore: lint
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Co-authored-by: Wing Lian <wing.lian@gmail.com>
* various bugfixes
use latest tinyllama release
check if val_set_size is empty first
update sdp and xformers llama patches for updated upstream transformers
fix system prompt when no input
calculate total and total supervised tokens even when not sample packing
* add fix for when eval size is estimated to be too small
* should be len 1 for dataset length
* add catchall kwargs
* test batch sampler w varying batch lens
* wip
* multipack batchsampler wip
* wip
* fix for prepare data loader to get correct # of steps based on gpues
* lint and clean up
* calculate len estimate
* fix total num steps calc
* add options for dataloader_num_workers and dataloader_pin_memory
* remove gitbook
* support prefetch_factor for dataloader optimization
* fix the kwarg
* Update to adapt to sharegpt datasets with "assistant" rather than "gpt" as the machine answers.
* use a strict option for hanedling incorrect turn data
* chore: lint
---------
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* Update zero3.json
Take away CPU Offload by default (Slows things down horribly, better off reducing batchsize), and changes LR Scheduler to a properly decaying one
* Update zero3.json
fix something
