* Adding qlora config for Mistral
Contains fix for Mistral FA issue - ValueError: You are attempting to perform batched generation with padding_side='right' this may lead to unexpected behaviour for Flash Attention version of Mistral. Make sure to call tokenizer.padding_side = 'left' before tokenizing the input.
Fix for now is to set sample_packing: true and pad_to_sequence_len: true
* Renamed to qlora.yml
* Allow usage of native Mistral FA when no sample_packing
* fix: do not apply custom patch when sample_pack off
* chore: lint
* chore: pin transformer to v4.35.0.dev0
* fix: split sample_packing to separate test
I'm using the Axolotl script to train models on https://modal.com serverless GPUs. Unfortunately, their environment seems to have some kind of bug where if I try to run `datasets.filter` with too high a `num_proc`, it throws an error and dies.
This PR adds a new configuration option `dataset_processes`, which lets you explicitly set the number of processes used to map/filter the dataset. If not included, this defaults to the current behavior of setting that to `os.cpu_count()`.
* Fix(cfg): Check save_strategy cfg conflict with save_steps
* Fix(cfg): Check evaluation_strategy cfg conflict with eval_steps
* chore: add extra check for steps only
* add mistral monkeypatch
* add arg for decoder attention masl
* fix lint for duplicate code
* make sure to update transformers too
* tweak install for e2e
* move mistral patch to conditional
* Fix bug in dataset loading
This fixes a bug when loading datasets. `d.data_files` is a list, so it cannot be directly passed to `hf_hub_download`
* Check type of data_files, and load accordingly
* use fastchat conversations template
* require fastchat (fschat) pip install
* handle roles dynamically from conversation
* tweak fastchat conversation with a monkeypatch to get individual turns
* fix up so it works with multiple conversation styles, and don't strip the turns
* fix sharegpt fixture now that we're using a more correct tokenization
* use a new prompter and support fastchat conversation type
* use sharegpt from prompt strategies now
* update docs, add chatml template
* add a newline after im_end token
* ensure we correctly set system message
* update per PR feedback to handle deprecated sharegpt types
* don't add duplicate wandb req
* make sharegpt fields configurable from yml
* llama2 fixes
* don't fail fatally when turns are improper
* attention_mask not needed for training
* specifically don't use attention mask for phi
* use a different check for phi
* small fixes since phi removed some values from their config
* Feat: Add support for upstream FA2
* chore: add is_falcon_derived_model: true to examples
* chore: add config to readme for documentation
* feat: add extra model types
* fix: remove old falcon flash patch
* chore: pin transformers and accelerate
