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()`.
* 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
* 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
* let hf trainer handle torch compile
* remove torch compile checks, include option for backend
* suppress torch errors to get further
* require min torch version of 2.1.0 for torch compile to work
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Co-authored-by: Aman Karmani <aman@tmm1.net>
* update readme to point to direct link to runpod template, cleanup install instrucitons
* default install flash-attn and auto-gptq now too
* update readme w flash-attn extra
* fix version in setup
* Add Metharme tokenizing strategy
This strategy accounts for how the Metharme JSONLs are formatted as well as adds duplicated EOS tokens which can help trim model output length.
I haven't gotten the chance to test this yet, and probably won't have the chance for quite a bit, so I'm committing this now.
* Redo Metharme tokenizing strategy
lol
* fix: oops
* Rearrange a conditional
* chore: reformat code in accordance with linter
* chore: Make lint not freak out
* chore: fix lint
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Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
* support user defined prompters, pretokenized datasets in config, local parquet, local arrow files
* fix user defined dataset types
* fix for system prompts
* fix tests
* fix checks for parquet and arrow
* aha moment that d.data_files isn't used
* add documentation for ds_type to add support for parquet and arrow
* flash attn pip
* add packaging
* add packaging to apt get
* install flash attn in dockerfile
* remove unused whls
* add wheel
* clean up pr
fix packaging requirement for ci
upgrade pip for ci
skip build isolation for requiremnents to get flash-attn working
install flash-attn seperately
* install wheel for ci
* no flash-attn for basic cicd
* install flash-attn as pip extras
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Co-authored-by: Ubuntu <mgh@mgh-vm.wsyvwcia0jxedeyrchqg425tpb.ax.internal.cloudapp.net>
Co-authored-by: mhenrichsen <some_email@hey.com>
Co-authored-by: Mads Henrichsen <mads@BrbartiendeMads.lan>
Co-authored-by: Wing Lian <wing.lian@gmail.com>
