* hf offline decorator for tests to workaround rate limits
* fail quicker so we can see logs
* try new cache name
* limit files downloaded
* phi mini predownload
* offline decorator for phi tokenizer
* handle meta llama 8b offline too
* make sure to return fixtures if they are wrapped too
* more fixes
* more things offline
* more offline things
* fix the env var
* fix the model name
* handle gemma also
* force reload of modules to recheck offline status
* prefetch mistral too
* use reset_sessions so hub picks up offline mode
* more fixes
* rename so it doesn't seem like a context manager
* fix backoff
* switch out tinyshakespeare dataset since it runs a py script to fetch data and doesn't work offline
* include additional dataset
* more fixes
* more fixes
* replace tiny shakespeaere dataset
* skip some tests for now
* use more robust check using snapshot download to determine if a dataset name is on the hub
* typo for skip reason
* use local_files_only
* more fixtures
* remove local only
* use tiny shakespeare as pretrain dataset and streaming can't be offline even if precached
* make sure fixtures aren't offline
improve the offline reset
try bumping version of datasets
reorder reloading and setting
prime a new cache
run the tests now with fresh cache
try with a static cache
* now run all the ci again with hopefully a correct cache
* skip wonky tests for now
* skip wonky tests for now
* handle offline mode for model card creation
* add mhenrichsen/alpaca_2k_test with revision dataset download fixture for flaky tests
* log slowest tests
* pin pynvml==11.5.3
* fix load local hub path
* optimize for speed w smaller models and val_set_size
* replace pynvml
* make the resume from checkpoint e2e faster
* make tests smaller
* Add example YAML file for training Mistral using DPO
* added deduplication code
* Add exact deduplication feature and update examples
* Improve deduplication for train/eval overlap
Changed the deduplication function to use a more memory-efficient hashing method. Applied Git suggestions to improve clarity and maintainability.\n\nThe deduplication now handles cases where train and eval datasets have overlapping elements.
* Improve deduplication for train/eval overlap
Changed the deduplication function to use a more memory-efficient hashing method. Applied Git suggestions to improve clarity and maintainability.\n\nThe deduplication now handles cases where train and eval datasets have overlapping elements.
* Apply suggestions from code review
To handle the original case where we do not do deduplication
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* Improve false collision detection to ensure dataset integrity
- Added test cases to simulate and verify handling of forced hash collisions between datasets.
- Ensured that datasets with identical hashes but different content are correctly identified, preventing incorrect deduplication.
- Updated unit tests to include scenarios where collisions occur across both training and evaluation datasets, as well as within a single dataset.
* Moved the constants file to the tests folder
- Relocated `constants.py` to the `tests` folder to improve modularity and maintain a clear separation between source and test files.
- Renamed `cicd/tests.py` to `cicd/cicd_tests.py` to resolve a conflict with `tests/__init__.py`, which caused Mypy to fail due to duplicate module names.
- Updated all references to `cicd.tests` in the codebase to `cicd.cicd_tests` to reflect the renaming and ensure compatibility.
- These changes ensure Mypy passes the pre-commit hook and maintain alignment with the project's structure.
* revert some changes from previous commit and fix relative import
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Co-authored-by: Wing Lian <wing.lian@gmail.com>
Co-authored-by: Wing Lian <wing@axolotl.ai>
* upgrade liger to 0.3.1
* update docs and example
* skip duplicate code check
* Update src/axolotl/integrations/liger/args.py
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* Update README.md
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* add logging
* chore: lint
* add test case
* upgrade liger and transformers
* also upgrade accelerate
* use kwargs to support patch release
* make sure prepared path is empty for test
* use transfromers 4.46.1 since 4.46.2 breaks fsdp
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Co-authored-by: NanoCode012 <nano@axolotl.ai>
* remove skipped test
* use mean_resizing_embeddings with qlora and added tokens
* use </s> as pad_token to prevent resize of embeddings
* make sure local hub test saves to a tmp dir
* use Path so concatenation works
* make sure to use tmp_ds_path for data files
* Add support for `revision` dataset parameter
* only use revision on hf hub backed datasets
* use revision tied to head
* set download to use revision
* feat: add config to model validator class
* feat: add revision config to RL and tests for it
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Co-authored-by: Wing Lian <wing.lian@gmail.com>
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* wrap prepared_ds_path in str() to avoid TypeError in fsspec package
`fsspec` calls `if "::" in path` on `prepared_ds_path`, which will throw an error if it is a `PosixPath` object.
* update test too
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Co-authored-by: Wing Lian <wing.lian@gmail.com>
