* 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
---------
Co-authored-by: Wing Lian <wing.lian@gmail.com>
Co-authored-by: Wing Lian <wing@axolotl.ai>
* see if unsloth installs cleanly in ci
* check unsloth install on regular tests, not sdist
* fix ampere check exception for ci
* use cached_property instead
* add an e2e test for unsloth qlora
* reduce seq len and mbsz to prevent oom in ci
* add checks for fp16 and sdp_attention
* pin unsloth to a specific release
* add unsloth to docker image too
* fix flash attn xentropy patch
* fix loss, add check for loss when using fa_xentropy
* fix special tokens for test
* typo
* test fa xentropy with and without gradient accum
* pr feedback changes
* support seperate lr for embeddings, similar to loraplus
* add test case for train w lr embedding scale
* use kwarg for optimizer
* make sure to handle the optimizer creation
* make sure to handle for embedding_lr too
* use smollm for e2e, check for embeddings lr first before wdecay
* feat: LOG warn if samples are dropped due to seq length
* feat: add drop long samples for RL
* feat: add ipo
* fix: remove num_proc for map as subprocesses are prone to die
* feat: shuffle rl dataset
* fix: support preprocess for kto
* chore: use set instead of list
* feat: add simpo
* Update `get_unpad_data` patching for multipack
* Update src/axolotl/utils/models.py
* Update src/axolotl/utils/models.py
* Add test case
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Co-authored-by: Wing Lian <wing.lian@gmail.com>
Co-authored-by: Wing Lian <wing@axolotl.ai>
* remove the bos token from dpo outputs
* don't forget to fix prompt_input_ids too
* use processing_class instead of tokenizer
* fix for processing class
* add more test cases for gradient accumulation and fix zero3
* swap out for smaller model
* fix missing return
* fix missing pad_token in config
* support concurrency for multigpu testing
* cast empty deepspeed to empty string for zero3 check
* fix temp_dir as fixture so parametrize works properly
* fix test file for multigpu evals
* don't use default
* don't use default for fsdp_state_dict_type
* don't use llama tokenizer w smollm
* also automatically cancel multigpu for concurrency
* 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>
* Allow using tokenizer's default chat template with fallbacks
Summary of changes:
1. Adds `tokenizer_default` as option for `chat_template` in
`chat_template` prompt strategy that allows using the chat template
from tokenizer's config.json
2. Allows falling back to chat templates available in axolotl if
tokenizer does not have a chat template
3. Adds a mistral chat template which supports system message - taken
from https://github.com/chujiezheng/chat_templates/blob/main/chat_templates/mistral-instruct.jinja
---
Why?
Many popular models are not trained with chatml format. As a result for
the model to correctly learn chatml we have to turn on train_on_inputs
which requires more compute and time. If we can use the model's already
learned chat template we can just learn the output tokens
---
Todo:
- Write tests
* Add tests
* Fix lint and bug post merge from main
* Add option `chat_template_jinja` to provide a jinja template
* remove custom mistral template
* Address review comments and add docs
* Update docs/dataset-formats/conversation.qmd
Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
* fix: set default to tokenizer template
* Merge branch 'main' into cj_tokenizer_default_prompt_template
* chore: remove redundant function
* fix: re-arrange enum declaration position
* fix: refactor artifact left from main merge
* feat(doc): updated config with chat template options and clarified examples
* chore: clarify doc
* chore: added example for non-default template
* chore: refactor
* fix: test
* fix: config being dropped and unittest to catch that
* chore: lint
* chore: skip duplicate
* fix: rename var after merge
* feat: add test for levy's dpo case
* fix: remove default setting on edge case where chat template overriden in dataset section
* feat: handle sharegpt deprecation better in docs
* feat: add example using fallback
* feat: handles chat_template requiring specific user/assistant order
* fix: update test based on new defaults
* fix: imported name incorrectly updated on merge
* chore: lint
* fix: update dummy message to prevent potential overlap with real content
* fix(doc): formatting
* fix: update bradleyterry to use new chat_template
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Co-authored-by: Chirag Jain <jain.chirag925@gmail.com>
* feat: support new arg num_items_in_batch
* use kwargs to manage extra unknown kwargs for now
* upgrade against upstream transformers main
* make sure trl is on latest too
* fix for upgraded trl
* fix: handle trl and transformer signature change
* feat: update trl to handle transformer signature
* RewardDataCollatorWithPadding no longer has max_length
* handle updated signature for tokenizer vs processor class
* invert logic for tokenizer vs processor class
* processing_class, not processor class
* also handle processing class in dpo
* handle model name w model card creation
* upgrade transformers and add a loss check test
* fix install of tbparse requirements
* make sure to add tbparse to req
* feat: revert kwarg to positional kwarg to be explicit
---------
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* Ensure hf_mlflow_log_artifact config var is set in env
* Add transformer MLflowCallback to callbacks list when mlflow enabled
* Test hf_mlflow_log_artifacts is set correctly
* Test mlflow not being used by default
