* fix: clarify input type
* fix: handling of error message if data_files not available
* fix: clarify attention handling
* fix: add doc on missing pad token
* add grpo scale_rewards config for trl#3135
* options to connect to vllm server directly w grpo trl#3094
* temperature support trl#3029
* sampling/generation kwargs for grpo trl#2989
* make vllm_enable_prefix_caching a config param trl#2900
* grpo multi-step optimizeations trl#2899
* remove overrides for grpo trainer
* bump trl to 0.16.0
* add cli to start vllm-serve via trl
* call the python module directly
* update to use vllm with 2.6.0 too now and call trl vllm serve from module
* vllm 0.8.1
* use python3
* use sys.executable
* remove context and wait for start
* fixes to make it actually work
* fixes so the grpo tests pass with new vllm paradigm
* explicit host/port and check in start vllm
* make sure that vllm doesn't hang by setting quiet so outouts go to dev null
* also bump bnb to latest release
* add option for wait from cli and nccl debugging for ci
* grpo + vllm test on separate devices for now
* make sure grpo + vllm tests runs single worker since pynccl comms would conflict
* fix cli
* remove wait and add caching for argilla dataset
* refactoring configs
* chore: lint
* add vllm config
* fixup vllm grpo args
* fix one more incorrect schema/config path
* fix another vlllm reference and increase timeout
* make the tests run a bit faster
* change mbsz back so it is correct for grpo
* another change mbsz back so it is correct for grpo
* fixing cli args
* nits
* adding docs
* docs
* include tensor parallel size for vllm in pydantic schema
* moving start_vllm, more docs
* limit output len for grpo vllm
* vllm enable_prefix_caching isn't a bool cli arg
* fix env ordering in tests and also use pid check when looking for vllm
---------
Co-authored-by: Salman Mohammadi <salman.mohammadi@outlook.com>
* guard return if ring attn alrady registered
* add docs link, bits in multi-gpu docs, remove save model callback (subsumed by HF trainers)
* configurable heads_k_stride from ring-flash-attn hf adapter
* fix: update chat_template
* fix: handle gemma3 showing a lot of no content for turn 0
* fix: remove unknown config from examples
* fix: test
* fix: temporary disable gemma2 test
* fix: stop overwriting config.text_config unnecessarily
* fix: handling of set cache to the text_config section
* feat: add liger gemma support and bump liger to 0.5.5
* fix: add double use_cache setting
* fix: add support for final_logit_softcap in CCE for gemma2/3
* fix: set use_cache before model load
* feat: add missing layernorm override
* fix: handle gemma3 rmsnorm
* fix: use wrapper to pass dim as hidden_size
* fix: change dim to positional
* fix: patch with wrong mlp
* chore: refactor use_cache handling
* fix import issues
* fix tests.e2e.utils import
---------
Co-authored-by: Wing Lian <wing@axolotl.ai>
* 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
* use default torch fused adamw optimizer as default as adamw_hf is deprecated
* make sure to have latest packaging installed
* bump packagingin requirements.txt too
* pass additional info for fix untrained tokens when using distributed + offloading
* use latest version of vendored lib
* use v0.0.5 of contribs lgpl
* fix for no bad tokens and add tests
* use release
* add multigpu test too
* make sure the multigpu zero3 test actually uses zero3
* add muon optimizer
optimizer_cls_and_kwargs is on trainer_kwargs
only add adamw_kwargs if they're non-null
fix mocks
better handling of override and check the optimizer
unwrap optimizer
* fix import
* override special tokens mock code
* fix(doc): remove duplicate config
* feat: replace added_tokens in tokenizer and add test
* make sure to run tokenizer modification on rank 0 only
* use is local main process instead
* feat: rename config
---------
Co-authored-by: NanoCode012 <nano@axolotl.ai>
Co-authored-by: Wing Lian <wing@axolotl.ai>
* Extend MultiPackBatchSampler test to include shorter sequence length and drop long sequences filter
* Fix get_dataset_lengths for datasets that were previously filtered (e.g., with drop_long_seq_in_dataset)
* Update src/axolotl/utils/samplers/utils.py
Fix get_dataset_lengths for datasets that do not have position_ids or length attributes
Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
---------
Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
* feat: update metadata fields and refactor config class in axolotlinputconfig
- Replace `metadata` fields with `json_schema_extra` in RayConfig class.
- Replace `Config` class with `ConfigDict` in AxolotlInputConfig.
- Set `populate_by_name` to `True` directly in `ConfigDict` instance.
* feat: update axolotlinputconfig in utils
* Replace `conlist` with `Annotated` for `datasets`, `test_datasets`, and `pretraining_dataset` fields
* Change default values for `lr_scheduler` and `optimizer` fields in `HyperparametersConfig` class
* Remove unnecessary Union from `evals_per_epoch` field in `AxolotlInputConfig` class
* Import `MinLen` from `annotated_types` module
* Remove import of `conlist` from `pydantic` module
* feat: update modelinputconfig and axolotlinputconfig in v0_4_1
- Removed ConfigDict import from pydantic in `src/axolotl/utils/config/models/input/v0_4_1/__init__.py`
- Added `model_config` with `protected_namespaces` to ModelInputConfig
- Replaced `config: ConfigDict` with `model_config` in AxolotlInputConfig
- Set `populate_by_name` to True in `model_config` for AxolotlInputConfig
* chore: get rid of unused import
