* feat: add gemma3n cce
* feat: add sample config
* feat: add gemma3n multimodal mode
* feat: add audio example
* feat: support audio and return pixel values in collator
* feat: support unmask only assistant region (gemma3n for now)
* feat(doc): add notes for audio loading
* feat: add audio support for gemma3n
* feat: update examples
* feat: add gemma3n to the docs
* fix: add link at top
* feat(doc): clarify additional requirements
* fix: mllama missing aspect ratio
* fix: mllama need attention fixes for fa2
* Partially Revert "fix: mllama need attention fixes for fa2"
This reverts commit a0bfdd1777.
* fix: disable FA2 for mllama in vision mode
* feat: update configs to use proper attention
* fix: support other vision features
* feat(doc): clarify requirements for gemma3n
* make pad_to_sequence_len default to the same value as sample_packing
* remove duplicate validation
* fix test
* update description meta
Co-authored-by: NanoCode012 <nano@axolotl.ai>
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Co-authored-by: NanoCode012 <nano@axolotl.ai>
* checkpoint model on first step callback
* remove debug
* add test cases; update existing tests not to save on first step
* move test out of solo
* delete
* default to False
* typo
* bump hf deps
* upgrade liger-kernel too
* install cce from fork for transformers fix
* fix reference to vocab size in gemma3 patch
* use padding_idx instead of pad_token_id
* remove fixed gemma3 patch
* use updated cce fork
* fix local mllama cce patches w docstring
* add test for multipack with trainer setup and fix trainer for trainer refactor upstream
* bump modal version
* guard for iterable datasetS
* mllama model arch layout changed in latest transformers
* fix batch sampler with drop_last
* fix: address upstream vlm changes for lora
* fix: update references to old lora target path
* fix: remove mllama fa2 patch due to upstream fix
* fix: lora kernel patch path for multimodal models
* fix: removed mllama from quarto
* run test for came optim on 2.6.0+
* fix fsdp2 patch and remove deprecated patch
* make sure to set sequence_parallel_degree for grpo
* Add SP test for GRPO
* add sp to grpo config for trainer
* use reward_funcs as kwarg to grpo trainer
* fix the comprehension for reward funcs
* reward funcs already passed in as args
* init sp_group right before training
* fix check for adding models to SP context
* make sure to pass args to super
* upgrade deepspeed
* use updated trl and add reasoning flags for vllm
* patch the worker
---------
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* feat: add config for optional parameters in a chat message
* chore: cleanup
* chore: fix nits and add light docs
* docs: update docs/dataset-formats/conversation.qmd
Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
* feat: configurable message mappings, jinja template analyzer
* chore: handle bradley terry
* docs: update docs
* refactor: change order of mappings, improve message transform
* refactor: make chat awware of property mappings
* chore: remove .python-version
* chore: revert change
* chore: add dataset validation to tests where appropriate
* chore: add dataset validation to tests where appropriate
* chore: clean up handling of ds_cfg
* chore: recursively serialize config
* make sure to use the return value from validate_config
* DefaultDict pickle/unpickle fix
* fix super call for override
* refactor: message fields
* chore: empty commit
* tests: validate config before using
* chore: add config validation to all e2e tests
* chore: add unneeded logging
* chore: add missed config validation
* chore: pass field_messages to prompter
* test: fix borked test
* chore: remove uninteded file
* chore: add deprecation warning and update chat_datasets script
* chore: lint
* refactor: message fields
* feat: update axolotlinputconfig and test_models
- add configdict import in axolotl/utils/config/models/input/v0_4_1/__init__.py
- remove unnecessary line breaks in sftdataset, dpodataset, ktodataset, stepwisesuperviseddataset classes
- update model_dump method in axolotlinputconfig to exclude none values
- correct typo in test_models.py comment
* feat: simplify dpodataset and ktodataset classes in config models
removed several optional fields from dpodataset and ktodataset classes in axolotl/utils/config/models/input/v0_4_1. this simplifies the configuration subsets for these datasets.
* feat: improve readability and structure in dataset configuration models
this commit enhances the readability and structure of the dataset configuration models in the `axolotl/utils/config/models/input/v0_4_1` module. it removes unused `configdict` import and adds line breaks to separate class definitions for better clarity. additionally, a minor documentation fix is included to ensure a newline at the end of the `stepwise_supervised.qmd` file.
* feat: change log level from info to debug in chattemplatestrategy
* feat(prompt_strategies): refactor chattemplateprompter and chattemplatestrategy
- Make `chat_template` a required parameter in `ChatTemplatePrompter` constructor
- Add default value for `message_property_mappings` in `ChatTemplatePrompter` constructor
- Add `messages_array_name` property to `ChatTemplatePrompter`
- Change `processor` type to Optional in `ChatTemplatePrompter`
- Add TypeError check for `processor` in `ChatTemplatePrompter.build_prompt`
- Remove `_messages` property from `ChatTemplateStrategy`
- Make `prompter` a required parameter and add type hint in `ChatTemplateStrategy` constructor
- Remove `messages` getter and setter from `ChatTemplateStrategy`
- Use `prompter.messages_array_name` in `ChatTemplateStrategy.get_conversation_thread`
- Remove condition to set `messages` field in `load` function
* feat(tests/utils): ignore type check in load_model call in test_models.py
* feat: improve type handling and test structure in chat templates
- Add return type hint for `get_chat_template` function in `chat_templates.py`
- Remove unnecessary assignment of `strategy.messages` in several test cases
- Add `messages_array_name` parameter to various test configurations in `test_chat_templates.py` and `test_chat_templates_advanced.py`
- Remove redundant `strategy.messages` assignment in `test_chat_templates_advanced.py`
* feat(axolotl): enhance chat strategy with datasetconfig support
This commit introduces support for DatasetConfig in the ChatTemplateStrategy. It also refines the strategy loader to handle different types of ds_cfg inputs and improves the clarity of the code by formatting and reordering. The key changes include:
- Importing Union from typing and BaseModel from pydantic.
- Adding DatasetConfig as an optional type for ds_cfg in StrategyLoader.
- Adjusting the handling of ds_cfg in StrategyLoader to account for BaseModel instances.
- Refactoring the prompter_params and strategy_params for better readability.
- Changing the reference from prompt[self.messages] to prompt[self.prompter.messages_array_name] in the is_prompt_batched method.
* feat: update message handling in btchattemplatestrategy
* Replace `self.messages` with direct string references to "chosen_messages" and "rejected_messages"
* Append system, user, and assistant content directly to "chosen_messages" and "rejected_messages"
* Add a new attribute "messages_array_name" to the `load` function parameters
* Remove the conditional attribute assignment for "field_messages" in the `load` function
* feat: add config validation in test_kd.py
- Import `validate_config` from `axolotl.utils.config`
- Validate the configuration in `test_llama_kd` and another function in `TestKnowledgeDistillation` class
* feat: enhance config validation and capabilities handling
* Import `EnvCapabilities` and `GPUCapabilities` from `axolotl.utils.config.models.internals`
* Update `validate_config` function to create `KTODataset` and `SFTDataset` instances using `dict(ds_cfg)`
* Replace `capabilities` and `env_capabilities` with instances of `GPUCapabilities` and `EnvCapabilities` respectively in `AxolotlConfigWCapabilities` model dump
* feat: update config validation in axolotl utils
- Remove import of `EnvCapabilities` and `GPUCapabilities` from `axolotl.utils.config.models.internals`
- Update `validate_config` function to use `capabilities` and `env_capabilities` directly instead of creating new instances of `GPUCapabilities` and `EnvCapabilities`
* feat: refactor strategyloader in chat_template.py
- Extracted the creation of strategy parameters into a separate function, `_get_strategy_params(cfg, dataset_config)`
- Created a new function, `_get_strategy_cls()`, to obtain the strategy class
- Replaced `ChatTemplateStrategy` with `strategy_cls` for strategy instantiation
* trigger CI
* chore: revert dataset config changes for kto/dpo
* subject: refactor: rename 'messages_array_name' to 'field_messages'
Body:
- Renamed 'messages_array_name' to 'field_messages' in 'ChatTemplatePrompter' class and its usages in 'chat_template.py'
- Updated 'load' function in 'bradley_terry/chat_template.py' to reflect the change
- Adjusted 'get_chat_template_msg_variables' and 'get_message_vars' methods in 'jinja_template_analyzer.py' to use the new variable name
- Modified 'StrategyLoader' in 'chat_template.py' to use 'field_messages'
- Updated tests in 'test_chat_templates.py' and 'test_chat_templates_advanced.py' to use 'field_messages' instead of 'messages_array_name'
* feat: refactor prompt strategies and update config models
* Remove redundant 'return None' in `axolotl/prompt_strategies/__init__.py`
* Simplify message handling in `axolotl/prompt_strategies/bradley_terry/chat_template.py` by using a single 'messages' list instead of separate 'chosen_messages' and 'rejected_messages' lists
* Update default 'message_property_mappings' in `axolotl/prompt_strategies/bradley_terry/chat_template.py`
* Add 'field_messages' field to `axolotl/utils/config/models/input/v0_4_1/__init__.py` configuration model
* chore: remove unused input
* chore: remove redundant type ignore
* fix: remove old configs and update examples
* fix: type check
* fix: remove loading old config in ChatMessage
* fix: update faq with potential new undefinederror
* fix: add debug if property mapped is not found
* chore: improve explanation for unmapped properties
* fix: update docs with new config
* chore: add note for deprecation config and del old config from dict
---------
Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
Co-authored-by: Wing Lian <wing@axolotl.ai>
Co-authored-by: NanoCode012 <nano@axolotl.ai>
* add example for mistral orpo
* sample_packing: false for orpo
* go to load_dataset (since load_rl_datasets require a transfom_fn, which only dpo uses currently)
* wip for dbrx finetuning
* add fastcore for parallel loading of sharded weights
* fix dtype for load, use PartialState instead of accelerator to init process group, remove redundant wandb callback
* update to use v2 of the converted model
* more fixes for dbrx loras
* make sure to enable fsdp activation checkpointing
* fix support for 8bit loras too for dbrx
* apply z3 leaf moe fix for DBRX with deepspeed
* don't raise value error since child module searches could fail and be ok
* revert a previous change to fix fsdp
* update mistral/mistral qlora+fsdp yamls
* fix qlora+fsdp quant storage type
* more edge cases for qlora-fsdp
* fixes for fsdp+qlora w optimizer in 8bit
* add bigstral z3 config and make sure to use full_state_dict for fsdp
* wip qlora + fsdp fixes
* more fixes
* make sure to load the lora 🤦
* only setup quantized meta on non-zero rank:
* only run setup_quantized_peft_meta_for_training for qlora+fsdp
* more fixes for qlora+fsdp
* chore: lint
* add example yml
* support mistral too
* fix for model_type and add mixtral support too
* set cpu_offload: false to reduce vram, constrain new accleerator logic to qlora + fsdp
* refactor for duplicate code
* Add CausalLMBenchEvalCallback for measuring seq2seq performance
* Fix code for pre-commit
* Fix typing and improve logging
* eval_sample_packing must be false with CausalLMBenchEvalCallback
* Mistral-7b finetune example using axolotl with code,config,data
* Corrected the path for huggingface dataset
* Update data.jsonl
* chore: lint
---------
Co-authored-by: twenty8th <twenty8th@users.noreply.github.com>
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* set fp16 to false if bf16, update bf16: auto in example YAMLs
* unset fp16 so that it fallsback properly if bf16 isn't available
* Update README.md [skip-ci]
Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
* test that bf16 disables fp16
---------
Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
* add check for zero3
* freeze parameters
* fixes for deepspeed loading
* fix model parameter check
* unfrozen parameters in example mixtral and logging when unfreezing
* mixtral multipack
* use mixtral model
* sample yml
* calculate cu_seqlens properly
* use updated flash ettention setting
* attn var checks
* force use of flash attention 2 for packing
* lint
* disable future fix for now
* update support table
* Feat: Update to handle wandb env better
* chore: rename wandb_run_id to wandb_name
* feat: add new recommendation and update config
* fix: indent and pop disabled env if project passed
* feat: test env set for wandb and recommendation
* feat: update to use wandb_name and allow id
* chore: add info to readme
* 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
