* 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
* 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>
* fix: use apply_chat_template to find turn boundaries and allow tool_calling field
* fix: keys to include in turn
* feat(doc): explicitly recommend setting train_on_eos and roles_to_train
* fix: eos not being masked for tool due to template padding
* chore: clear up docs
* fix: default messages format, train_on_eos: turn, and train on all assistant msg
* fix: properly warn if empty content
* feat: parametrize chat_template tests to test different tokenizers
* fix: set proper default for message key
* fix: update defaults to match load function
* fix: change defaults to use new
* feat: add tool_calling dataset
* feat: add tool_calling test
* fix: add handling of edge case of mistral tokenizer with only system prompt
* feat: refactor all test to follow source code
* fix: remove unnecessary eos_token from phi35
* fix test for phi3.5 since eos was dropped from chat_template
---------
Co-authored-by: Wing Lian <wing@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
---------
Co-authored-by: Chirag Jain <jain.chirag925@gmail.com>
* wip add new proposed message structure
* tokenization
* wip
* wip transform builder
* wip make the chat dataset loadable
* wip chatml + llama 3 new chat objects
* chore: lint
* chore: lint
* fix tokenization
* remove dacite dependency since we're using pydantic now
* fix handling when already correctly split in messages
* make sure to remove chat features from tokenized ds
* move chat to be a input transform for messages
* make sure llama3 has the bos token
* remove non-working special token code
* fix messages strat loader
* Add flexible configuration options for chat dataset training
- Introduce roles_to_train parameter to set training labels by role
- Add train_on_eos option to configure training on end-of-sequence tokens
- Implement per-message training configuration in dataset
- Allow fine-grained control over training specific portions of messages
- Add message_field_training and message_field_training_detail settings
- Implement mapping between dataset character offsets and tokenized prompt
- Enhance test suite to cover new functionality
* Fix missing field inits, things weren't working from yaml.
* Add flexible configuration options for chat dataset training
- Introduce roles_to_train parameter to set training labels by role
- Add train_on_eos option to configure training on end-of-sequence tokens
- Implement per-message training configuration in dataset
- Allow fine-grained control over training specific portions of messages
- Add message_field_training and message_field_training_detail settings
- Implement mapping between dataset character offsets and tokenized prompt
- Enhance test suite to cover new functionality
* Fix missing field inits, things weren't working from yaml.
* chore: lint
* Revert test repo back to NousResearch after opening PR to fix the tokenizer_config.json.
---------
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* Implementing a basic chat_template strategy for DPO datasets
This mimics the sft chat_template strategy such that users can:
* Specify the messages field
* Specify the per message role and content fields
* speicfy the chosen and rejected fields
* Let the tokenizer construct the raw prompt
* Ensure the chosen and rejected fields don't have any prefix tokens
* Adding additional dpo chat template unittests
* Rename test class
Allow in message objects the additional key `weight`, which can be set
to 0 (or 1) to cause that message to be masked out (or left unmasked)
for training (similar to [1]). This is helpful for training the model to be robust and
capable of error recovery upon a bad assistant message.
A missing `weight` key defaults to weight 1, to guarantee downward compatibility.
[1]: https://github.com/mistralai/mistral-finetune
The strategy now supports configuring several fields: * The data field holding message arrays * the role and
content fields for each message * role mapping from source to target types
additionally this adds a sample llama3-8b instruct template using the chat template
* Fix llama3 chat_template (the {{eos_token}} leads to an extra <|eot_id|> being added in the last turn). Output now matches official Llama 3 Instruct model
* add tests
* chore: lint
---------
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* Add Glaive conversation format support
* fix black formatting errors
* Fix black and pylint formatting errors
* only set role_key_tool if provided in the dataset constructor
* Update src/axolotl/prompt_strategies/sharegpt.py
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* sharegpt test
* tokenizer test
* fix formatting
---------
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* plain input/output prompt strategy w/o chat templates
* disable duplicate code check
* make sure to add an eos/eot token to the end of the output so it will stop
* multi turn segement support and test
* add system message to template
* readme update
* added code to register new system message
* register chatml template for test
---------
Co-authored-by: Mads Henrichsen <mads@BrbartiendeMads.lan>
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* fix: `train_on_inputs: true` ignored for sharegpt
* enable unit test for train_on_inputs for sharegpt
---------
Co-authored-by: Wing Lian <wing.lian@gmail.com>
* fix double eos token for chatml
* isolate fix to chatml conversation
* fix add special tokens to include rstrip
* add test for train_on_inputs for sharegpt
* don't use rstrip for chatml
