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feat: add config for optional parameters in a chat message (#2260)

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* 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>
This commit is contained in:
NJordan72
2025-02-17 21:59:27 -05:00
committed by GitHub
parent 3aac3b1da9
commit b194e17c28
51 changed files with 1190 additions and 230 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
tests/e2e/integrations/test_kd.py
View File

@@ -9,7 +9,7 @@ from e2e.utils import check_tensorboard, require_torch_2_5_1
from axolotl.cli.args import TrainerCliArgs
from axolotl.common.datasets import load_datasets
from axolotl.train import train
from axolotl.utils.config import normalize_config, prepare_plugins
from axolotl.utils.config import normalize_config, prepare_plugins, validate_config
from axolotl.utils.dict import DictDefault
@@ -79,6 +79,7 @@ class TestKnowledgeDistillation:
def test_llama_kd(self, temp_dir, kd_min_cfg):
cfg = DictDefault(kd_min_cfg)
# pylint: disable=duplicate-code
cfg = validate_config(cfg)
prepare_plugins(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
@@ -109,6 +110,7 @@ class TestKnowledgeDistillation:
| kd_min_cfg
)
# pylint: disable=duplicate-code
cfg = validate_config(cfg)
prepare_plugins(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()

4
tests/e2e/patched/test_fa_xentropy.py
View File

@@ -11,7 +11,7 @@ from transformers.utils import is_torch_bf16_gpu_available
from axolotl.cli.args import TrainerCliArgs
from axolotl.common.datasets import load_datasets
from axolotl.train import train
from axolotl.utils.config import normalize_config
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.dict import DictDefault
from ..utils import check_model_output_exists, check_tensorboard
@@ -76,7 +76,9 @@ class TestFAXentropyLlama:
else:
cfg.fp16 = True
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

4
tests/e2e/solo/test_relora_llama.py
View File

@@ -10,7 +10,7 @@ from pathlib import Path
from axolotl.cli.args import TrainerCliArgs
from axolotl.common.datasets import load_datasets
from axolotl.train import train
from axolotl.utils.config import normalize_config
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.dict import DictDefault
from ..utils import check_model_output_exists, check_tensorboard, with_temp_dir
@@ -73,6 +73,8 @@ class TestReLoraLlama(unittest.TestCase):
"use_tensorboard": True,
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

16
tests/e2e/test_dpo.py
View File

@@ -12,7 +12,7 @@ import pytest
from axolotl.cli.args import TrainerCliArgs
from axolotl.common.datasets import load_preference_datasets
from axolotl.train import train
from axolotl.utils.config import normalize_config
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.dict import DictDefault
from .utils import check_model_output_exists, with_temp_dir
@@ -63,6 +63,8 @@ class TestDPOLlamaLora(unittest.TestCase):
"gradient_checkpointing_kwargs": {"use_reentrant": True},
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_preference_datasets(cfg=cfg, cli_args=cli_args)
@@ -108,6 +110,8 @@ class TestDPOLlamaLora(unittest.TestCase):
"gradient_checkpointing_kwargs": {"use_reentrant": True},
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_preference_datasets(cfg=cfg, cli_args=cli_args)
@@ -153,6 +157,8 @@ class TestDPOLlamaLora(unittest.TestCase):
"gradient_checkpointing_kwargs": {"use_reentrant": True},
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_preference_datasets(cfg=cfg, cli_args=cli_args)
@@ -198,6 +204,8 @@ class TestDPOLlamaLora(unittest.TestCase):
"gradient_checkpointing_kwargs": {"use_reentrant": True},
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_preference_datasets(cfg=cfg, cli_args=cli_args)
@@ -242,6 +250,8 @@ class TestDPOLlamaLora(unittest.TestCase):
"gradient_checkpointing_kwargs": {"use_reentrant": True},
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_preference_datasets(cfg=cfg, cli_args=cli_args)
@@ -289,6 +299,8 @@ class TestDPOLlamaLora(unittest.TestCase):
"gradient_checkpointing_kwargs": {"use_reentrant": True},
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_preference_datasets(cfg=cfg, cli_args=cli_args)
@@ -353,6 +365,8 @@ class TestDPOLlamaLora(unittest.TestCase):
"gradient_checkpointing_kwargs": {"use_reentrant": True},
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_preference_datasets(cfg=cfg, cli_args=cli_args)

4
tests/e2e/test_embeddings_lr.py
View File

@@ -9,7 +9,7 @@ import unittest
from axolotl.cli.args import TrainerCliArgs
from axolotl.common.datasets import load_datasets
from axolotl.train import train
from axolotl.utils.config import normalize_config
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.dict import DictDefault
from .utils import check_model_output_exists, check_tensorboard, with_temp_dir
@@ -56,6 +56,8 @@ class TestEmbeddingsLrScale(unittest.TestCase):
"use_tensorboard": True,
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

8
tests/e2e/test_falcon.py
View File

@@ -9,7 +9,7 @@ import unittest
from axolotl.cli.args import TrainerCliArgs
from axolotl.common.datasets import load_datasets
from axolotl.train import train
from axolotl.utils.config import normalize_config
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.dict import DictDefault
from .utils import check_model_output_exists, with_temp_dir
@@ -65,6 +65,8 @@ class TestFalcon(unittest.TestCase):
"bf16": "auto",
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
@@ -118,6 +120,8 @@ class TestFalcon(unittest.TestCase):
"bf16": "auto",
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
@@ -157,6 +161,8 @@ class TestFalcon(unittest.TestCase):
"bf16": "auto",
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

8
tests/e2e/test_llama.py
View File

@@ -10,7 +10,7 @@ from e2e.utils import check_model_output_exists
from axolotl.cli.args import TrainerCliArgs
from axolotl.common.datasets import load_datasets
from axolotl.train import train
from axolotl.utils.config import normalize_config
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.dict import DictDefault
LOG = logging.getLogger("axolotl.tests.e2e")
@@ -56,6 +56,8 @@ class TestLlama:
"save_safetensors": True,
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
@@ -99,6 +101,8 @@ class TestLlama:
"save_safetensors": True,
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
@@ -138,6 +142,8 @@ class TestLlama:
"save_safetensors": True,
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

4
tests/e2e/test_llama_pretrain.py
View File

@@ -10,7 +10,7 @@ import pytest
from axolotl.cli.args import TrainerCliArgs
from axolotl.common.datasets import load_datasets
from axolotl.train import train
from axolotl.utils.config import normalize_config
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.dict import DictDefault
from .utils import check_model_output_exists, check_tensorboard
@@ -69,6 +69,8 @@ class TestPretrainLlama:
"use_tensorboard": True,
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

4
tests/e2e/test_llama_vision.py
View File

@@ -9,7 +9,7 @@ import unittest
from axolotl.cli.args import TrainerCliArgs
from axolotl.common.datasets import load_datasets
from axolotl.train import train
from axolotl.utils.config import normalize_config
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.dict import DictDefault
from .utils import check_model_output_exists, with_temp_dir
@@ -62,6 +62,8 @@ class TestLlamaVision(unittest.TestCase):
"bf16": True,
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

4
tests/e2e/test_lora_llama.py
View File

@@ -9,7 +9,7 @@ import unittest
from axolotl.cli.args import TrainerCliArgs
from axolotl.common.datasets import load_datasets
from axolotl.train import train
from axolotl.utils.config import normalize_config
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.dict import DictDefault
from .utils import check_model_output_exists, with_temp_dir
@@ -59,6 +59,8 @@ class TestLoraLlama(unittest.TestCase):
"max_steps": 20,
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

4
tests/e2e/test_mamba.py
View File

@@ -11,7 +11,7 @@ import pytest
from axolotl.cli.args import TrainerCliArgs
from axolotl.common.datasets import load_datasets
from axolotl.train import train
from axolotl.utils.config import normalize_config
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.dict import DictDefault
from .utils import check_model_output_exists, with_temp_dir
@@ -59,6 +59,8 @@ class TestMamba(unittest.TestCase):
"save_safetensors": False,
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

6
tests/e2e/test_mistral.py
View File

@@ -11,7 +11,7 @@ from transformers.utils import is_torch_bf16_gpu_available
from axolotl.cli.args import TrainerCliArgs
from axolotl.common.datasets import load_datasets
from axolotl.train import train
from axolotl.utils.config import normalize_config
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.dict import DictDefault
from .utils import check_model_output_exists, with_temp_dir
@@ -63,6 +63,8 @@ class TestMistral(unittest.TestCase):
"eval_steps": 10,
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
@@ -106,6 +108,8 @@ class TestMistral(unittest.TestCase):
cfg.bf16 = True
else:
cfg.fp16 = True
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

12
tests/e2e/test_mixtral.py
View File

@@ -12,7 +12,7 @@ from transformers.utils import is_torch_bf16_gpu_available
from axolotl.cli.args import TrainerCliArgs
from axolotl.common.datasets import load_datasets
from axolotl.train import train
from axolotl.utils.config import normalize_config
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.dict import DictDefault
from .utils import check_model_output_exists, with_temp_dir
@@ -69,6 +69,8 @@ class TestMixtral(unittest.TestCase):
"eval_steps": 10,
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
@@ -123,6 +125,8 @@ class TestMixtral(unittest.TestCase):
"eval_steps": 10,
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
@@ -180,6 +184,8 @@ class TestMixtral(unittest.TestCase):
cfg.bf16 = True
else:
cfg.fp16 = True
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
@@ -233,6 +239,8 @@ class TestMixtral(unittest.TestCase):
"eval_steps": 10,
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
if is_torch_bf16_gpu_available():
cfg.bf16 = True
@@ -281,6 +289,8 @@ class TestMixtral(unittest.TestCase):
cfg.bf16 = True
else:
cfg.fp16 = True
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

8
tests/e2e/test_optimizers.py
View File

@@ -9,7 +9,7 @@ import unittest
from axolotl.cli.args import TrainerCliArgs
from axolotl.common.datasets import load_datasets
from axolotl.train import train
from axolotl.utils.config import normalize_config
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.dict import DictDefault
from .utils import check_model_output_exists, require_torch_2_5_1, with_temp_dir
@@ -59,6 +59,8 @@ class TestCustomOptimizers(unittest.TestCase):
"lr_scheduler": "cosine",
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
@@ -103,6 +105,8 @@ class TestCustomOptimizers(unittest.TestCase):
"lr_scheduler": "cosine",
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
@@ -139,6 +143,8 @@ class TestCustomOptimizers(unittest.TestCase):
}
)
# pylint: disable=duplicate-code
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

4
tests/e2e/test_packing_loss.py
View File

@@ -11,7 +11,7 @@ from transformers.utils import is_torch_bf16_gpu_available
from axolotl.cli.args import TrainerCliArgs
from axolotl.common.datasets import load_datasets
from axolotl.train import train
from axolotl.utils.config import normalize_config
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.dict import DictDefault
from .utils import check_tensorboard, with_temp_dir
@@ -59,6 +59,8 @@ class TestPackedLlama(unittest.TestCase):
cfg.bf16 = True
else:
cfg.fp16 = True
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

3
tests/e2e/test_phi.py
View File

@@ -9,7 +9,7 @@ import unittest
from axolotl.cli.args import TrainerCliArgs
from axolotl.common.datasets import load_datasets
from axolotl.train import train
from axolotl.utils.config import normalize_config
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.dict import DictDefault
from .utils import check_model_output_exists, with_temp_dir
@@ -61,6 +61,7 @@ class TestPhi(unittest.TestCase):
"bf16": "auto",
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

6
tests/e2e/test_qwen.py
View File

@@ -40,8 +40,10 @@ class TestE2eQwen:
"field_messages": "conversation",
"field_chosen": "chosen",
"field_rejected": "rejected",
"message_field_role": "role",
"message_field_content": "content",
"message_property_mappings": {
"role": "role",
"content": "content",
},
"roles": {
"system": ["system"],
"user": ["user"],

3
tests/e2e/test_reward_model_smollm2.py
View File

@@ -9,7 +9,7 @@ import unittest
from axolotl.cli.args import TrainerCliArgs
from axolotl.common.datasets import load_datasets
from axolotl.train import train
from axolotl.utils.config import normalize_config
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.dict import DictDefault
from .utils import check_model_output_exists, check_tensorboard, with_temp_dir
@@ -66,6 +66,7 @@ class TestRewardModelLoraSmolLM2(unittest.TestCase):
"use_tensorboard": True,
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)