diff --git a/requirements.txt b/requirements.txt index 1af103e17..27b31a139 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,7 @@ peft @ git+https://github.com/huggingface/peft.git transformers @ git+https://github.com/huggingface/transformers.git bitsandbytes>=0.39.0 -attrdict +addict fire PyYAML==6.0 black diff --git a/scripts/finetune.py b/scripts/finetune.py index 8d7a18a4a..954ce1625 100644 --- a/scripts/finetune.py +++ b/scripts/finetune.py @@ -10,7 +10,7 @@ from typing import Optional, List, Dict, Any, Union import fire import torch import yaml -from attrdict import AttrDefault +from addict import Dict # add src to the pythonpath so we don't need to pip install this from axolotl.utils.tokenization import check_dataset_labels @@ -131,7 +131,7 @@ def train( # load the config from the yaml file with open(config, "r") as f: - cfg: AttrDefault = AttrDefault(lambda: None, yaml.load(f, Loader=yaml.Loader)) + cfg: Dict = Dict(lambda: None, yaml.load(f, Loader=yaml.Loader)) # if there are any options passed in the cli, if it is something that seems valid from the yaml, # then overwrite the value cfg_keys = dict(cfg).keys() diff --git a/src/axolotl/utils/models.py b/src/axolotl/utils/models.py index fe9f18979..6538086fb 100644 --- a/src/axolotl/utils/models.py +++ b/src/axolotl/utils/models.py @@ -29,7 +29,7 @@ from axolotl.prompt_tokenizers import LLAMA_DEFAULT_PAD_TOKEN if TYPE_CHECKING: from peft import PeftModel, PeftConfig - from attrdict import AttrDefault + from addict import Dict from transformers import PreTrainedTokenizer @@ -79,7 +79,7 @@ def load_model( adapter="lora", inference=False, ): - # type: (str, str, str, str, AttrDefault, Optional[str], bool) -> Tuple[PreTrainedModel, Optional[PeftConfig]] + # type: (str, str, str, str, Dict, Optional[str], bool) -> Tuple[PreTrainedModel, PreTrainedTokenizer, Optional[PeftConfig]] # TODO refactor as a kwarg load_in_8bit = cfg.load_in_8bit @@ -294,7 +294,7 @@ def load_model( def load_adapter(model, cfg, adapter): - # type: (PreTrainedModel, AttrDefault, Optional[str]) -> Tuple[PreTrainedModel, Optional[PeftConfig]] + # type: (PreTrainedModel, Dict, Optional[str]) -> Tuple[PreTrainedModel, Optional[PeftConfig]] if adapter is None: return model, None @@ -307,7 +307,7 @@ def load_adapter(model, cfg, adapter): def load_llama_adapter(model, cfg): - # type: (PreTrainedModel, AttrDefault) -> Tuple[PreTrainedModel, Optional[PeftConfig]] + # type: (PreTrainedModel, Dict) -> Tuple[PreTrainedModel, Optional[PeftConfig]] from peft import ( AdaptionPromptConfig, get_peft_model, @@ -355,7 +355,7 @@ def find_all_linear_names(bits, model): def load_lora(model, cfg): - # type: (PreTrainedModel, AttrDefault) -> Tuple[PreTrainedModel, Optional[PeftConfig]] + # type: (PreTrainedModel, Dict) -> Tuple[PreTrainedModel, Optional[PeftConfig]] from peft import ( LoraConfig,