mirror of
https://github.com/hpcaitech/ColossalAI.git
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df5e9c53cf
* Add dpo. Fix sft, ppo, lora. Refactor all * fix and tested ppo * 2 nd round refactor * add ci tests * fix ci * fix ci * fix readme, style * fix readme style * fix style, fix benchmark * reproduce benchmark result, remove useless files * rename to ColossalChat * use new image * fix ci workflow * fix ci * use local model/tokenizer for ci tests * fix ci * fix ci * fix ci * fix ci timeout * fix rm progress bar. fix ci timeout * fix ci * fix ci typo * remove 3d plugin from ci temporary * test environment * cannot save optimizer * support chat template * fix readme * fix path * test ci locally * restore build_or_pr * fix ci data path * fix benchmark * fix ci, move ci tests to 3080, disable fast tokenizer * move ci to 85 * support flash attention 2 * add all-in-one data preparation script. Fix colossal-llama2-chat chat template * add hardware requirements * move ci test data * fix save_model, add unwrap * fix missing bos * fix missing bos; support grad accumulation with gemini * fix ci * fix ci * fix ci * fix llama2 chat template config * debug sft * debug sft * fix colossalai version requirement * fix ci * add sanity check to prevent NaN loss * fix requirements * add dummy data generation script * add dummy data generation script * add dummy data generation script * add dummy data generation script * update readme * update readme * update readme and ignore * fix logger bug * support parallel_output * modify data preparation logic * fix tokenization * update lr * fix inference * run pre-commit --------- Co-authored-by: Tong Li <tong.li352711588@gmail.com>
25 lines
639 B
Python
Executable File
25 lines
639 B
Python
Executable File
from .base import BaseModel
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from .critic import Critic
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from .generation import generate, generate_streaming, prepare_inputs_fn, update_model_kwargs_fn
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from .lora import convert_to_lora_module
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from .loss import DpoLoss, LogExpLoss, LogSigLoss, PolicyLoss, ValueLoss
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from .reward_model import RewardModel
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from .utils import disable_dropout
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__all__ = [
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"BaseModel",
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"Critic",
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"RewardModel",
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"PolicyLoss",
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"ValueLoss",
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"LogSigLoss",
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"LogExpLoss",
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"convert_to_lora_module",
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"DpoLoss",
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"generate",
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"generate_streaming",
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"disable_dropout",
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"update_model_kwargs_fn",
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"prepare_inputs_fn",
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]
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