perf添加lora-hub实用程序

[www.ziwang.com]

huggingface/peft#39

此 PR 通过能够共享经过训练的 LoRA 权重和配置来添加一些增强功能.使用此 PR，API 如下所示：peft

from transformers import AutoModelForCausalLM
from peft import LoraConfig, LoraModel

model_id = "facebook/opt-350m"
lora_model_id = "./temp-lora"

Create a config

config = LoraConfig(
r=16,
lora_alpha=32,
target_modules=["q_proj", "v_proj"],
lora_dropout=0.05,
bias="none",
)
config.save_pretrained(lora_model_id)

Load the config

config = LoraConfig.from_pretrained(lora_model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

Load and save the model

model = LoraModel(config, model)

save the adapters only --> here only 6MB

model.save_pretrained(lora_model_id)

Load from saved model

model = AutoModelForCausalLM.from_pretrained(model_id)
model = LoraModel.from_pretrained(model, lora_model_id)
此公关补充说：

from_pretrained支持和xxConfigLoraModel
save_pretrained支持和xxConfigLoraModel

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以下是我在集线器上推送的推送适配器权重的示例： https://huggingface.co/ybelkada/test-opt-lora/tree/main，您可以使用以下内容在此 PR 中加载：facebook/opt-350m

from transformers import AutoModelForCausalLM
from peft import LoraConfig, LoraModel

from huggingface_hub.repocard import RepoCard

lora_model_id = "ybelkada/test-opt-lora"
card = RepoCard.load(lora_model_id)
model_id = card.data.to_dict()["base_model"]

Load the config & model

config = LoraConfig.from_pretrained(lora_model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

Load the Lora model

model = LoraModel.from_pretrained(model, lora_model_id)
抄送@sayakpaul@pacman100此 PR 现已准备好进行审核

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