File size: 2,758 Bytes
8567b2b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | import trl
from peft import LoraConfig
from datasets import load_dataset
from clemcore.backends.huggingface_local_api import HuggingfaceLocalModel
from playpen import BasePlaypenTrainer
class PeftSftTrainer(BasePlaypenTrainer):
def __init__(self, learner: HuggingfaceLocalModel):
super().__init__(learner)
# Note: We configure the proper chat template for the tokenizer already during model loading in the backend
def learn(self):
# Load a conversational dataset for SFT, that is, a list of "messages" -- basically tuples of role and content.
# The role can be "user" or "assistant" and typically alternates within the list.
# During training, everything up to the last assistant message becomes the prefix for prediction.
# The loss is calculated based on the differences to the last assistant message.
# Here we load the canonical training split as available in the huggingface playpen-data repository.
# By default, the dataset is stored in ~/.cache/huggingface/datasets/ on your machine. This might take a while.
dataset = load_dataset("colab-potsdam/playpen-data", "interactions", split="train")
# Only use the episodes we are interested to train on: here all episodes with successful outcome
dataset = dataset.filter(lambda episode: episode["meta"]["outcome"] == "success")
# We shuffle and split the remaining filtered samples to receive a test split
dataset = dataset.train_test_split(0.2, shuffle=True, seed=42)
# Initialize training configuration
config = trl.SFTConfig( # inherits TrainingArguments
max_length=300,
output_dir=f"models/sft+lora/{self.learner.name}",
eval_strategy="epoch",
packing=False,
completion_only_loss=True
)
# Initialize trainer context
trainer = trl.SFTTrainer(
model=self.learner.model,
train_dataset=dataset["train"],
eval_dataset=dataset["test"],
args=config,
# see https://huggingface.co/docs/trl/sft_trainer#training-adapters
peft_config=LoraConfig(
r=16, lora_alpha=32,
lora_dropout=0.05,
target_modules="all-linear",
modules_to_save=["lm_head", "embed_token"],
task_type="CAUSAL_LM",
)
)
# Train on the dataset; this will save only the adapters to the checkpoints directory
trainer.train()
# Optional: Uncomment these lines to merge and save directly
# merged_model = trainer.model.merge_and_unload()
# merged_model.save_pretrained(f"models/sft+lora/{self.learner.get_name()}")
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