ros/LLaMA-Factory
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

support function calling

Browse Source 
Former-commit-id: 66533b3f65babf2429c92c0f8fafe4eff5e0ff63
This commit is contained in:
hiyouga
2024-01-18 09:54:23 +08:00
parent f7329b1a0e
commit a423274fd9
67 changed files with 1239 additions and 1079 deletions
Download Patch File Download Diff File Expand all files Collapse all files

22
src/llmtuner/train/sft/workflow.py
View File

@@ -3,18 +3,19 @@
from typing import TYPE_CHECKING, Optional, List
from transformers import DataCollatorForSeq2Seq, Seq2SeqTrainingArguments
from llmtuner.data import get_dataset, preprocess_dataset, split_dataset
from llmtuner.extras.constants import IGNORE_INDEX
from llmtuner.extras.misc import get_logits_processor
from llmtuner.extras.ploting import plot_loss
from llmtuner.model import load_model_and_tokenizer
from llmtuner.train.sft.metric import ComputeMetrics
from llmtuner.train.sft.trainer import CustomSeq2SeqTrainer
from llmtuner.train.utils import create_modelcard_and_push
from ...data import get_dataset, split_dataset
from ...extras.constants import IGNORE_INDEX
from ...extras.misc import get_logits_processor
from ...extras.ploting import plot_loss
from ...model import load_model_and_tokenizer
from ...train.sft.metric import ComputeMetrics
from ...train.sft.trainer import CustomSeq2SeqTrainer
from ...train.utils import create_modelcard_and_push
if TYPE_CHECKING:
from transformers import TrainerCallback
from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
from ...hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
def run_sft(
@@ -25,9 +26,8 @@ def run_sft(
generating_args: "GeneratingArguments",
callbacks: Optional[List["TrainerCallback"]] = None
):
dataset = get_dataset(model_args, data_args)
model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train)
dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="sft")
dataset = get_dataset(model_args, data_args, tokenizer, training_args, stage="sft")
if training_args.predict_with_generate:
tokenizer.padding_side = "left" # use left-padding in generation