mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-01 03:02:51 +08:00
update get template
Former-commit-id: dabad5570bf4a6b1044c963d8f27717030f373ef
This commit is contained in:
parent
1dfd1aaf82
commit
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@ -25,7 +25,7 @@ from torch.utils.data import DataLoader
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from tqdm import tqdm
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from transformers import DataCollatorForLanguageModeling, DataCollatorForSeq2Seq
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from llamafactory.data import get_dataset
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from llamafactory.data import get_dataset, get_template_and_fix_tokenizer
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from llamafactory.extras.constants import IGNORE_INDEX
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from llamafactory.hparams import get_train_args
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from llamafactory.model import load_tokenizer
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@ -66,7 +66,8 @@ def calculate_lr(
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)
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tokenizer_module = load_tokenizer(model_args)
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tokenizer = tokenizer_module["tokenizer"]
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trainset = get_dataset(model_args, data_args, training_args, stage, **tokenizer_module)["train_dataset"]
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template = get_template_and_fix_tokenizer(tokenizer, data_args)
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trainset = get_dataset(template, model_args, data_args, training_args, stage, **tokenizer_module)["train_dataset"]
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if stage == "pt":
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data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
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elif stage == "sft":
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@ -23,7 +23,7 @@ from torch.utils.data import DataLoader
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from tqdm import tqdm
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from transformers import DataCollatorForLanguageModeling, DataCollatorForSeq2Seq
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from llamafactory.data import get_dataset
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from llamafactory.data import get_dataset, get_template_and_fix_tokenizer
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from llamafactory.extras.constants import IGNORE_INDEX
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from llamafactory.hparams import get_train_args
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from llamafactory.model import load_model, load_tokenizer
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@ -88,7 +88,8 @@ def cal_ppl(
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)
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tokenizer_module = load_tokenizer(model_args)
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tokenizer = tokenizer_module["tokenizer"]
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trainset = get_dataset(model_args, data_args, training_args, stage, **tokenizer_module)["train_dataset"]
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template = get_template_and_fix_tokenizer(tokenizer, data_args)
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trainset = get_dataset(template, model_args, data_args, training_args, stage, **tokenizer_module)["train_dataset"]
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model = load_model(tokenizer, model_args, finetuning_args, is_trainable=False)
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if stage == "pt":
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data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
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@ -18,7 +18,7 @@ from collections import defaultdict
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import fire
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from tqdm import tqdm
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from llamafactory.data import get_dataset
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from llamafactory.data import get_dataset, get_template_and_fix_tokenizer
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from llamafactory.hparams import get_train_args
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from llamafactory.model import load_tokenizer
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@ -48,7 +48,8 @@ def length_cdf(
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)
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)
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tokenizer_module = load_tokenizer(model_args)
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trainset = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)["train_dataset"]
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template = get_template_and_fix_tokenizer(tokenizer_module["tokenizer"], data_args)
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trainset = get_dataset(template, model_args, data_args, training_args, "sft", **tokenizer_module)["train_dataset"]
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total_num = len(trainset)
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length_dict = defaultdict(int)
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for sample in tqdm(trainset["input_ids"]):
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@ -54,7 +54,7 @@ class HuggingfaceEngine(BaseEngine):
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self.tokenizer = tokenizer_module["tokenizer"]
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self.processor = tokenizer_module["processor"]
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self.tokenizer.padding_side = "left" if self.can_generate else "right"
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self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template, data_args.tool_format)
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self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args)
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self.model = load_model(
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self.tokenizer, model_args, finetuning_args, is_trainable=False, add_valuehead=(not self.can_generate)
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) # must after fixing tokenizer to resize vocab
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@ -68,7 +68,7 @@ class VllmEngine(BaseEngine):
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self.tokenizer = tokenizer_module["tokenizer"]
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self.processor = tokenizer_module["processor"]
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self.tokenizer.padding_side = "left"
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self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template, data_args.tool_format)
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self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args)
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self.generating_args = generating_args.to_dict()
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engine_args = {
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@ -14,7 +14,7 @@
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import os
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import sys
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from typing import TYPE_CHECKING, Dict, Literal, Optional, Sequence, Tuple, Union
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from typing import TYPE_CHECKING, Dict, Literal, Optional, Sequence, Union
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import numpy as np
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from datasets import DatasetDict, load_dataset, load_from_disk
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@ -27,7 +27,6 @@ from .aligner import align_dataset
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from .data_utils import merge_dataset, split_dataset
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from .parser import get_dataset_list
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from .preprocess import get_preprocess_and_print_func
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from .template import get_template_and_fix_tokenizer
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if TYPE_CHECKING:
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@ -179,9 +178,6 @@ def _get_preprocessed_dataset(
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load_from_cache_file=(not data_args.overwrite_cache) or (training_args.local_process_index != 0),
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desc="Running tokenizer on dataset",
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)
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if data_args.dataset_map_batch_size:
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# Set the batch size conditionally without considering the default variable of the batch size in the map function
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kwargs.update(batch_size=data_args.dataset_map_batch_size)
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dataset = dataset.map(
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preprocess_func,
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@ -205,17 +201,14 @@ def _get_preprocessed_dataset(
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def get_dataset(
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template: "Template",
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model_args: "ModelArguments",
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data_args: "DataArguments",
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training_args: "Seq2SeqTrainingArguments",
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stage: Literal["pt", "sft", "rm", "ppo", "kto"],
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tokenizer: "PreTrainedTokenizer",
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processor: Optional["ProcessorMixin"] = None,
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) -> Tuple["DatasetModule", "Template"]:
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template = get_template_and_fix_tokenizer(tokenizer, data_args.template, data_args.tool_format)
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if data_args.train_on_prompt and template.efficient_eos:
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raise ValueError("Current template does not support `train_on_prompt`.")
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) -> "DatasetModule":
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# Load tokenized dataset
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if data_args.tokenized_path is not None:
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if has_tokenized_data(data_args.tokenized_path):
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@ -233,7 +226,7 @@ def get_dataset(
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if data_args.streaming:
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dataset_module = {k: v.to_iterable_dataset() for k, v in dataset_module.items()}
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return dataset_module, template
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return dataset_module
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if data_args.streaming:
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raise ValueError("Turn off `streaming` when saving dataset to disk.")
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@ -280,7 +273,8 @@ def get_dataset(
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dataset_module = {}
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if "train" in dataset_dict:
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dataset_module["train_dataset"] = dataset_dict["train"]
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if "validation" in dataset_dict:
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dataset_module["eval_dataset"] = dataset_dict["validation"]
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return dataset_module, template
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return dataset_module
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@ -27,6 +27,7 @@ from .mm_plugin import get_mm_plugin
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if TYPE_CHECKING:
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from transformers import PreTrainedTokenizer
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from ..hparams import DataArguments
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from .formatter import SLOTS, Formatter
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from .mm_plugin import BasePlugin
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@ -344,28 +345,27 @@ def _get_jinja_template(template: "Template", tokenizer: "PreTrainedTokenizer")
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return jinja_template
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def get_template_and_fix_tokenizer(
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tokenizer: "PreTrainedTokenizer",
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name: Optional[str] = None,
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tool_format: Optional[str] = None,
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) -> Template:
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if name in ["llava", "paligemma", "qwen2_vl"]:
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def get_template_and_fix_tokenizer(tokenizer: "PreTrainedTokenizer", data_args: "DataArguments") -> "Template":
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if data_args.template in ["llava", "paligemma", "qwen2_vl"]:
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require_version(
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"transformers>=4.45.0.dev0", "To fix: pip install git+https://github.com/huggingface/transformers.git"
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)
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if name is None:
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if data_args.template is None:
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template = TEMPLATES["empty"] # placeholder
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else:
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template = TEMPLATES.get(name, None)
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template = TEMPLATES.get(data_args.template, None)
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if template is None:
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raise ValueError("Template {} does not exist.".format(name))
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raise ValueError("Template {} does not exist.".format(data_args.template))
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if tool_format is not None:
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logger.info("Using tool format: {}.".format(tool_format))
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if data_args.train_on_prompt and template.efficient_eos:
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raise ValueError("Current template does not support `train_on_prompt`.")
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if data_args.tool_format is not None:
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logger.info("Using tool format: {}.".format(data_args.tool_format))
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eos_slots = [] if template.efficient_eos else [{"eos_token"}]
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template.format_function = FunctionFormatter(slots=eos_slots, tool_format=tool_format)
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template.format_tools = ToolFormatter(tool_format=tool_format)
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template.format_function = FunctionFormatter(slots=eos_slots, tool_format=data_args.tool_format)
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template.format_tools = ToolFormatter(tool_format=data_args.tool_format)
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stop_words = template.stop_words
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if template.replace_eos:
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@ -59,7 +59,7 @@ class Evaluator:
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self.model_args, self.data_args, self.eval_args, finetuning_args = get_eval_args(args)
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self.tokenizer = load_tokenizer(self.model_args)["tokenizer"]
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self.tokenizer.padding_side = "right" # avoid overflow issue in batched inference for llama2
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self.template = get_template_and_fix_tokenizer(self.tokenizer, self.data_args.template)
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self.template = get_template_and_fix_tokenizer(self.tokenizer, self.data_args)
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self.model = load_model(self.tokenizer, self.model_args, finetuning_args)
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self.eval_template = get_eval_template(self.eval_args.lang)
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self.choice_inputs = [self.tokenizer.encode(ch, add_special_tokens=False)[-1] for ch in CHOICES]
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@ -113,10 +113,6 @@ class DataArguments:
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default=None,
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metadata={"help": "Path to save or load the tokenized datasets."},
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)
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dataset_map_batch_size: Optional[int] = field(
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default=None,
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metadata={"help": "Batch size for dataset mapping."},
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)
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def __post_init__(self):
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def split_arg(arg):
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@ -17,7 +17,7 @@
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from typing import TYPE_CHECKING, List, Optional
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from ...data import PairwiseDataCollatorWithPadding, get_dataset
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from ...data import PairwiseDataCollatorWithPadding, get_dataset, get_template_and_fix_tokenizer
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from ...extras.constants import IGNORE_INDEX
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from ...extras.ploting import plot_loss
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from ...hparams import ModelArguments
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@ -41,7 +41,8 @@ def run_dpo(
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):
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tokenizer_module = load_tokenizer(model_args)
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tokenizer = tokenizer_module["tokenizer"]
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dataset_module, template = get_dataset(model_args, data_args, training_args, stage="rm", **tokenizer_module)
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template = get_template_and_fix_tokenizer(tokenizer, data_args)
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dataset_module = get_dataset(template, model_args, data_args, training_args, stage="rm", **tokenizer_module)
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model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
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data_collator = PairwiseDataCollatorWithPadding(
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@ -17,7 +17,7 @@
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from typing import TYPE_CHECKING, List, Optional
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from ...data import KTODataCollatorWithPadding, get_dataset
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from ...data import KTODataCollatorWithPadding, get_dataset, get_template_and_fix_tokenizer
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from ...extras.constants import IGNORE_INDEX
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from ...extras.ploting import plot_loss
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from ...hparams import ModelArguments
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@ -41,7 +41,8 @@ def run_kto(
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):
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tokenizer_module = load_tokenizer(model_args)
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tokenizer = tokenizer_module["tokenizer"]
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dataset_module, template = get_dataset(model_args, data_args, training_args, stage="kto", **tokenizer_module)
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template = get_template_and_fix_tokenizer(tokenizer, data_args)
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dataset_module = get_dataset(template, model_args, data_args, training_args, stage="kto", **tokenizer_module)
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model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
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data_collator = KTODataCollatorWithPadding(
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@ -17,7 +17,7 @@
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from typing import TYPE_CHECKING, List, Optional
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from ...data import MultiModalDataCollatorForSeq2Seq, get_dataset
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from ...data import MultiModalDataCollatorForSeq2Seq, get_dataset, get_template_and_fix_tokenizer
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from ...extras.ploting import plot_loss
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from ...model import load_model, load_tokenizer
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from ..callbacks import fix_valuehead_checkpoint
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@ -41,7 +41,8 @@ def run_ppo(
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):
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tokenizer_module = load_tokenizer(model_args)
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tokenizer = tokenizer_module["tokenizer"]
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dataset_module, template = get_dataset(model_args, data_args, training_args, stage="ppo", **tokenizer_module)
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template = get_template_and_fix_tokenizer(tokenizer, data_args)
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dataset_module = get_dataset(template, model_args, data_args, training_args, stage="ppo", **tokenizer_module)
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model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train, add_valuehead=True)
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tokenizer.padding_side = "left" # use left-padding in generation while using right-padding in training
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@ -20,7 +20,7 @@ from typing import TYPE_CHECKING, List, Optional
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from transformers import DataCollatorForLanguageModeling
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from ...data import get_dataset
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from ...data import get_dataset, get_template_and_fix_tokenizer
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from ...extras.ploting import plot_loss
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from ...model import load_model, load_tokenizer
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from ..trainer_utils import create_modelcard_and_push
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@ -42,7 +42,8 @@ def run_pt(
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):
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tokenizer_module = load_tokenizer(model_args)
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tokenizer = tokenizer_module["tokenizer"]
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dataset_module, _ = get_dataset(model_args, data_args, training_args, stage="pt", **tokenizer_module)
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template = get_template_and_fix_tokenizer(tokenizer, data_args)
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dataset_module = get_dataset(template, model_args, data_args, training_args, stage="pt", **tokenizer_module)
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model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
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data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
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@ -17,7 +17,7 @@
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from typing import TYPE_CHECKING, List, Optional
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from ...data import PairwiseDataCollatorWithPadding, get_dataset
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from ...data import PairwiseDataCollatorWithPadding, get_dataset, get_template_and_fix_tokenizer
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from ...extras.ploting import plot_loss
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from ...model import load_model, load_tokenizer
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from ..callbacks import fix_valuehead_checkpoint
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@ -41,7 +41,8 @@ def run_rm(
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):
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tokenizer_module = load_tokenizer(model_args)
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tokenizer = tokenizer_module["tokenizer"]
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dataset_module, template = get_dataset(model_args, data_args, training_args, stage="rm", **tokenizer_module)
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template = get_template_and_fix_tokenizer(tokenizer, data_args)
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dataset_module = get_dataset(template, model_args, data_args, training_args, stage="rm", **tokenizer_module)
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model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train, add_valuehead=True)
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data_collator = PairwiseDataCollatorWithPadding(template=template, pad_to_multiple_of=8, **tokenizer_module)
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@ -17,7 +17,7 @@
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from typing import TYPE_CHECKING, List, Optional
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from ...data import SFTDataCollatorWith4DAttentionMask, get_dataset
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from ...data import SFTDataCollatorWith4DAttentionMask, get_dataset, get_template_and_fix_tokenizer
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from ...extras.constants import IGNORE_INDEX
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from ...extras.misc import get_logits_processor
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from ...extras.ploting import plot_loss
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@ -43,7 +43,8 @@ def run_sft(
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):
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tokenizer_module = load_tokenizer(model_args)
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tokenizer = tokenizer_module["tokenizer"]
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dataset_module, template = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)
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template = get_template_and_fix_tokenizer(tokenizer, data_args)
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dataset_module = get_dataset(template, model_args, data_args, training_args, stage="sft", **tokenizer_module)
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model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
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if getattr(model, "is_quantized", False) and not training_args.do_train:
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@ -62,7 +63,7 @@ def run_sft(
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# Override the decoding parameters of Seq2SeqTrainer
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training_args.generation_max_length = training_args.generation_max_length or data_args.cutoff_len
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training_args.generation_num_beams = data_args.eval_num_beams or training_args.generation_num_beams
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training_args.remove_unused_columns = False # important for multimodal and pairwise dataset
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training_args.remove_unused_columns = False # important for multimodal dataset
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# Metric utils
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metric_module = {}
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@ -19,7 +19,7 @@ from peft import PeftModel
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from transformers import AutoModelForCausalLM
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from trl import AutoModelForCausalLMWithValueHead
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from ..data import get_dataset
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from ..data import get_dataset, get_template_and_fix_tokenizer
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from ..extras.misc import get_current_device
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from ..hparams import get_infer_args, get_train_args
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from ..model import load_model, load_tokenizer
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@ -105,7 +105,8 @@ def load_reference_model(
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def load_train_dataset(**kwargs) -> "Dataset":
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model_args, data_args, training_args, _, _ = get_train_args(kwargs)
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tokenizer_module = load_tokenizer(model_args)
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dataset_module, _ = get_dataset(model_args, data_args, training_args, stage=kwargs["stage"], **tokenizer_module)
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template = get_template_and_fix_tokenizer(tokenizer_module["tokenizer"], data_args)
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dataset_module = get_dataset(template, model_args, data_args, training_args, kwargs["stage"], **tokenizer_module)
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return dataset_module["train_dataset"]
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@ -19,6 +19,7 @@ import pytest
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from transformers import AutoTokenizer
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from llamafactory.data import get_template_and_fix_tokenizer
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from llamafactory.hparams import DataArguments
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if TYPE_CHECKING:
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@ -51,7 +52,7 @@ def _check_single_template(
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tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=use_fast, token=HF_TOKEN)
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content_str = tokenizer.apply_chat_template(MESSAGES, tokenize=False)
|
||||
content_ids = tokenizer.apply_chat_template(MESSAGES, tokenize=True)
|
||||
template = get_template_and_fix_tokenizer(tokenizer, name=template_name)
|
||||
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template=template_name))
|
||||
prompt_ids, answer_ids = template.encode_oneturn(tokenizer, MESSAGES)
|
||||
assert content_str == prompt_str + answer_str + extra_str
|
||||
assert content_ids == prompt_ids + answer_ids + tokenizer.encode(extra_str, add_special_tokens=False)
|
||||
@ -78,7 +79,7 @@ def _check_template(model_id: str, template_name: str, prompt_str: str, answer_s
|
||||
@pytest.mark.parametrize("use_fast", [True, False])
|
||||
def test_encode_oneturn(use_fast: bool):
|
||||
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA, use_fast=use_fast)
|
||||
template = get_template_and_fix_tokenizer(tokenizer, name="llama3")
|
||||
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
|
||||
prompt_ids, answer_ids = template.encode_oneturn(tokenizer, MESSAGES)
|
||||
prompt_str = (
|
||||
"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|>"
|
||||
@ -93,7 +94,7 @@ def test_encode_oneturn(use_fast: bool):
|
||||
@pytest.mark.parametrize("use_fast", [True, False])
|
||||
def test_encode_multiturn(use_fast: bool):
|
||||
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA, use_fast=use_fast)
|
||||
template = get_template_and_fix_tokenizer(tokenizer, name="llama3")
|
||||
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
|
||||
encoded_pairs = template.encode_multiturn(tokenizer, MESSAGES)
|
||||
prompt_str_1 = (
|
||||
"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|>"
|
||||
|
Loading…
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Reference in New Issue
Block a user