# Copyright 2020-2025 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import contextlib import os from collections import defaultdict from collections.abc import Mapping from dataclasses import dataclass from pathlib import Path from typing import Any, Callable, Optional, TypeVar, Union import torch import torch.nn as nn import transformers from accelerate import PartialState, logging from datasets import Dataset, IterableDataset from transformers import ( AutoConfig, AutoProcessor, BaseImageProcessor, DataCollator, FeatureExtractionMixin, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, Trainer, TrainingArguments, is_wandb_available, ) from transformers.data.data_collator import DataCollatorMixin from transformers.trainer_callback import TrainerCallback from transformers.trainer_utils import EvalPrediction from transformers.utils import is_peft_available from ..data_utils import ( apply_chat_template, is_conversational, is_conversational_from_value, maybe_convert_to_chatml, pack_dataset, prepare_multimodal_messages, truncate_dataset, ) from ..models import clone_chat_template, get_act_offloading_ctx_manager, prepare_peft_model from .sft_config import SFTConfig from .utils import ( entropy_from_logits, flush_left, generate_model_card, get_comet_experiment_url, pad, selective_log_softmax, ) if is_peft_available(): from peft import PeftConfig, PeftModel if is_wandb_available(): import wandb logger = logging.get_logger(__name__) TListOrMapping = TypeVar("TListOrMapping", list, Mapping) def remove_none_values(example: TListOrMapping) -> TListOrMapping: """ Recursively removes entries with `None` values from a nested structure (list or dictionary). Args: example (`list` or `Mapping`): Input nested structure (list or dictionary) from which to remove `None`. Example: ```python >>> [ ... { ... "a": {"aa": None, "ab": 1}, ... "b": "my_string", ... } ... ] >>> remove_none_values(example) [{'a': {'ab': 1}, 'b': 'my_string'}] ``` """ if isinstance(example, list): return [remove_none_values(value) if isinstance(value, (dict, list)) else value for value in example] elif isinstance(example, Mapping): return { key: remove_none_values(value) if isinstance(value, (dict, list)) else value for key, value in example.items() if value is not None } else: raise TypeError("Input must be a list or a dictionary.") @dataclass class DataCollatorForLanguageModeling(DataCollatorMixin): """ Data collator used for language modeling data. Inputs are dynamically padded to the maximum length of a batch. This collator expects each example in the input list to be a dictionary containing at least the `"input_ids"` key. If the input contains a `"completion_mask"`, it is used to set the labels to `-100` for tokens that are not in the completion. If `"assistant_masks"` are present, they are used to set the labels to `-100` for tokens that are not in the assistant part of the sequence. The collator returns a dictionary containing the following keys: - `"input_ids"`: Tensor of input IDs, padded to the maximum length of the batch. - `"attention_mask"`: Tensor of attention mask, padded to the maximum length of the batch. - `"position_ids"`: Tensor of position IDs, padded to the maximum length of the batch. - `"labels"`: Tensor of labels, padded to the maximum length of the batch. If `completion_only_loss` is set to `True`, tokens that are not in the completion are set to -100. If `assistant_masks` are present, tokens that are not in the assistant part of the sequence are set to -100. Args: pad_token_id (`int`): Token ID to use for padding. completion_only_loss (`bool`, *optional*, defaults to `True`): When the input contains a completion mask (`completion_mask`), the labels are set to -100 for the tokens that are no in the completion. padding_free (`bool`, *optional*, defaults to `False`): If set to `True`, the sequences will be flattened into a single sequence, and the position IDs will be generated accordingly. pad_to_multiple_of (`int` or `None`, *optional*, defaults to `None`): If set, the sequences will be padded to a multiple of this value. return_tensors (`str`, *optional*, defaults to `"pt"`): Type of Tensor to return. Only `"pt"` is currently supported. Examples: ```python >>> from trl.trainer.sft_trainer import DataCollatorForLanguageModeling >>> collator = DataCollatorForLanguageModeling(pad_token_id=0) >>> examples = [{"input_ids": [1, 2, 3]}, {"input_ids": [4, 5]}] >>> collator(examples) {'input_ids': tensor([[ 1, 2, 3], [ 4, 5, 0]]), 'attention_mask': tensor([[ 1, 1, 1], [ 1, 1, 0]]), 'position_ids': tensor([[0, 1, 2], [0, 1, 0]]), 'labels': tensor([[ 1, 2, 3], [ 4, 5, -100]])} >>> # With completion mask >>> examples = [ ... {"input_ids": [1, 2, 3], "completion_mask": [0, 1, 1]}, ... {"input_ids": [4, 5], "completion_mask": [0, 1]}, ... ] >>> collator(examples) {'input_ids': tensor([[ 1, 2, 3], [ 4, 5, 0]]), 'attention_mask': tensor([[ 1, 1, 1], [ 1, 1, 0]]), 'position_ids': tensor([[0, 1, 2], [0, 1, 0]]), 'labels': tensor([[-100, 2, 3], [-100, 5, -100]])} >>> # With padding_free >>> collator = DataCollatorForLanguageModeling(pad_token_id=0, padding_free=True) >>> collator(examples) {'input_ids': tensor([[ 1, 2, 3, 4, 5]]), 'attention_mask': tensor([[1, 1, 1, 1, 1]]), 'position_ids': tensor([[0, 1, 2, 0, 1]]), 'labels': tensor([[1, 2, 3, 4, 5]])} ``` """ pad_token_id: int completion_only_loss: bool = True padding_free: bool = False return_position_ids: bool = True pad_to_multiple_of: Optional[int] = None return_tensors: str = "pt" def torch_call(self, examples: list[Union[list[int], Any, dict[str, Any]]]) -> dict[str, Any]: # Convert to tensor input_ids = [torch.tensor(example["input_ids"]) for example in examples] # Check if we have meaningful seq_lengths from packing (restarting sequences) has_packed_position_ids = self.return_position_ids and "seq_lengths" in examples[0] and self.padding_free # For packing with position_ids, we should NOT create attention_mask as it causes # FlashAttention to ignore position_ids and compute wrong cu_seq_lens from the all-1s mask if not has_packed_position_ids: attention_mask = [torch.ones_like(input_ids) for input_ids in input_ids] if self.return_position_ids: if "seq_lengths" in examples[0]: position_ids = self.get_position_ids_from_packed_seq_lengths( [example["seq_lengths"] for example in examples] ) else: position_ids = [torch.arange(len(ids)) for ids in input_ids] if "labels" in examples[0]: labels = [torch.tensor(example["labels"]) for example in examples] else: labels = [torch.tensor(example["input_ids"]) for example in examples] if self.completion_only_loss and "completion_mask" in examples[0]: completion_mask = [torch.tensor(example["completion_mask"]) for example in examples] if "assistant_masks" in examples[0]: assistant_masks = [torch.tensor(example["assistant_masks"]) for example in examples] # If padding_free, flatten everything into a single sequence output = {} if self.padding_free: input_ids = [torch.cat(input_ids, dim=0)] if not has_packed_position_ids: attention_mask = [torch.cat(attention_mask, dim=0)] if self.return_position_ids: position_ids = [torch.cat(position_ids, dim=0)] labels = [torch.cat(labels, dim=0)] if self.completion_only_loss and "completion_mask" in examples[0]: completion_mask = [torch.cat(completion_mask, dim=0)] if "assistant_masks" in examples[0]: assistant_masks = [torch.cat(assistant_masks, dim=0)] # Pad output["input_ids"] = pad( input_ids, padding_value=self.pad_token_id, padding_side="right", pad_to_multiple_of=self.pad_to_multiple_of, ) if not has_packed_position_ids: output["attention_mask"] = pad( attention_mask, padding_value=0, padding_side="right", pad_to_multiple_of=self.pad_to_multiple_of ) if self.return_position_ids: output["position_ids"] = pad( position_ids, padding_value=0, padding_side="right", pad_to_multiple_of=self.pad_to_multiple_of ) output["labels"] = pad( labels, padding_value=-100, padding_side="right", pad_to_multiple_of=self.pad_to_multiple_of ) if self.completion_only_loss and "completion_mask" in examples[0]: completion_mask = pad( completion_mask, padding_value=0, padding_side="right", pad_to_multiple_of=self.pad_to_multiple_of ) output["labels"][completion_mask == 0] = -100 # mask everything that is not in the completion if "assistant_masks" in examples[0]: assistant_masks = pad( assistant_masks, padding_value=0, padding_side="right", pad_to_multiple_of=self.pad_to_multiple_of ) output["labels"][assistant_masks == 0] = -100 return output @staticmethod def get_position_ids_from_packed_seq_lengths(batch_seq_lengths: list[list[int]]) -> list[torch.Tensor]: """ Get position IDs for packed sequences. Args: batch_seq_lengths (`list[list[int]]`): A list of lists containing the lengths of each individual document in the packed batch. Return: `list[torch.Tensor]`: A list of tensors containing the position IDs for each packed sequence. """ # Get lengths per row example_lengths = [sum(seq_lengths) for seq_lengths in batch_seq_lengths] # Flat list of lengths batch_seq_lengths = torch.tensor( [seq_length for seq_lengths in batch_seq_lengths for seq_length in seq_lengths] ) position_ids = torch.ones(sum(example_lengths), dtype=batch_seq_lengths.dtype) position_ids[0] = 0 # Reset position ids to 0 at the start of each sequence position_ids[batch_seq_lengths[:-1].cumsum(0)] = -(batch_seq_lengths[:-1] - 1) position_ids = position_ids.cumsum(0) # Split back into one tensor per example return list(position_ids.split(example_lengths)) @dataclass class DataCollatorForVisionLanguageModeling(DataCollatorMixin): """ Data collator for vision-language modeling tasks. Unlike text-only datasets—where the collator typically receives pre-tokenized inputs ready for batching, vision-language data processing involves converting images into pixel values. This conversion is disk-intensive, making upfront preprocessing of the entire dataset impractical. Therefore, this collator performs tokenization and image processing on-the-fly to efficiently prepare batches. Each input example should be a dictionary containing at least: - An `"images"` key holding the image data. - [language modeling](#language-modeling) type: either a `"messages"` key for conversational inputs or a `"text"` key for standard text inputs. - [prompt-completion](#prompt-completion) type: keys `"prompt"` and `"completion"` for the prompt and completion. The collator outputs a dictionary including: - `"input_ids"`: Tensor of token IDs. - `"attention_mask"`: Tensor indicating attention mask. - `"pixel_values"`: Tensor representing image pixel values. - `"labels"`: Tensor for training labels. Additional keys may be present depending on the processor, such as `"image_grid_thw"`. Args: processor (`ProcessorMixin`): The processor used to tokenize text and process images. It must be a subclass of `ProcessorMixin` and include a `tokenizer` with a defined `pad_token_id`. max_length (`int` or `None`, optional, defaults to `None`): Maximum sequence length for input tokens. If `None`, no truncation is applied. completion_only_loss (`bool`, *optional*, defaults to `False`): Whether to compute loss only on the completion part of the sequence. When `True`, the labels for the prompt part are set to -100. It requires the dataset type to be prompt-completion. pad_to_multiple_of (`int` or `None`, optional, defaults to `None`): If set, the sequences will be padded to a multiple of this value. dataset_text_field (`str`, optional, defaults to `"text"`): Name of the column that contains text data in the dataset. This parameter is only relevant for [standard datasets format](dataset_formats#standard). return_tensors (`str`, optional, defaults to `"pt"`): The tensor type to return. Currently, only `"pt"` (PyTorch tensors) is supported. Example: ```python >>> from trl.trainer.sft_trainer import DataCollatorForVisionLanguageModeling >>> from transformers import AutoProcessor >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct") >>> collator = DataCollatorForVisionLanguageModeling(processor) >>> examples = [ ... {"images": [Image.open("image_0.png")], "messages": [{"role": "user", "content": "What is this?"}]}, ... {"images": [Image.open("image_1.png")], "messages": [{"role": "user", "content": "Describe this image."}]}, ... ] >>> collator(examples) {'input_ids': tensor([[151644, 8948, 198, 2610, 525, 264, 10950, 17847, 13, 151645, 198, 151644, 872, 198, 151652, 151655, 151655, 151655, 151655, 151653, 3838, 374, 419, 30, 151645, 198], [151644, 8948, 198, 2610, 525, 264, 10950, 17847, 13, 151645, 198, 151644, 872, 198, 151652, 151655, 151655, 151655, 151655, 151653, 74785, 419, 2168, 13, 151645, 198]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'pixel_values': tensor([[-0.9893, 0.1785, 1.5362, ..., -0.0582, 0.8661, -0.2431], [-0.2302, 0.9522, -1.1061, ..., 0.0555, 1.3354, -0.6412], [ 1.2150, 0.9084, 0.7041, ..., 0.2404, -0.8403, -0.5133], ..., [ 0.6895, 0.2807, 0.2515, ..., -0.2004, -1.2100, 0.0555], [ 0.8209, -0.9748, 1.5654, ..., 1.6055, -0.4706, 0.5817], [-1.0915, 0.4559, 0.9230, ..., 0.5106, 0.0982, -0.1720]]), 'image_grid_thw': tensor([[1, 4, 4], [1, 4, 4]]), 'labels': tensor([[151644, 8948, 198, 2610, 525, 264, 10950, 17847, 13, 151645, 198, 151644, 872, 198, 151652, 151655, 151655, 151655, 151655, 151653, 3838, 374, 419, 30, 151645, 198], [151644, 8948, 198, 2610, 525, 264, 10950, 17847, 13, 151645, 198, 151644, 872, 198, 151652, 151655, 151655, 151655, 151655, 151653, 74785, 419, 2168, 13, 151645, 198]])} ``` """ processor: ProcessorMixin max_length: Optional[int] = None completion_only_loss: bool = False # default not used in practice; SFTTrainer always passes the relevant value pad_to_multiple_of: Optional[int] = None dataset_text_field: str = "text" return_tensors: str = "pt" def torch_call(self, examples: list[Union[list[int], Any, dict[str, Any]]]) -> dict[str, Any]: if "messages" in examples[0] or self.dataset_text_field in examples[0]: if self.completion_only_loss: raise ValueError( "The `completion_only_loss` argument is not supported for language modeling datasets." ) return self._collate_language_modeling(examples) elif "prompt" in examples[0] and "completion" in examples[0]: return self._collate_prompt_completion(examples) else: raise KeyError(f"Unexpected input keys in examples: {list(examples[0].keys())}.") def _collate_language_modeling(self, examples: list[Union[list[int], Any, dict[str, Any]]]) -> dict[str, Any]: images = [example["images"] for example in examples] if "messages" in examples[0]: # conversational case for example in examples: prepare_multimodal_messages(example["messages"], len(example["images"])) messages = [example["messages"] for example in examples] texts = self.processor.apply_chat_template(messages) elif self.dataset_text_field in examples[0]: # standard case texts = [example[self.dataset_text_field] for example in examples] else: raise KeyError( "The input examples must contain either 'messages' for conversational data or 'text' for standard " "data." ) output = self.processor( images=images, text=texts, padding=True, padding_side="right", pad_to_multiple_of=self.pad_to_multiple_of, truncation=self.max_length is not None, max_length=self.max_length, return_tensors=self.return_tensors, add_special_tokens=False, # to avoid adding the BOS, twice see https://huggingface.co/blog/qgallouedec/gotchas-in-tokenizer-behavior#7-chat-template-and-tokenization-dont-compose-due-to-special-tokens ) labels = output["input_ids"].clone() labels[output["attention_mask"] == 0] = -100 # We mask only padding tokens (-100) in the labels. Vision tokens are left unchanged because their handling in # loss computation has to be done by the model, and masking them here would be infeasible in practice as vision # token definitions vary across architectures. output["labels"] = labels return output def _collate_prompt_completion(self, examples: list[Union[list[int], Any, dict[str, Any]]]) -> dict[str, Any]: if self.pad_to_multiple_of is not None: raise NotImplementedError( "Padding to a multiple of a value is not yet implemented for vision-language modeling and " "prompt-completion data yet." ) images = [example["images"] for example in examples] if is_conversational(examples[0]): # conversational case for example in examples: prepare_multimodal_messages(example["prompt"] + example["completion"], len(example["images"])) examples = [apply_chat_template(example, self.processor) for example in examples] prompts = [example["prompt"] for example in examples] completions = [example["completion"] for example in examples] processed_prompts = self.processor( images=images, text=prompts, padding=True, padding_side="left", return_tensors=self.return_tensors, add_special_tokens=False, # to avoid adding the BOS, twice see https://huggingface.co/blog/qgallouedec/gotchas-in-tokenizer-behavior#7-chat-template-and-tokenization-dont-compose-due-to-special-tokens ) processed_completions = self.processor( text=completions, padding=True, padding_side="right", return_tensors=self.return_tensors, add_special_tokens=False, # to avoid adding the BOS, twice see https://huggingface.co/blog/qgallouedec/gotchas-in-tokenizer-behavior#7-chat-template-and-tokenization-dont-compose-due-to-special-tokens ) # Concatenate prompts and completions prompt_ids, completion_ids = processed_prompts["input_ids"], processed_completions["input_ids"] prompt_mask, completion_mask = processed_prompts["attention_mask"], processed_completions["attention_mask"] input_ids = torch.cat((prompt_ids, completion_ids), dim=1) attention_mask = torch.cat((prompt_mask, completion_mask), dim=1) completion_mask = torch.cat((torch.zeros_like(prompt_mask), completion_mask), dim=1) # Flush left to reduce padding attention_mask, input_ids, completion_mask = flush_left(attention_mask, input_ids, completion_mask) # Truncate if necessary if self.max_length is not None: input_ids = input_ids[:, : self.max_length] attention_mask = attention_mask[:, : self.max_length] completion_mask = completion_mask[:, : self.max_length] # Create labels and mask padding tokens labels = input_ids.clone() labels[attention_mask == 0] = -100 if self.completion_only_loss: labels[completion_mask == 0] = -100 # Build the output dictionary output = processed_prompts # we take processed_prompts because it contains the images output["input_ids"] = input_ids output["attention_mask"] = attention_mask output["labels"] = labels return output def dft_loss(outputs, labels, num_items_in_batch): """ DFT loss function, as presented in [On the Generalization of SFT: A Reinforcement Learning Perspective with Reward Rectification](https://huggingface.co/papers/2508.05629) """ labels = nn.functional.pad(labels, (0, 1), value=-100) shift_labels = labels[..., 1:].contiguous() loss_mask = shift_labels != -100 shift_labels[~loss_mask] = 0 logprobs = selective_log_softmax(outputs.logits, shift_labels) per_token_loss = -logprobs.exp().detach() * logprobs loss = (per_token_loss * loss_mask).sum() / num_items_in_batch return loss class SFTTrainer(Trainer): """ Trainer for Supervised Fine-Tuning (SFT) method. This class is a wrapper around the [`~transformers.Trainer`] class and inherits all of its attributes and methods. Example: ```python from datasets import load_dataset from trl import SFTTrainer dataset = load_dataset("roneneldan/TinyStories", split="train[:1%]") trainer = SFTTrainer(model="Qwen/Qwen2-0.5B-Instruct", train_dataset=dataset) trainer.train() ``` Args: model (`Union[str, PreTrainedModel]`): Model to be trained. Can be either: - A string, being the *model id* of a pretrained model hosted inside a model repo on huggingface.co, or a path to a *directory* containing model weights saved using [`~transformers.PreTrainedModel.save_pretrained`], e.g., `'./my_model_directory/'`. The model is loaded using `.from_pretrained` (where `` is derived from the model config) with the keyword arguments in `args.model_init_kwargs`. - A [`~transformers.PreTrainedModel`] object. If you're training a model with an MoE architecture and want to include the load balancing/auxilliary loss as a part of the final loss, remember to set the `output_router_logits` config of the model to `True`. args ([`SFTConfig`], *optional*, defaults to `None`): Configuration for this trainer. If `None`, a default configuration is used. data_collator ([`~transformers.DataCollator`] or `None`, *optional*): Function to use to form a batch from a list of elements of the processed `train_dataset` or `eval_dataset`. Will default to [`~trainer.sft_trainer.DataCollatorForLanguageModeling`] if the model is a language model and [`~trainer.sft_trainer.DataCollatorForVisionLanguageModeling`] if the model is a vision-language model. train_dataset ([`~datasets.Dataset`] or [`~datasets.IterableDataset`]): Dataset to use for training. SFT supports both [language modeling](#language-modeling) type and [prompt-completion](#prompt-completion) type. The format of the samples can be either: - [Standard](dataset_formats#standard): Each sample contains plain text. - [Conversational](dataset_formats#conversational): Each sample contains structured messages (e.g., role and content). The trainer also supports processed datasets (tokenized) as long as they contain an `input_ids` field. eval_dataset ([`~datasets.Dataset`], [`~datasets.IterableDataset`] or `dict[str, Union[Dataset, IterableDataset]]`): Dataset to use for evaluation. It must meet the same requirements as `train_dataset`. processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.ProcessorMixin`] or `None`, *optional*, defaults to `None`): Processing class used to process the data. If `None`, the processing class is loaded from the model's name with [`~transformers.AutoProcessor.from_pretrained`]. A padding token, `tokenizer.pad_token`, must be set. If the processing class has not set a padding token, `tokenizer.eos_token` will be used as the default. compute_loss_func (`Callable` or `None`, *optional*, defaults to `None`): A function that accepts the raw model outputs, labels, and the number of items in the entire accumulated batch (batch_size * gradient_accumulation_steps) and returns the loss. For example, see the default [loss function](https://github.com/huggingface/transformers/blob/052e652d6d53c2b26ffde87e039b723949a53493/src/transformers/trainer.py#L3618) used by [`Trainer`]. compute_metrics (`Callable[[EvalPrediction], dict]` or `None`, *optional*, defaults to `None`): The function that will be used to compute metrics at evaluation. Must take a [`~transformers.EvalPrediction`] and return a dictionary string to metric values. When passing [`SFTConfig`] with `batch_eval_metrics` set to `True`, your `compute_metrics` function must take a boolean `compute_result` argument. This will be triggered after the last eval batch to signal that the function needs to calculate and return the global summary statistics rather than accumulating the batch-level statistics. callbacks (list of [`~transformers.TrainerCallback`] or `None`, *optional*, defaults to `None`): List of callbacks to customize the training loop. Will add those to the list of default callbacks detailed in [here](https://huggingface.co/docs/transformers/main_classes/callback). If you want to remove one of the default callbacks used, use the [`~transformers.Trainer.remove_callback`] method. optimizers (`tuple[Optional[torch.optim.Optimizer], Optional[torch.optim.lr_scheduler.LambdaLR]]`, *optional*, defaults to `(None, None)`): A tuple containing the optimizer and the scheduler to use. Will default to an instance of `AdamW` on your model and a scheduler given by [`~transformers.get_linear_schedule_with_warmup`] controlled by `args`. optimizer_cls_and_kwargs (`tuple[Type[torch.optim.Optimizer], Dict[str, Any]]`, *optional*, defaults to `None`): A tuple containing the optimizer class and keyword arguments to use. Overrides `optim` and `optim_args` in `args`. Incompatible with the `optimizers` argument. Unlike `optimizers`, this argument avoids the need to place model parameters on the correct devices before initializing the Trainer. preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *optional*, defaults to `None`): A function that preprocess the logits right before caching them at each evaluation step. Must take two tensors, the logits and the labels, and return the logits once processed as desired. The modifications made by this function will be reflected in the predictions received by `compute_metrics`. Note that the labels (second parameter) will be `None` if the dataset does not have them. peft_config ([`~peft.PeftConfig`] or `None`, *optional*, defaults to `None`): PEFT configuration used to wrap the model. If `None`, the model is not wrapped. formatting_func (`Callable` or `None`, *optional*, defaults to `None`): Formatting function applied to the dataset before tokenization. Applying the formatting function explicitly converts the dataset into a [language modeling](#language-modeling) type. """ _tag_names = ["trl", "sft"] def __init__( self, model: Union[str, nn.Module, PreTrainedModel], args: Optional[Union[SFTConfig, TrainingArguments]] = None, data_collator: Optional[DataCollator] = None, # type: ignore train_dataset: Optional[Union[Dataset, IterableDataset]] = None, eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None, processing_class: Optional[Union[PreTrainedTokenizerBase, ProcessorMixin]] = None, compute_loss_func: Optional[Callable] = None, compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None, callbacks: Optional[list[TrainerCallback]] = None, optimizers: tuple[Optional[torch.optim.Optimizer], Optional[torch.optim.lr_scheduler.LambdaLR]] = (None, None), optimizer_cls_and_kwargs: Optional[tuple[type[torch.optim.Optimizer], dict[str, Any]]] = None, preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, peft_config: Optional["PeftConfig"] = None, formatting_func: Optional[Callable[[dict], str]] = None, ): # Args if args is None: model_name = model if isinstance(model, str) else model.config._name_or_path model_name = model_name.split("/")[-1] args = SFTConfig(f"{model_name}-SFT") elif isinstance(args, TrainingArguments) and not isinstance(args, SFTConfig): dict_args = args.to_dict() dict_args["hub_token"] = args.hub_token # to_dict hides the hub_token dict_args.pop("push_to_hub_token") args = SFTConfig(**dict_args) # Model model_init_kwargs = args.model_init_kwargs or {} if isinstance(model, str): model_id = model dtype = model_init_kwargs.get("dtype") if isinstance(dtype, torch.dtype) or dtype == "auto" or dtype is None: pass # dtype is already a torch.dtype or "auto" or None elif isinstance(dtype, str) and dtype in ["bfloat16", "float16", "float32"]: dtype = getattr(torch, dtype) model_init_kwargs["dtype"] = dtype else: raise ValueError( "Invalid `dtype` passed to `SFTConfig`. Expected either 'auto' or a string representing " f"a valid `torch.dtype` (e.g., 'float32'), but got {dtype}." ) config = AutoConfig.from_pretrained(model_id) architecture = getattr(transformers, config.architectures[0]) model = architecture.from_pretrained(model_id, **model_init_kwargs) else: model_id = model.config._name_or_path if args.model_init_kwargs is not None: logger.warning( "You passed `model_init_kwargs` to the `SFTConfig`, but your model is already instantiated. " "The `model_init_kwargs` will be ignored." ) # Processing class if processing_class is None: processing_class = AutoProcessor.from_pretrained(model_id) # Handle pad token for processors or tokenizers if isinstance(processing_class, ProcessorMixin): tokenizer = processing_class.tokenizer self._is_vlm = True elif isinstance(processing_class, PreTrainedTokenizerBase): tokenizer = processing_class self._is_vlm = False else: raise TypeError("The `processing_class` must be either a `PreTrainedTokenizerBase` or a `ProcessorMixin`") if args.eos_token is not None: eos_token = args.eos_token eos_token_id = tokenizer.convert_tokens_to_ids(eos_token) if eos_token_id is None: raise ValueError( f"The specified `eos_token` ('{eos_token}') is not found in the vocabulary of the given " f"`processing_class` ({processing_class.__class__.__name__}). Ensure that the `eos_token` exists " "in the vocabulary before using it as an EOS token." ) tokenizer.eos_token_id = eos_token_id if args.chat_template_path is not None: if os.path.isfile(args.chat_template_path) and args.chat_template_path.endswith((".jinja", ".j2")): with open(args.chat_template_path, encoding="utf-8") as chat_template_file: processing_class.chat_template = chat_template_file.read() added_tokens = [] else: model, processing_class, added_tokens = clone_chat_template( model, processing_class, args.chat_template_path ) else: added_tokens = [] # Catch some wrong configurations related to VLMs if self._is_vlm and args.packing: raise ValueError( "Packing is not supported for vision-language models. Please set `packing=False` in the SFTConfig." ) if self._is_vlm and args.padding_free: raise ValueError( "Padding-free training is yet not supported for vision-language models. Please set " "`padding_free=False` in the `SFTConfig`." ) if self._is_vlm and args.assistant_only_loss: raise ValueError( "Assistant-only loss is not yet supported for vision-language models. Please set " "`assistant_only_loss=False` in the `SFTConfig`." ) # PEFT configuration and model wrapping if peft_config is not None: if added_tokens: # Ensure that the added tokens are trainable if peft_config.trainable_token_indices is None: peft_config.trainable_token_indices = {"embed_tokens": added_tokens} elif "embed_tokens" not in peft_config.trainable_token_indices: peft_config.trainable_token_indices["embed_tokens"] = added_tokens else: peft_config.trainable_token_indices["embed_tokens"].extend(added_tokens) # Ensure that the lm_head is trainable if peft_config.modules_to_save is None or "lm_head" not in peft_config.modules_to_save: logger.warning( "Cloning chat template added new tokens to the tokenizer, but 'lm_head' is not in PEFT's " "`modules_to_save`. As a result, the model may not learn to generate outputs with these new " "tokens, leading to degraded generation quality. To fix this, add " "`modules_to_save=['lm_head']` to your PEFT configuration." ) if peft_config.modules_to_save is None: peft_config.modules_to_save = ["lm_head"] else: peft_config.modules_to_save.append("lm_head") # In Prompt Tuning a small set of trainable virtual tokens (continuous prompt embeddings) is prepended to the # input. We store the number of these tokens so we can account for them correctly when calculating accuracy. self.num_virtual_tokens = 0 if peft_config is not None or (is_peft_available() and isinstance(model, PeftModel)): model = prepare_peft_model(model, peft_config, args) if model.active_adapter in model.peft_config: peft_model_config = model.peft_config[model.active_adapter] self.num_virtual_tokens = getattr(peft_model_config, "num_virtual_tokens", 0) # Data collator # BFD packing requires padding-free mode; otherwise, the collator outputs padded attention masks, causing # FlashAttention to ignore position_ids and recompute them incorrectly from the padded attention mask. self.padding_free = args.padding_free or (args.packing and args.packing_strategy == "bfd") use_flash_attention = model.config._attn_implementation in [ "flash_attention_2", "flash_attention_3", "kernels-community/vllm-flash-attn3", ] if self.padding_free: if data_collator is not None: raise ValueError("Passing a custom data collator is not supported when using padding-free.") if args.packing and args.packing_strategy == "wrapped": logger.warning( "You are passing `padding_free=True` with the 'wrapped' packing strategy, which is not " "recommended. Please refer to the documentation to understand why this is not recommended." ) if not use_flash_attention: logger.warning( "Padding-free training is enabled, but the attention implementation is not set to " "'flash_attention_2'. Padding-free training flattens batches into a single sequence, and " "'flash_attention_2' is the only known attention mechanism that reliably supports this. Using " "other implementations may lead to unexpected behavior. To ensure compatibility, set " "`attn_implementation='flash_attention_2'` in the model configuration, or verify that your " "attention mechanism can handle flattened sequences." ) if args.per_device_train_batch_size == 1 and not args.packing: logger.warning( "You are using a per_device_train_batch_size of 1 with padding-free training. Using a batch size " "of 1 anihilate the benefits of padding-free training. Please consider increasing the batch size " "to at least 2." ) # Decide whether to use completion-only loss: if not specified, then it is set to True if the dataset format # is prompt-completion, and False if the dataset format is language modeling. dataset_sample = next(iter(train_dataset)) if args.completion_only_loss is None: self.completion_only_loss = "prompt" in dataset_sample and "completion" in dataset_sample else: self.completion_only_loss = args.completion_only_loss if data_collator is None and not self._is_vlm: # Get the pad token: if not provided, use the one from the processing class or the eos token # if the processing class does not have a pad token. pad_token = args.pad_token or tokenizer.pad_token or tokenizer.eos_token pad_token_id = tokenizer.convert_tokens_to_ids(pad_token) if pad_token_id is None: raise ValueError( f"The specified `pad_token` ('{pad_token}') is not found in the vocabulary of the given " f"`processing_class` ({processing_class.__class__.__name__}). Ensure that the `pad_token` exists " "in the vocabulary before using it as a padding token." ) data_collator = DataCollatorForLanguageModeling( pad_token_id=pad_token_id, completion_only_loss=self.completion_only_loss, padding_free=self.padding_free, # Using position_ids without flash_attn hurts the training return_position_ids=use_flash_attention, pad_to_multiple_of=args.pad_to_multiple_of, ) elif data_collator is None and self._is_vlm: data_collator = DataCollatorForVisionLanguageModeling( processor=processing_class, max_length=args.max_length, completion_only_loss=self.completion_only_loss, pad_to_multiple_of=args.pad_to_multiple_of, dataset_text_field=args.dataset_text_field, ) if args.packing and args.packing_strategy == "bfd" and not use_flash_attention: logger.warning( "You are using packing, but the attention implementation is not set to 'flash_attention_2' or " "'kernels-community/vllm-flash-attn3'. Packing flattens batches into a single sequence, and Flash " "Attention is the only known attention mechanisms that reliably support this. Using other " "implementations may lead to cross-contamination between batches. To avoid this, either disable " "packing by setting `packing=False`, or set `attn_implementation='flash_attention_2'` or " "`attn_implementation='kernels-community/vllm-flash-attn3'` in the model configuration." ) if args.assistant_only_loss and not is_conversational(dataset_sample): raise ValueError( "You set `assistant_only_loss=True`, but the dataset is not conversational. This option is only " "supported for conversational datasets." ) # Dataset # Skip dataset preparation if `skip_prepare_dataset=True` in `dataset_kwargs`, or if it's a VLM, where # preprocessing (e.g., image-to-pixel conversion) is too costly and done on the fly instead. skip_prepare_dataset = ( args.dataset_kwargs is not None and args.dataset_kwargs.get("skip_prepare_dataset", False) or self._is_vlm ) if not skip_prepare_dataset: if self.completion_only_loss and formatting_func: raise ValueError( "A formatting function was provided while `completion_only_loss=True`, which is incompatible. " "Using a formatter converts the dataset to a language modeling type, conflicting with " "completion-only loss. To resolve this, apply your formatting function before passing the " "dataset, or disable `completion_only_loss` in `SFTConfig`." ) train_dataset = self._prepare_dataset( train_dataset, processing_class, args, args.packing, formatting_func, "train" ) if eval_dataset is not None: packing = args.packing if args.eval_packing is None else args.eval_packing if isinstance(eval_dataset, dict): eval_dataset = { key: self._prepare_dataset(dataset, processing_class, args, packing, formatting_func, key) for key, dataset in eval_dataset.items() } else: eval_dataset = self._prepare_dataset( eval_dataset, processing_class, args, packing, formatting_func, "eval" ) # Loss function if args.loss_type == "nll": pass # use the default loss elif args.loss_type == "dft": if compute_loss_func is not None: raise ValueError( "You passed a `compute_loss_func` together with `loss_type='dft'` to the `SFTTrainer`. " "When using `loss_type='dft'`, the loss function is internally set to the DFT loss, so passing a " "`compute_loss_func` is not allowed." ) compute_loss_func = dft_loss else: raise ValueError(f"Invalid `loss_type` {args.loss_type} passed. Supported values are 'nll' and 'dft'.") # Initialize the metrics self._metrics = {"train": defaultdict(list), "eval": defaultdict(list)} self._total_train_tokens = 0 # Initialize the Trainer. Parent class will handle: # - DeepSpeed configuration (through create_accelerator_and_postprocess) # - FSDP setup # - Distributed training setup # - Optimizer and scheduler creation super().__init__( model=model, args=args, data_collator=data_collator, train_dataset=train_dataset, eval_dataset=eval_dataset, processing_class=processing_class, compute_loss_func=compute_loss_func, compute_metrics=compute_metrics, callbacks=callbacks, optimizers=optimizers, optimizer_cls_and_kwargs=optimizer_cls_and_kwargs, preprocess_logits_for_metrics=preprocess_logits_for_metrics, ) # Initialize activation offloading context if self.args.activation_offloading: self.maybe_activation_offload_context = get_act_offloading_ctx_manager(model=self.model) else: self.maybe_activation_offload_context = contextlib.nullcontext() # Add tags for models that have been loaded with the correct transformers version if hasattr(self.model, "add_model_tags"): self.model.add_model_tags(self._tag_names) self.aux_loss_enabled = getattr(model.config, "output_router_logits", False) self.aux_loss_coef = getattr(model.config, "router_aux_loss_coef", 0.0) if self.aux_loss_enabled and self.aux_loss_coef == 0.0: logger.warning( "You set `output_router_logits` to `True` in the model config, but `router_aux_loss_coef` is set to " "`0.0`, meaning the auxiliary loss will not be used. Either set `router_aux_loss_coef` to a value " "greater than `0.0`, or set `output_router_logits` to `False` if you don't want to use the auxiliary " "loss.", ) def _prepare_dataset( self, dataset: Union[Dataset, IterableDataset], processing_class: Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin], args: SFTConfig, packing: bool, formatting_func: Optional[Callable[[dict], str]], dataset_name: str, ) -> Union[Dataset, IterableDataset]: # Tabular backends like Arrow/Parquet insert `None` for mismatched keys in nested structures. Clean them from # sampled data. if isinstance(dataset, Dataset): # IterableDataset does not support `with_transform` dataset = dataset.with_transform(remove_none_values) # If the dataset is already preprocessed (tokenized), skip the processing steps. column_names = list(next(iter(dataset)).keys()) is_processed = "input_ids" in column_names # Build the kwargs for the `map` function map_kwargs = {} if isinstance(dataset, Dataset): # IterableDataset does not support num_proc map_kwargs["num_proc"] = args.dataset_num_proc with PartialState().main_process_first(): # Apply the formatting function if any if formatting_func is not None and is_processed: logger.warning( "You passed a dataset that is already processed (contains an `input_ids` field) together with a " "formatting function. Therefore `formatting_func` will be ignored. Either remove the " "`formatting_func` or pass a dataset that is not already processed.", ) if formatting_func is not None and not is_processed: if isinstance(dataset, Dataset): # `IterableDataset.map` does not support `desc` map_kwargs["desc"] = f"Applying formatting function to {dataset_name} dataset" def _func(example): return {"text": formatting_func(example)} dataset = dataset.map(_func, batched=False, **map_kwargs) if not is_processed: # Convert the dataset to ChatML if needed first_example = next(iter(dataset)) if is_conversational_from_value(first_example): if isinstance(dataset, Dataset): # `IterableDataset.map` does not support `desc` map_kwargs["desc"] = f"Converting {dataset_name} dataset to ChatML" column_names = next(iter(dataset)).keys() dataset = dataset.map( maybe_convert_to_chatml, remove_columns="conversations" if "conversations" in column_names else None, **map_kwargs, ) # Apply the chat template if needed first_example = next(iter(dataset)) if not is_conversational(first_example): if isinstance(dataset, Dataset): # `IterableDataset.map` does not support `desc` map_kwargs["desc"] = f"Adding EOS to {dataset_name} dataset" def add_eos(example, eos_token): if "text" in example and not example["text"].endswith(eos_token): # language modeling case example["text"] = example["text"] + eos_token elif "completion" in example and not example["completion"].endswith(eos_token): example["completion"] = example["completion"] + eos_token return example dataset = dataset.map( add_eos, fn_kwargs={"eos_token": processing_class.eos_token}, remove_columns="messages" if "messages" in column_names else None, # renamed to "text" **map_kwargs, ) # Tokenize the dataset if isinstance(dataset, Dataset): # `IterableDataset.map` does not support `desc` map_kwargs["desc"] = f"Tokenizing {dataset_name} dataset" def tokenize(example, processing_class, dataset_text_field, assistant_only_loss): if "prompt" in example: # prompt-completion case output = {} if is_conversational(example): prompt_ids = processing_class.apply_chat_template( example["prompt"], tools=example.get("tools"), **example.get("chat_template_kwargs", {}), ) prompt_completion_processed = processing_class.apply_chat_template( example["prompt"] + example["completion"], return_dict=True, return_assistant_tokens_mask=assistant_only_loss, tools=example.get("tools"), **example.get("chat_template_kwargs", {}), ) prompt_completion_ids = prompt_completion_processed["input_ids"] if "assistant_masks" in prompt_completion_processed: output["assistant_masks"] = prompt_completion_processed["assistant_masks"] else: prompt_ids = processing_class(text=example["prompt"])["input_ids"] prompt_completion_ids = processing_class(text=example["prompt"] + example["completion"])[ "input_ids" ] # Check if the tokenized prompt starts with the tokenized prompt+completion if not prompt_completion_ids[: len(prompt_ids)] == prompt_ids: logger.warning( "Mismatch between tokenized prompt and the start of tokenized prompt+completion. " "This may be due to unexpected tokenizer behavior, whitespace issues, or special " "token handling. Verify that the tokenizer is processing text consistently." ) # Create a completion mask completion_mask = [0] * len(prompt_ids) + [1] * (len(prompt_completion_ids) - len(prompt_ids)) output["input_ids"] = prompt_completion_ids output["completion_mask"] = completion_mask else: # language modeling case if is_conversational(example): processed = processing_class.apply_chat_template( example["messages"], return_dict=True, return_assistant_tokens_mask=assistant_only_loss, tools=example.get("tools"), **example.get("chat_template_kwargs", {}), ) if "assistant_masks" in processed and 1 not in processed["assistant_masks"]: raise RuntimeError( "You're using `assistant_only_loss=True`, but at least one example has no " "assistant tokens. This usually means the tokenizer's chat template doesn't " "generate assistant masks — it may be missing the `{% generation %}` keyword. Please " "check the template and ensure it's correctly configured to support assistant " "masking." ) output = {k: processed[k] for k in ("input_ids", "assistant_masks") if k in processed} else: output = {"input_ids": processing_class(text=example[dataset_text_field])["input_ids"]} return output dataset = dataset.map( tokenize, fn_kwargs={ "processing_class": processing_class, "dataset_text_field": args.dataset_text_field, "assistant_only_loss": args.assistant_only_loss, }, **map_kwargs, ) # Pack or truncate if packing: if args.max_length is None: raise ValueError("When packing is enabled, `max_length` can't be `None`.") if isinstance(dataset, Dataset): # `IterableDataset.map` does not support `desc` map_kwargs["desc"] = f"Packing {dataset_name} dataset" columns = ["input_ids"] if "completion_mask" in dataset.column_names: columns.append("completion_mask") if "assistant_masks" in dataset.column_names: columns.append("assistant_masks") dataset = dataset.select_columns(columns) # Packing adds new column "seq_lengths" needed for document aware FlashAttention dataset = pack_dataset(dataset, args.max_length, args.packing_strategy, map_kwargs) elif args.max_length is not None: if isinstance(dataset, Dataset): # `IterableDataset.map` does not support `desc` map_kwargs["desc"] = f"Truncating {dataset_name} dataset" dataset = truncate_dataset(dataset, args.max_length, map_kwargs) # For Liger kernel, ensure only the essential columns if args.use_liger_kernel: collator_expected_keys = {"input_ids", "seq_lengths", "completion_mask", "assistant_masks"} dataset = dataset.select_columns(collator_expected_keys.intersection(dataset.column_names)) return dataset def _set_signature_columns_if_needed(self): # If `self.args.remove_unused_columns` is True, non-signature columns are removed. # By default, this method sets `self._signature_columns` to the model's expected inputs (usually, "input_ids" # and "attention_mask"). When using `train_on_completion_only` we add a "completion_mask" column to the # dataset. So we need to override the default signature columns to include "completion_mask" as well. if self._signature_columns is None: if self._is_vlm: self._signature_columns = ["messages", "prompt", "completion", "images"] else: self._signature_columns = ["input_ids", "labels", "seq_lengths", "completion_mask", "assistant_masks"] def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None): """ Compute training loss and additionally compute token accuracies """ mode = "train" if self.model.training else "eval" # Set aside labels as it will be dropped by super().compute_loss() if a custom `compute_loss_func` is used. # This can be removed when this issue is fixed. labels = inputs["labels"] # If not set, defaults from model config and may warn since cache isn't compatible with gradient checkpointing inputs["use_cache"] = False (loss, outputs) = super().compute_loss( model, inputs, return_outputs=True, num_items_in_batch=num_items_in_batch ) # Add auxiliary loss if available if self.aux_loss_enabled and self.aux_loss_coef: aux_loss = self.aux_loss_coef * outputs.aux_loss loss += aux_loss # Compute entropy if not self.args.use_liger_kernel: # liger doesn't return logits with torch.no_grad(): per_token_entropy = entropy_from_logits(outputs.logits) if "attention_mask" in inputs: attention_mask = inputs["attention_mask"] # When using Prompt Tuning, we need to add attention for the virtual tokens (all set to 1). virtual_attention_mask = torch.ones( attention_mask.size(0), self.num_virtual_tokens, device=attention_mask.device ) attention_mask = torch.cat((virtual_attention_mask, attention_mask), dim=1) entropy = torch.sum(per_token_entropy * attention_mask) / attention_mask.sum() elif "position_ids" in inputs: entropy = torch.mean(per_token_entropy) else: raise ValueError("Expected 'attention_mask' or 'position_ids' in inputs.") entropy = self.accelerator.gather_for_metrics(entropy).mean().item() self._metrics[mode]["entropy"].append(entropy) if mode == "train": # When using padding-free, the attention_mask is not present in the inputs, instead we have cu_seq_lens_q, # cu_seq_lens_k, and max_length_k, max_length_q and position_ids. if "attention_mask" in inputs: num_tokens_in_batch = self.accelerator.gather_for_metrics(inputs["attention_mask"].sum()).sum().item() elif "position_ids" in inputs: local_num_tokens = torch.tensor(inputs["position_ids"].size(1), device=inputs["position_ids"].device) num_tokens_in_batch = self.accelerator.gather_for_metrics(local_num_tokens).sum().item() else: raise ValueError("Expected 'attention_mask' or 'position_ids' in inputs.") self._total_train_tokens += num_tokens_in_batch self._metrics[mode]["num_tokens"] = [self._total_train_tokens] # Compute token accuracy if we have labels and if the model is not using Liger (no logits) if not self.args.use_liger_kernel: with torch.no_grad(): if "shift_labels" in inputs: # When using CP, labels are pre-shifted. We must use these (and cannot manually shift) because: # - The first discarded token from inputs["labels"] actually belongs to process n-1 # - The last logits require the label from process n+1 shift_logits = outputs.logits.contiguous() shift_labels = inputs["shift_labels"] else: shift_logits = outputs.logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # When using Prompt Tuning, skip the virtual tokens in logits before accuracy computation, since they do # not correspond to actual input labels. shift_logits = shift_logits[:, self.num_virtual_tokens :, :] # Get predictions predictions = shift_logits.argmax(dim=-1) # Create mask for non-padding tokens (assuming ignore_index is -100) mask = shift_labels != -100 # Calculate accuracy only on non-padding tokens correct_predictions = (predictions == shift_labels) & mask total_tokens = mask.sum() correct_tokens = correct_predictions.sum() # Gather the correct_tokens and total_tokens across all processes correct_tokens = self.accelerator.gather_for_metrics(correct_tokens) total_tokens = self.accelerator.gather_for_metrics(total_tokens) # Compute the mean token accuracy and log it total_sum = total_tokens.sum() accuracy = (correct_tokens.sum() / total_sum).item() if total_sum > 0 else 0.0 self._metrics[mode]["mean_token_accuracy"].append(accuracy) if self.aux_loss_enabled: aux_loss = self.accelerator.gather_for_metrics(aux_loss).mean().item() self._metrics[mode]["aux_loss"].append(aux_loss) return (loss, outputs) if return_outputs else loss # Override training step to add activation offloading context. def training_step(self, *args, **kwargs): with self.maybe_activation_offload_context: return super().training_step(*args, **kwargs) def log(self, logs: dict[str, float], start_time: Optional[float] = None) -> None: mode = "train" if self.model.training else "eval" metrics = {key: sum(val) / len(val) for key, val in self._metrics[mode].items()} # average the metrics # This method can be called both in training and evaluation. When called in evaluation, the keys in `logs` # start with "eval_". We need to add the prefix "eval_" to the keys in `metrics` to match the format. if mode == "eval": metrics = {f"eval_{key}": val for key, val in metrics.items()} logs.update(metrics) super().log(logs, start_time) self._metrics[mode].clear() # Ensure the model card is saved along with the checkpoint def _save_checkpoint(self, model, trial): if self.args.hub_model_id is None: model_name = Path(self.args.output_dir).name else: model_name = self.args.hub_model_id.split("/")[-1] self.create_model_card(model_name=model_name) super()._save_checkpoint(model, trial) def create_model_card( self, model_name: Optional[str] = None, dataset_name: Optional[str] = None, tags: Union[str, list[str], None] = None, ): """ Creates a draft of a model card using the information available to the `Trainer`. Args: model_name (`str` or `None`, *optional*, defaults to `None`): Name of the model. dataset_name (`str` or `None`, *optional*, defaults to `None`): Name of the dataset used for training. tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`): Tags to be associated with the model card. """ if not self.is_world_process_zero(): return if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path): base_model = self.model.config._name_or_path else: base_model = None # normalize `tags` to a mutable set if tags is None: tags = set() elif isinstance(tags, str): tags = {tags} else: tags = set(tags) if hasattr(self.model.config, "unsloth_version"): tags.add("unsloth") if "JOB_ID" in os.environ: tags.add("hf_jobs") tags.update(self._tag_names) model_card = generate_model_card( base_model=base_model, model_name=model_name, hub_model_id=self.hub_model_id, dataset_name=dataset_name, tags=list(tags), wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None, comet_url=get_comet_experiment_url(), trainer_name="SFT", ) model_card.save(os.path.join(self.args.output_dir, "README.md"))