unsloth_compiled_cache/UnslothSFTTrainer.py

"""
2026.6.7
2026.6.9
5.5.0
1.7.0
__UNSLOTH_VERSIONING__
"""

# Unsloth auto generated code
# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program.  If not, see <https://www.gnu.org/licenses/>.

from torch import Tensor
import torch
import torch.nn as nn
from torch.nn import functional as F
from unsloth_zoo.temporary_patches.common import torch_compile
from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable
from trl.trainer.sft_trainer import (Any, AutoProcessor, Callable, DataCollator, DataCollatorForLanguageModeling, DataCollatorForVisionLanguageModeling, Dataset, EvalPrediction, F, FLASH_ATTENTION_VARIANTS, IterableDataset, Path, PeftConfig, PeftModel, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, SFTConfig, SFTTrainer, TrainerCallback, TrainingArguments, Version, _BaseTrainer, _CHUNKED_LM_HEAD_CHUNK_SIZE, _chunk, _patch_chunked_ce_lm_head, apply_chat_template, clone_chat_template, contextlib, create_model_from_path, dataclass, defaultdict, dft_loss, get_act_offloading_ctx_manager, get_config_model_id, get_dataset_column_names, get_peft_model, get_training_chat_template, has_generation_markers, is_chat_template_stop_token_trained, is_conversational, is_peft_available, is_peft_model, logger, nn, os, pack_dataset, pad, peft, prepare_multimodal_messages, selective_log_softmax, torch, transformers, types, warnings, Any, AutoProcessor, Callable, DataCollator, DataCollatorForLanguageModeling, DataCollatorForVisionLanguageModeling, Dataset, EvalPrediction, F, FLASH_ATTENTION_VARIANTS, IterableDataset, PeftConfig, PeftModel, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, SFTConfig, SFTTrainer, TrainerCallback, TrainingArguments, Version, clone_chat_template, contextlib, create_model_from_path, defaultdict, dft_loss, get_act_offloading_ctx_manager, get_config_model_id, get_peft_model, get_training_chat_template, has_generation_markers, is_chat_template_stop_token_trained, is_conversational, is_peft_available, is_peft_model, logger, nn, os, pad, peft, torch, transformers, Callable, DataCollator, DataCollatorForLanguageModeling, Dataset, F, IterableDataset, apply_chat_template, is_conversational, os, pack_dataset, pad, transformers, warnings, F, PeftModel, PreTrainedModel, is_peft_available, logger, os, peft, torch, F, os)


import os
import math
import logging
from typing import *
from dataclasses import dataclass, field
from packaging.version import Version
import torch
import numpy as np
from contextlib import nullcontext
from torch.nn import functional as F
import inspect
from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling
from transformers.training_args import ParallelMode
from unsloth_zoo.device_type import DEVICE_TYPE, device_synchronize

# Wrap trainer with padding to right and enable training mode
import functools
from types import MethodType
try:
    from unsloth_zoo.gradient_checkpointing import reset_unsloth_gradient_checkpointing_buffers
except:
    def reset_unsloth_gradient_checkpointing_buffers(): pass
# Canonical reset lives in unsloth.models._utils so the SFT auto-packing wrapper and the plain
# Trainer loop can import the same helper; fall back to a no-op only if it can't be imported.
try:
    from unsloth.models._utils import _unsloth_reset_stray_compile_cache
except Exception:
    def _unsloth_reset_stray_compile_cache(self): pass
def prepare_for_training_mode(f):
    @functools.wraps(f)
    def wrapper(self, *args, **kwargs):
        # Drop any torch.compile graph cache poisoned by a stray pre-train forward.
        try:
            _unsloth_reset_stray_compile_cache(self)
        except Exception:
            pass
        # Finish the previous W&B run if this is a subsequent train() call.
        # We do this at the START of train() (not the end) so that
        # evaluate() / log() still work after train() completes.
        # HF's WandbCallback.setup() will call wandb.init() for the new run.
        # See: https://github.com/unslothai/unsloth/issues/3954
        if getattr(self, '_unsloth_training_completed', False):
            try:
                import wandb
                if wandb.run is not None:
                    wandb.finish()
                    # Reset HF's WandbCallback so it calls wandb.init() for the new run
                    for cb in self.callback_handler.callbacks:
                        if type(cb).__name__ == 'WandbCallback':
                            cb._initialized = False
                            break
            except:
                pass
        # Enable training mode
        _was_training = None
        # Get gradient checkpointing setting from training arguments
        use_gc = getattr(self.args, 'gradient_checkpointing', True)
        if hasattr(self, 'model') and hasattr(self.model, "training"):
            _was_training = self.model.training
        if hasattr(self, 'model') and hasattr(self.model, "for_training"):
            self.model.for_training(use_gradient_checkpointing=use_gc)
        output = f(self, *args, **kwargs)
        # Restore previous mode when possible
        if hasattr(self, 'model') and hasattr(self.model, "for_inference"):
            if _was_training is False:
                self.model.for_inference()
            elif _was_training is True and hasattr(self.model, "for_training"):
                self.model.for_training(use_gradient_checkpointing=use_gc)
        # Reset gradient checkpointing buffers to free memory while staying ready for next run
        try:
            reset_unsloth_gradient_checkpointing_buffers()
        except:
            pass
        # Mark that training completed so the next train() call can
        # finish this W&B run before starting a new one
        self._unsloth_training_completed = True
        return output
    return wrapper
pass

torch_compile_options = {
    "epilogue_fusion"   : True,
    "max_autotune"      : False,
    "shape_padding"     : True,
    "trace.enabled"     : False,
    "triton.cudagraphs" : False,
}

@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,)
def chunked_hidden_states_selective_log_softmax(
    hidden_states: torch.Tensor,
    lm_head: torch.Tensor,
    index: torch.Tensor,
    chunks: int = 4,
    logit_scale_multiply: float = 0.0,
    logit_scale_divide: float = 0.0,
    logit_softcapping: float = 0.0,
    temperature: float = 1.0,
) -> torch.Tensor:
    # All Unsloth Zoo code licensed under AGPL3
    flat_hidden_states = hidden_states.reshape(-1, hidden_states.shape[-1])
    flat_index = index.reshape(-1)

    chunked_hidden_states = torch.chunk(flat_hidden_states, chunks=chunks, dim=0)
    chunked_index = torch.chunk(flat_index, chunks=chunks, dim=0)

    all_per_token_logps = []

    for chunk_hidden_states, chunk_index in zip(chunked_hidden_states, chunked_index):
        chunk_logits = chunk_hidden_states.to(lm_head.dtype) @ lm_head.t()

        if logit_scale_multiply != 0.0:
            chunk_logits = chunk_logits * logit_scale_multiply
        if logit_scale_divide != 0.0:
            chunk_logits = chunk_logits / logit_scale_divide
        if logit_softcapping != 0.0:
            chunk_logits = logit_softcapping * torch.tanh(chunk_logits / logit_softcapping)

        chunk_logits = chunk_logits.to(torch.float32)

        if temperature != 1.0:
            chunk_logits = chunk_logits / temperature

        selected_logits = torch.gather(chunk_logits, dim=-1, index=chunk_index.unsqueeze(-1)).squeeze(-1)
        logsumexp_values = torch.logsumexp(chunk_logits, dim=-1)
        per_token_logps = selected_logits - logsumexp_values
        all_per_token_logps.append(per_token_logps)

    all_per_token_logps = torch.concat(all_per_token_logps)

    all_per_token_logps = all_per_token_logps.reshape((hidden_states.shape[0], hidden_states.shape[1]))
    return all_per_token_logps

@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,)
def chunked_selective_log_softmax(
    logits,
    index,
    temperature: float = 1.0,
    chunks: int = 4,
):
    chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = chunks, dim = 0)
    chunked_index  = torch.chunk(index.reshape(-1), chunks = chunks, dim = 0)
    all_per_token_logps = []
    # Per-chunk selective_log_softmax.
    for chunk_logits, chunk_index in zip(chunked_logits, chunked_index):
        chunk_logits = chunk_logits.to(torch.float32)
        if temperature != 1.0:
            chunk_logits = chunk_logits / temperature
        selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1)
        logsumexp_values = torch.logsumexp(chunk_logits, dim = -1)
        per_token_logps = selected_logits - logsumexp_values
        all_per_token_logps.append(per_token_logps)
    pass
    all_per_token_logps = torch.concat(all_per_token_logps)
    all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1]))
    return all_per_token_logps

def calculate_pad_tokens_in_prompt(
    input_ids: torch.Tensor,
    logits_to_keep: int,
    pad_token_id: int
) -> torch.Tensor:
    """Count left-padded tokens per sequence, e.g. [pad, pad, pad, cat] -> 3."""
    if logits_to_keep >= input_ids.shape[1]:
        raise ValueError("logits_to_keep must be smaller than the sequence length.")

    prompt_section = input_ids[:, :-logits_to_keep]

    padding_mask = (prompt_section == pad_token_id)

    pad_token_counts = padding_mask.sum(dim=1)

    return pad_token_counts

def create_completion_attention_mask(
    completion_input_ids: torch.Tensor,
    left_pad_tokens_per_prompt: torch.Tensor,
    max_left_pad: int,
    pad_token_id: int
) -> torch.Tensor:
    """Build a completion mask that zeros leading prompt and trailing pad tokens.

    For [p,p,p,c,c,c,pad,pad,pad] (p=sliced prompt, c=completion, pad=padding)
    this returns [0,0,0,1,1,1,0,0,0].
    """
    batch_size, completion_len = completion_input_ids.shape
    device = completion_input_ids.device

    num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt

    indices = torch.arange(completion_len, device=device).unsqueeze(0)
    shift_mask = indices >= num_tokens_to_mask.unsqueeze(1)

    non_padding_mask = (completion_input_ids != pad_token_id)

    final_mask = shift_mask & non_padding_mask

    return final_mask

def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor:
    """Move all padding tokens in each sequence to the right."""
    mask = (tensor != pad_id)
    # stable=True since the binary mask is unordered.
    sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True)
    packed_tensor = torch.gather(tensor, 1, sorted_indices)
    return packed_tensor

def align_logprobs_with_mask(
    logprob_tensor: torch.Tensor,
    attention_mask: torch.Tensor,
    pad_value: float = 0.0
) -> torch.Tensor:
    """Align a log probability tensor with a given attention mask."""

    device = logprob_tensor.device
    batch_size, logprob_seq_len = logprob_tensor.shape
    mask_seq_len = attention_mask.shape[1]

    padded_logprobs = torch.full(
        attention_mask.shape,
        fill_value=pad_value,
        dtype=logprob_tensor.dtype,
        device=device
    )

    left_pad_counts = torch.argmax(attention_mask, dim=1)

    cols = torch.arange(logprob_seq_len, device=device)
    dest_indices = left_pad_counts.unsqueeze(1) + cols

    # Destination row indices, shape [batch_size, logprob_seq_len].
    row_indices = torch.arange(batch_size, device=device).unsqueeze(1).expand_as(dest_indices)

    # Keep only in-bounds destinations, then scatter via advanced indexing.
    valid_mask = dest_indices < mask_seq_len
    valid_rows = row_indices[valid_mask]
    valid_cols = dest_indices[valid_mask]
    valid_vals = logprob_tensor[valid_mask]
    padded_logprobs[valid_rows, valid_cols] = valid_vals

    return padded_logprobs

def align_completion_tool_mask(
    tool_mask: torch.Tensor,
    completion_mask: torch.Tensor,
) -> torch.Tensor:
    """Align a raw completion-length tool/env mask with Unsloth's repacked loss mask."""
    if tool_mask is None:
        return completion_mask
    if tool_mask.shape[0] != completion_mask.shape[0]:
        raise ValueError("tool_mask batch size must match completion_mask batch size.")

    tool_mask = tool_mask.to(device=completion_mask.device)
    if tool_mask.shape == completion_mask.shape:
        aligned_tool_mask = tool_mask
    else:
        aligned_tool_mask = align_logprobs_with_mask(
            tool_mask,
            completion_mask,
            pad_value=0,
        )
    return completion_mask * aligned_tool_mask.to(dtype=completion_mask.dtype)

def autotune_batch_and_chunks(
    total_input_rows,
    seq_len,
    hidden_size,
    vocab_size,
    dtype_bytes=16,
    multiplier=None
):
    if multiplier is None:
        final_m = max(4, seq_len // 4096)
    else:
        final_m = multiplier

    if torch.cuda.is_available():
        free_bytes, _ = torch.cuda.mem_get_info()
        limit_gb = (free_bytes / (1024**3))*.80
    elif hasattr(torch, "xpu") and torch.xpu.is_available():
        # XPU: estimate free memory as total - reserved.
        total_mem = torch.xpu.get_device_properties(0).total_memory
        reserved_mem = torch.xpu.memory_reserved()
        free_bytes = total_mem - reserved_mem
        limit_gb = (free_bytes / (1024**3)) * 0.80
    else:
        # Fallback: assume 8GB available.
        limit_gb = 8.0

    bytes_to_gb = 1024**3

    b_vals = torch.arange(total_input_rows, 0, -1, device='cpu', dtype=torch.float32)

    hidden_gb = (b_vals * seq_len * hidden_size * dtype_bytes) / bytes_to_gb

    base_logits = ((b_vals/total_input_rows) * b_vals * seq_len * vocab_size * dtype_bytes) / bytes_to_gb
    logits_gb = base_logits / final_m

    total_mem_gb = hidden_gb + logits_gb

    valid_mask = total_mem_gb <= limit_gb
    valid_indices = torch.nonzero(valid_mask, as_tuple=False)

    if valid_indices.shape[0] == 0:
        #This means your GPU will OOM
        return 4, final_m

    best_idx = valid_indices[0].item()
    final_b = int(b_vals[best_idx].item())

    return final_b, final_m

def sanitize_logprob(logprob):
    """Local port of trl.scripts.vllm_serve.sanitize_logprob.
    Filters NaN logprobs from vLLM outputs."""
    value = logprob.logprob
    if math.isnan(value):
        logging.getLogger(__name__).warning(
            f"Generated NaN logprob, token logprob '{logprob}' will be ignored"
        )
        return None
    return value
@dataclass
class UnslothSFTConfig(SFTConfig):
    """
    
    Configuration class for the [`SFTTrainer`].

    This class includes only the parameters that are specific to SFT training. For a full list of training arguments,
    please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this class may
    differ from those in [`~transformers.TrainingArguments`].

    Using [`~transformers.HfArgumentParser`] we can turn this class into
    [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
    command line.

    Parameters:
        > Parameters that control the model

        model_init_kwargs (`dict[str, Any]`, *optional*):
            Keyword arguments for [`~transformers.AutoModelForCausalLM.from_pretrained`], used when the `model`
            argument of the [`SFTTrainer`] is provided as a string.
        trust_remote_code (`bool`, *optional*, defaults to `False`):
            Whether to allow loading models and tokenizers that ship custom Python code from the Hub. Forwarded to
            [`~transformers.AutoModelForCausalLM.from_pretrained`] and
            [`~transformers.AutoProcessor.from_pretrained`].
        router_aux_loss_coef (`float`, *optional*, defaults to `0.001`):
            Coefficient of the load-balancing auxiliary loss. Only has an effect when training a Mixture-of-Experts
            (MoE) model; for other models it does nothing. The auxiliary loss is added to the training loss with this
            weight. Set to `0.0` to disable it.
        chat_template_path (`str`, *optional*):
            If specified, sets the model's chat template. This can either be the path to a tokenizer (local directory
            or Hugging Face Hub model) or a direct path to a Jinja template file. When using a Jinja file, you must
            ensure that any special tokens referenced in the template are added to the tokenizer and that the model's
            embedding layer is resized accordingly.

        > Parameters that control the data preprocessing

        dataset_text_field (`str`, *optional*, defaults to `"text"`):
            Name of the column that contains text data in the dataset.
        dataset_kwargs (`dict[str, Any]`, *optional*):
            Dictionary of optional keyword arguments for the dataset preparation. The only supported key is
            `skip_prepare_dataset`. When the model is a VLM, `skip_prepare_dataset` is automatically treated as `True`
            regardless of the provided value, since preprocessing is done on the fly.
        dataset_num_proc (`int`, *optional*):
            Number of processes to use for processing the dataset.
        eos_token (`str`, *optional*):
            Token used to indicate the end of a turn or sequence. If `None`, it defaults to
            `processing_class.eos_token`.
        max_length (`int` or `None`, *optional*, defaults to `1024`):
            Maximum length of the tokenized sequence. Sequences longer than `max_length` are truncated from the left
            or right depending on `truncation_mode`. If `None`, no truncation is applied. When packing is enabled,
            this value sets the sequence length.
        truncation_mode (`str`, *optional*, defaults to `"keep_start"`):
            Truncation mode to use when the sequence exceeds `max_length`. The only supported value is
            `"keep_start"`. The `"keep_end"` value is deprecated and will be removed in v2.0.0.
        shuffle_dataset (`bool`, *optional*, defaults to `False`):
            Whether to shuffle the dataset.
        packing (`bool`, *optional*, defaults to `False`):
            Whether to group multiple sequences into fixed-length blocks to improve computational efficiency and reduce
            padding. Uses `max_length` to define sequence length.
        packing_strategy (`str`, *optional*, defaults to `"bfd"`):
            Strategy for packing sequences. Can be `"bfd"` (best-fit decreasing, truncates overflow), `"bfd_split"`
            (best-fit decreasing, splits overflow sequences), or `"wrapped"` (aggressive, cuts mid-sequence).
        padding_free (`bool`, *optional*, defaults to `False`):
            Whether to perform forward passes without padding by flattening all sequences in the batch into a single
            continuous sequence. This reduces memory usage by eliminating padding overhead. Currently, this is only
            supported with the FlashAttention 2 or 3, which can efficiently handle the flattened batch structure. When
            packing is enabled with strategy `"bfd"`, padding-free is enabled, regardless of the value of this
            parameter.
        pad_to_multiple_of (`int`, *optional*):
            If set, the sequences will be padded to a multiple of this value.
        eval_packing (`bool`, *optional*):
            Whether to pack the eval dataset. If `None`, uses the same value as `packing`.

        > Parameters that control the training

        completion_only_loss (`bool`, *optional*):
            Whether to compute loss only on the completion part of the sequence. If set to `True`, loss is computed
            only on the completion, which is supported only for [prompt-completion](#prompt-completion) datasets. If
            `False`, loss is computed on the entire sequence. If `None` (default), the behavior depends on the dataset:
            loss is computed on the completion for [prompt-completion](#prompt-completion) datasets, and on the full
            sequence for [language modeling](#language-modeling) datasets.
        assistant_only_loss (`bool`, *optional*, defaults to `False`):
            Whether to compute loss only on the assistant part of the sequence. If set to `True`, loss is computed only
            on the assistant responses, which is supported only for [conversational](#conversational) datasets. If
            `False`, loss is computed on the entire sequence.
        loss_type (`str`, *optional*, defaults to `"chunked_nll"`):
            Type of loss to use. When left unset, it defaults to `"chunked_nll"`, except when `use_liger_kernel=True`,
            in which case it defaults to `"nll"`. Possible values are:

            - `"nll"`: standard negative log-likelihood.
            - `"dft"`: Dynamic Fine-Tuning, as described in
              [this paper](https://huggingface.co/papers/2508.05629).
            - `"chunked_nll"`: same math as `"nll"`, but the `lm_head` projection is computed on non-ignored tokens
              only (positions with `labels == -100` are dropped before the matmul) and the cross-entropy is processed
              in chunks of tokens to reduce peak activation memory. Not compatible with `use_liger_kernel`.

        activation_offloading (`bool`, *optional*, defaults to `False`):
            Whether to offload the activations to the CPU.

        > Deprecated parameters

        pad_token:

            <Deprecated version="1.1.0">

            Parameter `pad_token` is deprecated and will be removed in version v2.0.0. Set `tokenizer.pad_token`
            directly and pass it as `processing_class` to the trainer instead.

            </Deprecated>

    > [!NOTE]
    > These parameters have default values different from [`~transformers.TrainingArguments`]:
    > - `logging_steps`: Defaults to `10` instead of `500`.
    > - `gradient_checkpointing`: Defaults to `True` instead of `False`.
    > - `bf16`: Defaults to `True` if `fp16` is not set, instead of `False`.
    > - `learning_rate`: Defaults to `2e-5` instead of `5e-5`.
    
    """
    vllm_sampling_params: Optional[Any] = field(
        default = None,
        metadata = {'help': 'vLLM SamplingParams'},
    )
    unsloth_num_chunks : Optional[int] = field(
        default = -1,
        metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'},
    )
    unsloth_logit_chunk_multiplier : Optional[int] = field(
            default = None,
            metadata = {'help': 'Multiplier for chunked logit computations.'},
        )
    unsloth_grpo_mini_batch : Optional[int] = field(
        default = None,
        metadata = {'help': 'Mini batch size for GRPO hidden state accumulation. Default is None unless user defines it.'},
    )
    max_seq_length : Optional[int] = field(
        default = None,
        metadata = {'help': 'Maximum sequence length to truncate to.'},
    )
    def __init__(
        self,
        output_dir = None,
        per_device_train_batch_size = 4,
        num_train_epochs = 3.0,
        max_steps = -1,
        learning_rate = 5e-05,
        lr_scheduler_type = 'linear',
        lr_scheduler_kwargs = None,
        warmup_steps = 0.1,
        optim = 'adamw_8bit',
        optim_args = None,
        weight_decay = 0.001,
        adam_beta1 = 0.9,
        adam_beta2 = 0.999,
        adam_epsilon = 1e-08,
        optim_target_modules = None,
        gradient_accumulation_steps = 2,
        average_tokens_across_devices = True,
        max_grad_norm = 1.0,
        label_smoothing_factor = 0.0,
        bf16 = False,
        fp16 = False,
        bf16_full_eval = False,
        fp16_full_eval = False,
        tf32 = None,
        gradient_checkpointing = True,
        gradient_checkpointing_kwargs = None,
        torch_compile = False,
        torch_compile_backend = None,
        torch_compile_mode = None,
        use_liger_kernel = False,
        liger_kernel_config = None,
        use_cache = False,
        neftune_noise_alpha = None,
        torch_empty_cache_steps = 250,
        auto_find_batch_size = False,
        logging_strategy = 'steps',
        logging_steps = 1,
        logging_first_step = False,
        log_on_each_node = True,
        logging_nan_inf_filter = False,
        include_num_input_tokens_seen = False,
        log_level = 'passive',
        log_level_replica = 'warning',
        disable_tqdm = None,
        report_to = 'none',
        run_name = None,
        project = 'huggingface',
        trackio_space_id = 'trackio',
        eval_strategy = 'no',
        eval_steps = None,
        eval_delay = 0,
        per_device_eval_batch_size = 4,
        prediction_loss_only = False,
        eval_on_start = False,
        eval_do_concat_batches = True,
        eval_use_gather_object = False,
        eval_accumulation_steps = 2,
        batch_eval_metrics = False,
        save_only_model = False,
        save_strategy = 'steps',
        save_steps = 500,
        save_on_each_node = False,
        save_total_limit = None,
        enable_jit_checkpoint = False,
        push_to_hub = False,
        hub_token = None,
        hub_private_repo = None,
        hub_model_id = None,
        hub_strategy = 'every_save',
        hub_always_push = False,
        hub_revision = None,
        load_best_model_at_end = False,
        metric_for_best_model = None,
        greater_is_better = None,
        ignore_data_skip = False,
        restore_callback_states_from_checkpoint = False,
        full_determinism = False,
        seed = 3407,
        data_seed = 3407,
        use_cpu = False,
        accelerator_config = None,
        parallelism_config = None,
        dataloader_drop_last = False,
        dataloader_num_workers = 0,
        dataloader_pin_memory = True,
        dataloader_persistent_workers = False,
        dataloader_prefetch_factor = None,
        remove_unused_columns = True,
        label_names = None,
        train_sampling_strategy = 'random',
        length_column_name = 'length',
        ddp_find_unused_parameters = None,
        ddp_bucket_cap_mb = None,
        ddp_broadcast_buffers = None,
        ddp_backend = None,
        ddp_timeout = 1800,
        fsdp = None,
        fsdp_config = None,
        deepspeed = None,
        debug = '',
        skip_memory_metrics = True,
        do_train = False,
        do_eval = False,
        do_predict = False,
        resume_from_checkpoint = None,
        warmup_ratio = None,
        logging_dir = None,
        local_rank = -1,
        model_init_kwargs = None,
        router_aux_loss_coef = 0.001,
        trust_remote_code = False,
        chat_template_path = None,
        dataset_text_field = 'text',
        dataset_kwargs = None,
        dataset_num_proc = None,
        eos_token = None,
        max_length = 1024,
        truncation_mode = 'keep_start',
        shuffle_dataset = False,
        packing = False,
        packing_strategy = 'bfd',
        padding_free = None,
        pad_to_multiple_of = None,
        eval_packing = None,
        completion_only_loss = None,
        assistant_only_loss = False,
        loss_type = None,
        activation_offloading = False,
        pad_token = None,
        vllm_sampling_params = None,
        unsloth_num_chunks = -1,
        unsloth_logit_chunk_multiplier = None,
        unsloth_grpo_mini_batch = None,
        max_seq_length = None,
        **kwargs,
    ):
        if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!')
        if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!')
        if num_train_epochs is None:
            num_train_epochs = 3.0  # Default to 3 epochs if None, max_steps will override
        if output_dir is None and save_strategy == 'steps' and save_steps == 500:
            output_dir = 'unsloth_training_checkpoints'
            save_strategy = 'no'
        import multiprocessing as _mp
        if dataset_num_proc is None:
            if _mp.get_start_method() != 'fork':
                dataset_num_proc = None
            else:
                import psutil
                dataset_num_proc = min(max((psutil.cpu_count() or 1)+4, 2), 64)
                memory_gb_left = psutil.virtual_memory().available / (1024**3)
                if memory_gb_left <= 2: dataset_num_proc = 1
                else: dataset_num_proc = min(dataset_num_proc, int(memory_gb_left))
        if os.environ.get('UNSLOTH_ENABLE_FLEX_ATTENTION', '0') == '1':
            from unsloth_zoo.flex_attention import HAS_FLEX_ATTENTION
            if HAS_FLEX_ATTENTION and pad_to_multiple_of is None:
                from unsloth_zoo.flex_attention import FLEX_ATTENTION_BLOCK_SIZE
                pad_to_multiple_of = FLEX_ATTENTION_BLOCK_SIZE
        
        
        super().__init__(
            output_dir = output_dir,
            per_device_train_batch_size = per_device_train_batch_size,
            num_train_epochs = num_train_epochs,
            max_steps = max_steps,
            learning_rate = learning_rate,
            lr_scheduler_type = lr_scheduler_type,
            lr_scheduler_kwargs = lr_scheduler_kwargs,
            warmup_steps = warmup_steps,
            optim = optim,
            optim_args = optim_args,
            weight_decay = weight_decay,
            adam_beta1 = adam_beta1,
            adam_beta2 = adam_beta2,
            adam_epsilon = adam_epsilon,
            optim_target_modules = optim_target_modules,
            gradient_accumulation_steps = gradient_accumulation_steps,
            average_tokens_across_devices = average_tokens_across_devices,
            max_grad_norm = max_grad_norm,
            label_smoothing_factor = label_smoothing_factor,
            bf16 = bf16,
            fp16 = fp16,
            bf16_full_eval = bf16_full_eval,
            fp16_full_eval = fp16_full_eval,
            tf32 = tf32,
            gradient_checkpointing = gradient_checkpointing,
            gradient_checkpointing_kwargs = gradient_checkpointing_kwargs,
            torch_compile = torch_compile,
            torch_compile_backend = torch_compile_backend,
            torch_compile_mode = torch_compile_mode,
            use_liger_kernel = use_liger_kernel,
            liger_kernel_config = liger_kernel_config,
            use_cache = use_cache,
            neftune_noise_alpha = neftune_noise_alpha,
            torch_empty_cache_steps = torch_empty_cache_steps,
            auto_find_batch_size = auto_find_batch_size,
            logging_strategy = logging_strategy,
            logging_steps = logging_steps,
            logging_first_step = logging_first_step,
            log_on_each_node = log_on_each_node,
            logging_nan_inf_filter = logging_nan_inf_filter,
            include_num_input_tokens_seen = include_num_input_tokens_seen,
            log_level = log_level,
            log_level_replica = log_level_replica,
            disable_tqdm = disable_tqdm,
            report_to = report_to,
            run_name = run_name,
            project = project,
            trackio_space_id = trackio_space_id,
            eval_strategy = eval_strategy,
            eval_steps = eval_steps,
            eval_delay = eval_delay,
            per_device_eval_batch_size = per_device_eval_batch_size,
            prediction_loss_only = prediction_loss_only,
            eval_on_start = eval_on_start,
            eval_do_concat_batches = eval_do_concat_batches,
            eval_use_gather_object = eval_use_gather_object,
            eval_accumulation_steps = eval_accumulation_steps,
            batch_eval_metrics = batch_eval_metrics,
            save_only_model = save_only_model,
            save_strategy = save_strategy,
            save_steps = save_steps,
            save_on_each_node = save_on_each_node,
            save_total_limit = save_total_limit,
            enable_jit_checkpoint = enable_jit_checkpoint,
            push_to_hub = push_to_hub,
            hub_token = hub_token,
            hub_private_repo = hub_private_repo,
            hub_model_id = hub_model_id,
            hub_strategy = hub_strategy,
            hub_always_push = hub_always_push,
            hub_revision = hub_revision,
            load_best_model_at_end = load_best_model_at_end,
            metric_for_best_model = metric_for_best_model,
            greater_is_better = greater_is_better,
            ignore_data_skip = ignore_data_skip,
            restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint,
            full_determinism = full_determinism,
            seed = seed,
            data_seed = data_seed,
            use_cpu = use_cpu,
            accelerator_config = accelerator_config,
            parallelism_config = parallelism_config,
            dataloader_drop_last = dataloader_drop_last,
            dataloader_num_workers = dataloader_num_workers,
            dataloader_pin_memory = dataloader_pin_memory,
            dataloader_persistent_workers = dataloader_persistent_workers,
            dataloader_prefetch_factor = dataloader_prefetch_factor,
            remove_unused_columns = remove_unused_columns,
            label_names = label_names,
            train_sampling_strategy = train_sampling_strategy,
            length_column_name = length_column_name,
            ddp_find_unused_parameters = ddp_find_unused_parameters,
            ddp_bucket_cap_mb = ddp_bucket_cap_mb,
            ddp_broadcast_buffers = ddp_broadcast_buffers,
            ddp_backend = ddp_backend,
            ddp_timeout = ddp_timeout,
            fsdp = fsdp,
            fsdp_config = fsdp_config,
            deepspeed = deepspeed,
            debug = debug,
            skip_memory_metrics = skip_memory_metrics,
            do_train = do_train,
            do_eval = do_eval,
            do_predict = do_predict,
            resume_from_checkpoint = resume_from_checkpoint,
            warmup_ratio = warmup_ratio,
            logging_dir = logging_dir,
            local_rank = local_rank,
            model_init_kwargs = model_init_kwargs,
            router_aux_loss_coef = router_aux_loss_coef,
            trust_remote_code = trust_remote_code,
            chat_template_path = chat_template_path,
            dataset_text_field = dataset_text_field,
            dataset_kwargs = dataset_kwargs,
            dataset_num_proc = dataset_num_proc,
            eos_token = eos_token,
            max_length = max_length,
            truncation_mode = truncation_mode,
            shuffle_dataset = shuffle_dataset,
            packing = packing,
            packing_strategy = packing_strategy,
            padding_free = padding_free,
            pad_to_multiple_of = pad_to_multiple_of,
            eval_packing = eval_packing,
            completion_only_loss = completion_only_loss,
            assistant_only_loss = assistant_only_loss,
            loss_type = loss_type,
            activation_offloading = activation_offloading,
            pad_token = pad_token,**kwargs)
        self.vllm_sampling_params = vllm_sampling_params
        self.unsloth_num_chunks = unsloth_num_chunks
        if unsloth_grpo_mini_batch is not None:
            if self.generation_batch_size >= unsloth_grpo_mini_batch:
                self.unsloth_grpo_mini_batch = unsloth_grpo_mini_batch
            else:
                raise ValueError(
                    f"Unsloth GRPO mini batch size needs to be less than or equal to the effective generation batch size, "
                    f"which is self.per_device_train_batch_size * gradient_accumulation_steps."
                )
        self.unsloth_logit_chunk_multiplier = unsloth_logit_chunk_multiplier
        self.max_seq_length = max_seq_length
        # Unsloth: Remove use_reentrant=False forced by TRL 0.27.0+
        if getattr(self, 'gradient_checkpointing_kwargs', None) is not None:
            if 'use_reentrant' in self.gradient_checkpointing_kwargs:
                del self.gradient_checkpointing_kwargs['use_reentrant']

pass

class _UnslothSFTTrainer(_BaseTrainer):
    """"""

    _tag_names = ["trl", "sft"]
    _name = "SFT"

    def __init__(
        self,
        model: "str | PreTrainedModel | PeftModel",
        args: SFTConfig | TrainingArguments | None = None,
        data_collator: DataCollator | None = None,
        train_dataset: Dataset | IterableDataset | None = None,
        eval_dataset: Dataset | IterableDataset | dict[str, Dataset | IterableDataset] | None = None,
        processing_class: PreTrainedTokenizerBase | ProcessorMixin | None = None,
        compute_loss_func: Callable | None = None,
        compute_metrics: Callable[[EvalPrediction], dict] | None = None,
        callbacks: list[TrainerCallback] | None = None,
        optimizers: tuple[torch.optim.Optimizer | None, torch.optim.lr_scheduler.LambdaLR | None] = (None, None),
        optimizer_cls_and_kwargs: tuple[type[torch.optim.Optimizer], dict[str, Any]] | None = None,
        preprocess_logits_for_metrics: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] | None = None,
        peft_config: "PeftConfig | None" = None,
        formatting_func: Callable[[dict], str] | None = None,
    ):
        # Args
        if args is None:
            model_name = model if isinstance(model, str) else get_config_model_id(model.config)
            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
            if Version(transformers.__version__) < Version("5.0.0"):
                dict_args.pop("push_to_hub_token", None)
            args = SFTConfig(**dict_args)

        if train_dataset is None:
            raise ValueError("`train_dataset` is required")
        elif isinstance(train_dataset, IterableDataset):
            # IterableDataset requires dispatch_batches=False because Accelerate's dispatch mode may try to concatenate
            # batches from multiple processes, leading to mismatch errors.
            if args.accelerator_config.dispatch_batches is True:
                logger.warning(
                    "You are using an `IterableDataset` for training with `dispatch_batches=True`. `dispatch_batches` "
                    "is forced to `False` when using an `IterableDataset`. To remove this warning, unset "
                    "`dispatch_batches` in `SFTConfig` or set it to `False`."
                )
            args.accelerator_config.dispatch_batches = False

        # Model
        if isinstance(model, str):
            model_init_kwargs = args.model_init_kwargs or {}
            # Distributed training requires device_map=None ["auto" fails]
            if args.distributed_state.distributed_type in ["MULTI_GPU", "DEEPSPEED"]:
                model_init_kwargs["device_map"] = None
            model_init_kwargs.setdefault("trust_remote_code", args.trust_remote_code)
            model = create_model_from_path(model, **model_init_kwargs)
        else:
            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."
                )
        # Non-quantized models do not have the `is_loaded_in_{8,4}bit` attributes, whereas quantized models do
        _is_quantized_model = getattr(model, "is_loaded_in_4bit", False) or getattr(model, "is_loaded_in_8bit", False)

        # Processing class
        if processing_class is None:
            processing_class = AutoProcessor.from_pretrained(
                get_config_model_id(model.config), trust_remote_code=args.trust_remote_code
            )

        # Handle pad token for processors or tokenizers
        if isinstance(processing_class, ProcessorMixin):
            self._tokenizer = processing_class.tokenizer
            self._is_vlm = True
        elif isinstance(processing_class, PreTrainedTokenizerBase):
            self._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:
            if args.eos_token not in self._tokenizer.get_vocab():
                raise ValueError(
                    f"The specified `eos_token` ('{args.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."
                )
            self._tokenizer.eos_token = args.eos_token

        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`."
            )
        if self._is_vlm and args.max_length is not None and args.truncation_mode == "keep_end":
            raise ValueError(
                "truncation_mode='keep_end' is not supported for vision-language models. Image tokens reside "
                "inside the prompt portion of the sequence; depending on the example, keep_end may silently "
                "drop them, causing pixel_values to be forwarded to the model with no corresponding visual "
                "tokens in input_ids. Use truncation_mode='keep_start' (the default) or set max_length=None."
            )

        # PEFT
        if False:
            if not is_peft_available():
                raise ImportError(
                    "You passed `peft_config` but the `peft` library is not installed. "
                    "Install it with `pip install trl[peft]`."
                )
            if not isinstance(peft_config, PeftConfig):
                raise TypeError(
                    f"`peft_config` must be a `peft.PeftConfig` instance (e.g. `peft.LoraConfig`), "
                    f"got {type(peft_config).__name__}."
                )
            if is_peft_model(model):
                raise ValueError(
                    "You passed a `PeftModel` instance together with a `peft_config` to the trainer. Please first merge "
                    "and unload the existing adapter, save the resulting base model, and then pass that base model along "
                    "with the new `peft_config` to the trainer."
                )
            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")
            # Create PEFT model
            # ZeRO-3 + PEFT for non-quantized models:
            # - PEFT's default autocast_adapter_dtype=True upcasts LoRA adapter params to fp32 even when the base model is bf16.
            # - ZeRO-3's _allgather_params_coalesced allocates output buffers using the dtype of the first persistent parameter,
            #   so mixed-dtype persistent_parameters [bf16 base + fp32 LoRA] cause a TypeError on the first optimizer step.
            # - Passing autocast_adapter_dtype=False keeps adapter params in the base model dtype [bf16], fixing the mismatch.
            # - This is safe: the fp32 upcast is a QLoRA-specific concern [low-bit quantized base models], not needed for
            #   non-quantized bf16 training.
            # - See:
            #   - TRL issue: https://github.com/huggingface/trl/issues/6089
            #   - Upstream issue: https://github.com/deepspeedai/DeepSpeed/issues/8072
            # - autocast_adapter_dtype was introduced in PEFT 0.12.0; before, no upcast existed: no need to pass the kwarg
            get_peft_model_kwargs = {}
            if (
                args.deepspeed_plugin is not None
                and args.deepspeed_plugin.zero_stage == 3
                and not _is_quantized_model
                and Version(peft.__version__) >= Version("0.12.0")
            ):
                get_peft_model_kwargs["autocast_adapter_dtype"] = False
            model = get_peft_model(model, peft_config, **get_peft_model_kwargs)

        # PEFT + DeepSpeed ZeRO-3 requires reentrant checkpointing. For more details, see
        # https://github.com/huggingface/trl/issues/2514#issuecomment-2692152703
        if (
            is_peft_model(model)
            and args.deepspeed_plugin is not None
            and args.deepspeed_plugin.zero_stage == 3
            and args.gradient_checkpointing
        ):
            args.gradient_checkpointing_kwargs = args.gradient_checkpointing_kwargs or {}
            use_reentrant = args.gradient_checkpointing_kwargs.get("use_reentrant")
            if use_reentrant is False:
                logger.warning(
                    "You are using PEFT with DeepSpeed ZeRO-3 and gradient checkpointing with `use_reentrant=False`. "
                    "`use_reentrant` is forced to `True` in this configuration to ensure correct training. To remove "
                    "this warning, unset `use_reentrant` in `gradient_checkpointing_kwargs` or set it to `True`."
                )
            args.gradient_checkpointing_kwargs["use_reentrant"] = True

        # When using gradient checkpointing with PEFT, we need to enable input gradients. transformers.Trainer normally
        # handles this, but a bug currently prevents it; see https://github.com/huggingface/transformers/issues/42489
        if is_peft_model(model) and args.gradient_checkpointing:
            model.enable_input_require_grads()

        # When using QLoRA, the PEFT adapter weights are converted to bf16 to follow the recommendations from the
        # original paper [see https://huggingface.co/papers/2305.14314, paragraph 3]. Normally, this can be done by
        # passing `autocast_adapter_dtype=False` to `get_peft_model`, but this option is not yet supported for
        # quantized models. See: https://github.com/huggingface/peft/issues/2889
        if _is_quantized_model:
            for param in model.parameters():
                if param.requires_grad:
                    param.data = param.data.to(torch.bfloat16)

        # 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 is_peft_model(model):
            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 in {"bfd", "bfd_split"})
        use_flash_attention = model.config._attn_implementation in FLASH_ATTENTION_VARIANTS
        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 a supported "
                    "Flash Attention variant. Padding-free training flattens batches into a single sequence, and only "
                    "the following implementations are known to reliably support this: "
                    f"{', '.join(sorted(FLASH_ATTENTION_VARIANTS))}. Using other implementations may lead to "
                    "unexpected behavior. To ensure compatibility, set `attn_implementation` in the model "
                    "configuration to one of these supported options or verify that your attention mechanism can "
                    "handle flattened sequences."
                )
        # 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

        self._is_vision_dataset = "image" in dataset_sample or "images" in dataset_sample
        # Unsloth: override _is_vlm for VLM models that pass a bare tokenizer
        if not self._is_vlm and self._is_vision_dataset:
            _m = model
            if hasattr(_m, "model"): _m = _m.model
            if hasattr(getattr(_m, "config", None), "vision_config") or \
               _m.__class__.__name__.endswith("ForConditionalGeneration"):
                self._is_vlm = True
        if self._is_vision_dataset and not self._is_vlm:
            raise ValueError(
                "The dataset appears to be vision-related (contains 'image' or 'images' keys), but the provided "
                "model does not seem to be a vision-language model. Please check your model and dataset."
            )

        if data_collator is None and not self._is_vision_dataset:
            # 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 self._tokenizer.pad_token or self._tokenizer.eos_token
            if pad_token not in self._tokenizer.get_vocab():
                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."
                )
            self._tokenizer.pad_token = pad_token
            data_collator = DataCollatorForLanguageModeling(
                pad_token_id=self._tokenizer.pad_token_id,
                max_length=None if self.padding_free else args.max_length,
                truncation_mode=args.truncation_mode,
                padding_free=self.padding_free,
                pad_to_multiple_of=args.pad_to_multiple_of,
            )
        elif data_collator is None and self._is_vision_dataset:
            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 in {"bfd", "bfd_split"} and not use_flash_attention:
            logger.warning(
                "You are using packing, but the attention implementation is not set to a supported Flash Attention "
                "variant. Packing gathers multiple samples into a single sequence, and only the following "
                f"implementations are known to reliably support this: {', '.join(sorted(FLASH_ATTENTION_VARIANTS))}. "
                "Using other implementations may lead to cross-contamination between samples. To avoid this, either "
                "disable packing by setting `packing=False`, or set `attn_implementation` in the model configuration "
                "to one of these supported options."
            )
        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."
            )

        # When assistant_only_loss is enabled, swap in a training chat template with {% generation %} markers
        # if the current template doesn't already have them.
        if args.assistant_only_loss and not has_generation_markers(processing_class.chat_template):
            self.chat_template = get_training_chat_template(processing_class)
        else:
            self.chat_template = None

        # A template can define generation markers and still attribute the assistant's end-of-turn token to the next
        # message, leaving it out of the assistant mask so the model is never trained to stop.
        if args.assistant_only_loss and not is_chat_template_stop_token_trained(
            processing_class, chat_template=self.chat_template
        ):
            logger.warning(
                "The chat template does not include the assistant turn's end-of-turn token in the loss mask; "
                "the model may not learn to stop."
            )

        # Dataset
        if self.padding_free and not args.packing and args.max_length is not None and not self._is_vision_dataset:
            raise ValueError(
                "When `padding_free=True` without packing, `max_length` is not enforced. Either enable packing "
                "(e.g., `packing=True, packing_strategy='bfd'`), provide already truncated inputs, or set "
                "`max_length=None`."
            )
        # 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.
        self._skip_prepare_dataset = (
            args.dataset_kwargs is not None
            and args.dataset_kwargs.get("skip_prepare_dataset", False)
            or self._is_vision_dataset
        )
        # Kept on the instance so that `evaluate` can preprocess freshly-passed eval datasets the same way.
        self._formatting_func = formatting_func
        eval_datasets = (
            eval_dataset if isinstance(eval_dataset, dict) else {"eval": eval_dataset} if eval_dataset else {}
        )
        self._reject_skip_prepare_without_labels({"train": train_dataset, **eval_datasets}, data_collator)
        if not self._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`."
                )
            self._unsloth_model_ref = model
            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 not args.use_liger_kernel:  # liger supports dft loss by just passing use_token_scaling=True
            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
            elif args.loss_type == "chunked_nll":
                # Same math as `"nll"` but the `lm_head` matmul is skipped on ignored tokens and the CE is computed in
                # chunks of tokens. Implemented by patching the model's forward before `super[].__init__` so accelerate
                # wraps the patched forward.
                # For PEFT, patch the inner causal LM rather than the `PeftModel` wrapper. LoRA / IA³ /
                # `modules_to_save` adapters live in the module tree, so they're hit even when we bypass
                # `PeftModel.forward`. Prompt-learning variants need `PeftModel.forward` to run first [to inject
                # virtual tokens], then it delegates into the patched inner forward.
                target = model.get_base_model() if is_peft_model(model) else model
                # The chunked path reads the output projection weight directly, which would silently drop the
                # adapter delta [and starve its parameters of gradients] if the head itself is a PEFT tuner layer.
                if is_peft_model(model):
                    from peft.tuners.tuners_utils import BaseTunerLayer

                    if isinstance(target.get_output_embeddings(), BaseTunerLayer):
                        raise ValueError(
                            "`loss_type='chunked_nll'` is not supported when `lm_head` is wrapped by a PEFT adapter "
                            "(e.g. `target_modules='all-linear'` or explicitly including `'lm_head'`). Either remove "
                            "`lm_head` from `target_modules`, or switch to `loss_type='nll'`. If this is a real use "
                            "case for you, please open an issue at https://github.com/huggingface/trl/issues."
                        )
                _patch_chunked_ce_lm_head(target, chunk_size=_CHUNKED_LM_HEAD_CHUNK_SIZE, is_vlm=self._is_vlm)
            else:
                raise ValueError(
                    f"Invalid `loss_type` {args.loss_type} passed. Supported values are 'nll', 'dft', and "
                    "'chunked_nll'."
                )
        elif args.loss_type == "chunked_nll":
            raise ValueError("`loss_type='chunked_nll'` is not compatible with `use_liger_kernel=True`.")

        # Transformers explicitly set use_reentrant=True in the past to silence a PyTorch warning, but the default was
        # never updated once PyTorch switched to recommending use_reentrant=False. Until that change lands upstream
        # [see https://github.com/huggingface/transformers/pull/43203] and is released [most likely in 5.0.0], we
        # default to the recommended non-reentrant behavior here, while preserving any user-provided value.
        if args.gradient_checkpointing and Version(transformers.__version__) < Version("5.0.0"):
            args.gradient_checkpointing_kwargs = args.gradient_checkpointing_kwargs or {}
            args.gradient_checkpointing_kwargs.setdefault("use_reentrant", False)

        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()

        # MoE load-balancing auxiliary loss, applied to Mixture-of-Experts models [no effect otherwise]
        text_config = model.config.get_text_config()
        is_moe = getattr(text_config, "output_router_logits", None) is not None
        self.aux_loss_enabled = is_moe and self.args.router_aux_loss_coef != 0.0
        if is_moe:
            # The native and chunked forwards add the aux loss from the model config, so keep the config in sync with
            # the coef: enable it [and propagate the coef] when non-zero, disable it otherwise. This overrides any
            # `output_router_logits` the model was loaded with, so `router_aux_loss_coef=0.0` reliably turns it off.
            text_config.output_router_logits = self.aux_loss_enabled
            text_config.router_aux_loss_coef = self.args.router_aux_loss_coef

        # Initialize the metrics
        self._metrics = {"train": defaultdict(list), "eval": defaultdict(list)}
        self._total_train_tokens = 0

        # Add tags to the model
        self.model.add_model_tags(self._tag_names)

    def _tokenize(
        self,
        processing_class: PreTrainedTokenizerBase | ProcessorMixin,
        input: str | list,
        **kwargs,
    ) -> dict[str, list]:
        """Tokenize a single example for dataset preprocessing.

        Dispatches to `apply_chat_template` for conversational input (list of message dicts) and to `__call__` for
        non-conversational input (str). For VLMs, normalizes the batch dimension that processors emit even for single
        examples.

        Args:
            processing_class ([`~transformers.PreTrainedTokenizerBase`] or [`~transformers.ProcessorMixin`]):
                The tokenizer or processor to use.
            input (`str` or `list`):
                A string for non-conversational input, or a list of message dicts for conversational input.
            **kwargs:
                Forwarded to `apply_chat_template` (e.g. `add_generation_prompt`, `return_assistant_tokens_mask`).

        Returns:
            `dict` with at least an `"input_ids"` key mapping to a flat `list[int]`.
        """
        if isinstance(input, list):  # conversational: list of message dicts
            if self._is_vlm:
                input = prepare_multimodal_messages(input)
            result = processing_class.apply_chat_template(
                input, tokenize=True, return_dict=True, chat_template=self.chat_template, **kwargs
            )
        else:  # non-conversational: plain text string
            result = processing_class(text=input)
        # VLMs emit a batch dimension even for single examples; unwrap it
        if self._is_vlm:
            return {k: v[0] for k, v in result.items()}
        return result

    def _prepare_dataset(
        self,
        dataset: Union[Dataset, IterableDataset],
        processing_class,
        args,
        packing: bool,
        formatting_func: Optional[Callable[[dict], str]],
        dataset_name: str,
    ) -> Union[Dataset, IterableDataset]:
        # All Unsloth Zoo code licensed under LGPLv3
        try:
            if isinstance(dataset, ConstantLengthDataset): return dataset
        except:
            pass
    
        map_kwargs = {}
        use_desc = isinstance(dataset, Dataset)
        is_vlm = hasattr(processing_class, "tokenizer")
        tokenizer = processing_class
        if is_vlm: tokenizer = processing_class.tokenizer
    
        # Detect whether the model's module needs token_type_ids when training
        import sys as _sys
        _needs_token_type_ids = False
        # Split to avoid compiler substring match on masking_utils names
        _ccm = 'create_' + 'causal_mask_mapping'
        _model = getattr(self, '_unsloth_model_ref', None) or getattr(self, 'model', None)
        if _model is not None:
            for _m in (_model, getattr(_model, 'model', None)):
                if _m is None: continue
                _mod = _sys.modules.get(type(_m).__module__)
                if _mod is not None and hasattr(_mod, _ccm):
                    _needs_token_type_ids = True
                    break
    
        if not _needs_token_type_ids:
            # Fallback: model not yet available, check processor class MRO
            for _base in type(processing_class).__mro__:
                _base_mod = getattr(_base, '__module__', '')
                if 'transformers.models.' in _base_mod:
                    _modeling_mod = _base_mod.replace('.processing_', '.modeling_')
                    _mod = _sys.modules.get(_modeling_mod)
                    if _mod is not None and hasattr(_mod, _ccm):
                        _needs_token_type_ids = True
                        break
        if _needs_token_type_ids and hasattr(args, 'remove_unused_columns'):
            args.remove_unused_columns = False
    
        # Get max length
        max_seq_length = getattr(args, "max_length", 0)
        if max_seq_length == 0: max_seq_length = getattr(args, "max_seq_length", 0)
        if max_seq_length == 0: max_seq_length = getattr(self, "max_seq_length", 0)
        if max_seq_length == 0: max_seq_length = getattr(self, "max_seq", 0)
        if max_seq_length == 0: raise RuntimeError("Unsloth: max_seq_length is 0! Please specify one!")
        dataset_text_field = getattr(args, "dataset_text_field", "text")
        do_truncation = max_seq_length != 0
        do_formatting_func = False
        do_tokenize = True
        do_prompt_completion = False
    
        # Get correct column names
        column_names = set(next(iter(dataset)).keys())
        used_column_names = ["input_ids"]
        if "attention_mask" in column_names:
            used_column_names.append("attention_mask")
        if _needs_token_type_ids:
            used_column_names.append("token_type_ids")
    
        # Skip tokenization if already tokenized; just set the data collator
        from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling
        if "labels" in column_names:
            # Most likely forgot data collator!
            if is_vlm and not hasattr(tokenizer, "pad"):
                raise RuntimeError(f"Unsloth: {processing_class.__class__} does not have .pad!")
            self.data_collator = DataCollatorForSeq2Seq(tokenizer)
            used_column_names.append("labels")
            do_tokenize = False
        elif "input_ids" in column_names:
            if is_vlm and not hasattr(tokenizer, "pad"):
                raise RuntimeError(f"Unsloth: {processing_class.__class__} does not have .pad!")
            self.data_collator = DataCollatorForLanguageModeling(tokenizer, mlm = False)
            do_tokenize = False
        elif "prompt" in column_names and "completion" in column_names:
            # Prompt/completion dataset (used with completion_only_loss).
            # TRL's __init__ already set self.data_collator for completion_only_loss
            # before calling us -- we must NOT overwrite it here.
            do_prompt_completion = True
            used_column_names.append("completion_mask")
        elif dataset_text_field not in column_names:
            do_formatting_func = True
            if formatting_func is None:
                raise RuntimeError("Unsloth: You must specify a `formatting_func`")
        pass
    
        if do_tokenize:
            if do_formatting_func:
                test_text = formatting_func(next(iter(dataset)))
                if not isinstance(test_text, list):
                    raise ValueError(
                        "Unsloth: The `formatting_func` should return a list of processed strings."
                    )
                test_text = test_text[0]
            elif do_prompt_completion:
                _first_ex = next(iter(dataset))
                try:
                    from trl import is_conversational as _sft_is_conversational
                except ImportError:
                    def _sft_is_conversational(example):
                        for key in ("prompt", "completion", "messages"):
                            val = example.get(key)
                            if isinstance(val, list) and val and isinstance(val[0], dict):
                                if "role" in val[0] and "content" in val[0]:
                                    return True
                        return False
                _is_conv = _sft_is_conversational(_first_ex)
                if not _is_conv:
                    test_text = _first_ex["prompt"]
                else:
                    test_text = None  # chat template handles BOS
            else:
                test_text = next(iter(dataset))[dataset_text_field][0]
    
            chat_template = getattr(processing_class, 'chat_template', '')
            if chat_template == '' and is_vlm:
                chat_template = getattr(tokenizer, 'chat_template', '')
            if chat_template is None:
                chat_template = ''
    
            # Detect double BOS so we can drop the duplicate
            add_special_tokens = True
            bos_token_1 = getattr(processing_class, 'bos_token', None)
            bos_token_2 = getattr(tokenizer, 'bos_token', None)
            bos_token = bos_token_1 or bos_token_2
    
            if bos_token is not None:
                if (test_text is not None and test_text.startswith(bos_token)) or bos_token in chat_template:
                    add_special_tokens = False
                    print("Unsloth: We found double BOS tokens - we shall remove one automatically.")
            pass
    
            def _tokenize(example):
                return tokenizer(
                    example[dataset_text_field] if not do_formatting_func else formatting_func(example),
                    truncation = do_truncation,
                    max_length = max_seq_length,
                    return_token_type_ids = _needs_token_type_ids,
                    add_special_tokens = add_special_tokens,
                )
            pass
    
            if not isinstance(dataset, IterableDataset):
                import multiprocessing as _mp
                dataset_num_proc = getattr(args, "dataset_num_proc", None)
                if dataset_num_proc is None:
                    if _mp.get_start_method() != 'fork':
                        dataset_num_proc = None
                    else:
                        import psutil
                        dataset_num_proc = min(max((psutil.cpu_count() or 1)+4, 2), 64)
                        memory_gb_left = psutil.virtual_memory().available / (1024**3)
                        if memory_gb_left <= 2:
                            dataset_num_proc = 1
                        else:
                            dataset_num_proc = min(dataset_num_proc, int(memory_gb_left))
                map_kwargs["num_proc"] = dataset_num_proc
            else:
                map_kwargs["batch_size"] = dataset._ex_iterable.batch_size
    
            if do_prompt_completion:
                _eos_token = getattr(tokenizer, 'eos_token', None)
    
                def _tokenize_pc(example):
                    if _is_conv:
                        prompt_ids = processing_class.apply_chat_template(
                            example["prompt"], tokenize=True,
                            add_generation_prompt=True, return_dict=False,
                            tools=example.get("tools"),
                            **(example.get("chat_template_kwargs") or {}),
                        )
                        if prompt_ids and isinstance(prompt_ids[0], list):
                            prompt_ids = prompt_ids[0]
                        pc_processed = processing_class.apply_chat_template(
                            example["prompt"] + example["completion"],
                            return_dict=True, tokenize=True,
                            tools=example.get("tools"),
                            **(example.get("chat_template_kwargs") or {}),
                        )
                        if isinstance(pc_processed.get("input_ids", [None])[0], list):
                            pc_processed = {k: v[0] for k, v in pc_processed.items()}
                        pc_ids = pc_processed["input_ids"]
                    else:
                        _completion = example["completion"]
                        if _eos_token and not _completion.endswith(_eos_token):
                            _completion = _completion + _eos_token
                        prompt_ids = tokenizer(
                            example["prompt"], add_special_tokens=add_special_tokens,
                        )["input_ids"]
                        pc_ids = tokenizer(
                            example["prompt"] + _completion,
                            add_special_tokens=add_special_tokens,
                        )["input_ids"]
                    if do_truncation and max_seq_length > 0:
                        pc_ids = pc_ids[:max_seq_length]
                    n_prompt = min(len(prompt_ids), len(pc_ids))
                    completion_mask = [0] * n_prompt + [1] * (len(pc_ids) - n_prompt)
                    result = {"input_ids": pc_ids, "completion_mask": completion_mask}
                    if _needs_token_type_ids:
                        result["token_type_ids"] = [0] * len(pc_ids)
                    return result
    
                if use_desc:
                    map_kwargs["desc"] = 'Unsloth: Tokenizing ["prompt"+"completion"]'
                import warnings as _w
                with _w.catch_warnings():
                    _w.filterwarnings("ignore", message=".*couldn't be hashed properly.*")
                    dataset = dataset.map(
                        _tokenize_pc, batched=False,
                        remove_columns=list(column_names), **map_kwargs,
                    )
            else:
                if use_desc: map_kwargs["desc"] = f'Unsloth: Tokenizing ["{dataset_text_field}"]'
                import warnings as _w
                with _w.catch_warnings():
                    _w.filterwarnings("ignore", message=".*couldn't be hashed properly.*")
                    dataset = dataset.map(_tokenize, batched = True, remove_columns = list(column_names), **map_kwargs)
    
            # VLMs need .pad; switch the data collator
            if is_vlm and not hasattr(processing_class, "pad") and not do_prompt_completion:
                data_collator = DataCollatorForLanguageModeling(tokenizer, mlm = False)
                self.data_collator = data_collator
            pass
        pass
        if packing:
            # Use TRL's pack_dataset if available
            try:
                pack_dataset
            except:
                print("Unsloth: Hugging Face's packing is currently buggy - we're disabling it for now!")
                return dataset
    
            if max_seq_length == 0:
                raise ValueError("When packing is enabled, `max_seq_length` can't be `None`.")
    
            if use_desc: map_kwargs["desc"] = f"Unsloth: Packing {dataset_name} dataset"
            dataset = pack_dataset(
                dataset.select_columns(used_column_names),
                max_seq_length,
                getattr(args, "packing_strategy", "bfd"),
                map_kwargs,
            )
        pass
        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",
        # "attention_mask" and "labels"). Dataset preparation also produces a "seq_lengths" column (for packing /
        # padding-free), so we override the default signature columns to keep it alongside the model inputs.
        if self._signature_columns is None:
            if self._is_vision_dataset:
                self._signature_columns = ["messages", "prompt", "completion", "image", "images"]
            else:
                self._signature_columns = ["input_ids", "labels", "seq_lengths"]

    def _reject_skip_prepare_without_labels(self, datasets: dict[str, Dataset], data_collator) -> None:
        # This guard may look defensive, but it covers a behavior change introduced when label building moved from
        # the collator to dataset preparation: the collator used to consume the mask columns directly, so a
        # skipped-preparation dataset carrying masks trained correctly. Now labels are built during preparation, which
        # is skipped here, and the collator ignores the mask columns. Without a "labels" column, such a dataset would
        # silently optimize the loss over the full sequence, so we fail loudly instead. Checked both at init and in
        # `evaluate`, since a dataset passed directly to `evaluate` also skips preparation.
        if not (
            self._skip_prepare_dataset
            and not self._is_vision_dataset
            and isinstance(data_collator, DataCollatorForLanguageModeling)
        ):
            return
        for name, dataset in datasets.items():
            cols = get_dataset_column_names(dataset)
            if "labels" not in cols and ("completion_mask" in cols or "assistant_masks" in cols):
                raise ValueError(
                    f"The {name} dataset has mask columns but no 'labels', and `skip_prepare_dataset=True` skips "
                    "label building, so it would train on the full sequence. Add a 'labels' column (-100 for "
                    "non-loss tokens) or drop `skip_prepare_dataset`."
                )

    def evaluate(
        self,
        eval_dataset: Dataset | dict[str, Dataset] | None = None,
        ignore_keys: list[str] | None = None,
        metric_key_prefix: str = "eval",
    ) -> dict[str, float]:
        # When a dataset is passed directly to `evaluate` (e.g. a held-out test set), preprocess it the same way
        # `__init__` does, so that `evaluate` accepts the same dataset types as the trainer (language modeling,
        # prompt-completion, etc.). `_prepare_dataset` is idempotent: it skips datasets that are already tokenized. A
        # `str` selects a dataset that was already prepared at init time, so it's left untouched.
        if not self._skip_prepare_dataset and eval_dataset is not None and not isinstance(eval_dataset, str):
            packing = self.args.packing if self.args.eval_packing is None else self.args.eval_packing
            if isinstance(eval_dataset, dict):
                eval_dataset = {
                    key: self._prepare_dataset(
                        dataset, self.processing_class, self.args, packing, self._formatting_func, key
                    )
                    for key, dataset in eval_dataset.items()
                }
            else:
                eval_dataset = self._prepare_dataset(
                    eval_dataset, self.processing_class, self.args, packing, self._formatting_func, "eval"
                )
        eval_datasets = (
            eval_dataset
            if isinstance(eval_dataset, dict)
            else {"eval": eval_dataset}
            if eval_dataset is not None and not isinstance(eval_dataset, str)
            else {}
        )
        self._reject_skip_prepare_without_labels(eval_datasets, self.data_collator)
        return super().evaluate(
            eval_dataset=eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix
        )

    def compute_loss(
        self,
        model,
        inputs,
        return_outputs = False,
        num_items_in_batch = None,
    ):
        outputs = super().compute_loss(
            model,
            inputs,
            return_outputs = return_outputs,
            num_items_in_batch = num_items_in_batch,
        )
        return outputs

    def prediction_step(self, model, inputs, prediction_loss_only, ignore_keys=None):
        # Preserve the eval loop intent so compute_loss can decide whether logits are needed.
        inputs["_prediction_loss_only"] = prediction_loss_only
        return super().prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)

    # 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: float | None = 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)
class UnslothSFTTrainer(_UnslothSFTTrainer):
    """
    
    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 trl import SFTTrainer
    >>> from datasets import load_dataset

    >>> dataset = load_dataset("roneneldan/TinyStories", split="train[:1%]")

    >>> trainer = SFTTrainer(
    ...     model="Qwen/Qwen2.5-0.5B-Instruct",
    ...     train_dataset=dataset,
    ... )
    >>> trainer.train()
    ```

    Args:
        model (`str` or [`~transformers.PreTrainedModel`] or [`~peft.PeftModel`]):
            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 `<ModelArchitecture>.from_pretrained` (where `<ModelArchitecture>` is derived from the model
              config) with the keyword arguments in `args.model_init_kwargs`.
            - A [`~transformers.PreTrainedModel`] object. Only causal language models are supported.
            - A [`~peft.PeftModel`] object. Only causal language models are supported.
        args ([`SFTConfig`], *optional*):
            Configuration for this trainer. If `None`, a default configuration is used.
        data_collator ([`~transformers.DataCollator`], *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.
            Custom collators must truncate sequences before padding; the trainer does not apply post-collation
            truncation.
        train_dataset ([`~datasets.Dataset`] or [`~datasets.IterableDataset`]):
            Dataset to use for training. This trainer 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, Dataset | IterableDataset]`):
            Dataset to use for evaluation. It must meet the same requirements as `train_dataset`.
        processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.ProcessorMixin`], *optional*):
            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`, *optional*):
            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]`, *optional*):
            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`], *optional*):
            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[torch.optim.Optimizer | None, torch.optim.lr_scheduler.LambdaLR | None]`, *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*):
            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*):
            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`], *optional*):
            PEFT configuration used to wrap the model. If `None`, the model is not wrapped.
        formatting_func (`Callable`, *optional*):
            Formatting function applied to the dataset before tokenization. Applying the formatting function explicitly
            converts the dataset into a [language modeling](#language-modeling) type.
    
    """
    def __init__(
        self,
        model,
        args = None,
        data_collator = None,
        train_dataset = None,
        eval_dataset = None,
        processing_class = None,
        compute_loss_func = None,
        compute_metrics = None,
        callbacks = None,
        optimizer_cls_and_kwargs = None,
        preprocess_logits_for_metrics = None,
        peft_config = None,
        formatting_func = None,
        **kwargs
    ):
        if args is None: args = UnslothSFTConfig()
        use_bf16 = getattr(args, 'bf16', False)
        if type(use_bf16) is not bool: use_bf16 = False
        use_fp16 = getattr(args, 'fp16', False)
        if type(use_fp16) is not bool: use_fp16 = False
        force_float32 = False
        full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1'
        if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'):
            print('Unsloth: Switching to float32 training since model cannot work with float16')
            force_float32 = True
        mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32')
        dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None)
        if dtype is None: dtype = model.get_input_embeddings().weight.dtype
        from unsloth_zoo.utils import _get_dtype
        dtype = _get_dtype(dtype)
        float16 = dtype == torch.float16
        if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`')
        if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`')
        if force_float32:
            # Forced float32 training
            args.fp16 = False
            args.bf16 = False
            os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'
            if hasattr(args, 'mixed_precision'): args.mixed_precision = 'no'
            # args.mixed_precision is a new argument which needs to be set now
        elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32':
            # Mixed precision training
            args.fp16 = float16
            args.bf16 = not float16
            os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16'
            if hasattr(args, 'mixed_precision'): args.mixed_precision = 'fp16' if float16 else 'bf16'
            # args.mixed_precision is a new argument which needs to be set now
        elif mixed_precision_dtype == 'bfloat16':
            # Both False since bfloat16 full finetuning doesn't do any autocasting.
            args.fp16 = False
            args.bf16 = False
            os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'
            if hasattr(args, 'mixed_precision'): args.mixed_precision = 'no'
            # args.mixed_precision is a new argument which needs to be set now
        
        if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no':
            args.eval_strategy = 'steps'
            if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1
        ga_steps = getattr(args, 'gradient_accumulation_steps', None)
        if ga_steps is not None and ga_steps > 1:
            from transformers import __version__ as transformers_version
            if Version(transformers_version) <= Version('4.45.2'):
                print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n'
                      '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`')
        if getattr(args, 'eval_strategy', 'no') != 'no':
            eval_bsz = getattr(args, 'per_device_eval_batch_size', 8)
            if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size
            if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps
        fp16_full_eval = getattr(args, 'fp16_full_eval', False)
        if type(fp16_full_eval) is not bool: fp16_full_eval = False
        bf16_full_eval = getattr(args, 'bf16_full_eval', False)
        if type(bf16_full_eval) is not bool: bf16_full_eval = False
        if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True
        if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False
        if force_float32:
            args.bf16_full_eval = False
            args.fp16_full_eval = False
        elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16':
            args.bf16_full_eval = True
            args.fp16_full_eval = False
        elif not bf16_full_eval and not fp16_full_eval:
            args.bf16_full_eval = args.bf16
            args.fp16_full_eval = args.fp16
        _output_logits = False
        if locals().get('compute_metrics', None) is not None: _output_logits = True
        if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True
        if _output_logits:
            os.environ['UNSLOTH_RETURN_LOGITS'] = '1'
        if model is not None:
            _warnings_issued = getattr(model, 'warnings_issued', None)
            if _warnings_issued is None:
                model.warnings_issued = {}
            elif not isinstance(_warnings_issued, dict):
                try:
                    model.warnings_issued = dict(_warnings_issued)
                except Exception:
                    model.warnings_issued = {}
        if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'):
            pass
        else:
            model_max_seq_length = getattr(model, 'max_seq_length', None)
            args_max_seq_length  = getattr(args,  'max_seq_length', None)
            if args_max_seq_length is None and model_max_seq_length is not None:
                max_seq_length = model.max_seq_length
                if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length
            elif args_max_seq_length is not None and model_max_seq_length is not None:
                if args_max_seq_length > model_max_seq_length:
                    print('Unsloth: You set `max_seq_length` as ' + str(args_max_seq_length) + ' but '
                           'the maximum the model supports is ' + str(model_max_seq_length) + '. We shall reduce it.')
                    args.max_seq_length = model_max_seq_length
        if 'max_length' not in locals() and not hasattr(args, 'max_length'):
            pass
        else:
            if hasattr(args, 'max_seq_length') and args.max_seq_length is not None and args.max_seq_length > 0:
                if hasattr(args, 'max_length'):
                    args.max_length = args.max_seq_length
                    max_length = args.max_length
            else:
                model_max_length = getattr(model, 'max_seq_length', None)
                if model_max_length is None: model_max_length = getattr(model, 'max_length', None)
                if model_max_length is not None:
                    args.max_length = model_max_length
                    max_length = args.max_length
                elif hasattr(args, 'max_length') and args.max_length is not None:
                    max_length = args.max_length
                    # if we are here, then we are in a weird case where max_length is set but max_seq_length is not set
                    setattr(model, 'max_seq_length', max_length)
                else:
                    print('Unsloth: We did not find `max_seq_length` or `max_length` in the model or args. We will set it to 1024.')
                    args.max_length = 1024
        if model is not None and hasattr(model, 'for_training'):
            model.for_training(use_gradient_checkpointing=getattr(args, 'gradient_checkpointing', True))
        if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right'
        if 'processing_class' in locals():
            if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right'
            if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right'
        __tokenizer = processing_class if 'processing_class' in locals() else tokenizer
        from unsloth_zoo.vision_utils import UnslothVisionDataCollator
        if not isinstance(data_collator, UnslothVisionDataCollator):
            if isinstance(data_collator, DataCollatorForSeq2Seq) and 'labels' not in train_dataset.column_names:
                data_collator = TransformersDataCollatorForLanguageModeling(
                    __tokenizer,
                    mlm = False,
                    mlm_probability = 0.0,
                    pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None),
                )
            elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling) and 'labels' in train_dataset.column_names:
                data_collator = DataCollatorForSeq2Seq(
                    __tokenizer,
                    pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None),
                )
        else:
            if hasattr(args, 'remove_unused_columns'): args.remove_unused_columns = False
            if hasattr(args, 'dataset_text_field'): args.dataset_text_field = ''
            if hasattr(args, 'dataset_kwargs'): args.dataset_kwargs = {'skip_prepare_dataset': True}
        if not isinstance(data_collator, UnslothVisionDataCollator):
            if not hasattr(__tokenizer, 'pad') and hasattr(__tokenizer, 'tokenizer'):
                if isinstance(data_collator, DataCollatorForSeq2Seq):
                    data_collator = DataCollatorForSeq2Seq(
                        __tokenizer.tokenizer,
                        pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None),
                    )
                elif isinstance(data_collator, TransformersDataCollatorForLanguageModeling):
                    data_collator = TransformersDataCollatorForLanguageModeling(
                        __tokenizer.tokenizer,
                        mlm = False,
                        mlm_probability = 0.0,
                        pad_to_multiple_of = getattr(args, 'pad_to_multiple_of', None),
                    )
        other_metrics = []
        
        from unsloth_zoo.logging_utils import PatchRLStatistics
        PatchRLStatistics('sft_trainer', other_metrics)
        IGNORED_TOKENIZER_NAMES = os.environ.get('UNSLOTH_IGNORED_TOKENIZER_NAMES', '').split('\n')
        from unsloth_zoo.tokenizer_utils import fix_untrained_tokens
        from unsloth_zoo.training_utils  import fix_zero_training_loss
        if 'tokenizer' not in locals(): tokenizer = processing_class
        fix_untrained_tokens(model, tokenizer, train_dataset, IGNORED_TOKENIZER_NAMES, eps = 1e-16)
        fix_zero_training_loss(model, tokenizer, train_dataset)
        
        # [TODO] Fix up DataParallel multiplying batch sizes
        # [TODO] DDP works, but DP seems to not work? [TODO]
        if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1:
            if getattr(args, "_n_gpu", 1) != 1:
                args._n_gpu = 1
        if "model" in locals() and hasattr(model, "for_training"):
            model.for_training(use_gradient_checkpointing=getattr(args, 'gradient_checkpointing', True))
        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,
            optimizer_cls_and_kwargs = optimizer_cls_and_kwargs,
            preprocess_logits_for_metrics = preprocess_logits_for_metrics,
            peft_config = peft_config,
            formatting_func = formatting_func,**kwargs)
        if "model" in locals() and hasattr(model, "for_inference"):
            model.for_inference()
        if hasattr(self, 'neftune_hook_handle'):
            self.neftune_hook_handle.remove()
            if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle
        if getattr(args, 'neftune_noise_alpha', None) is not None:
            model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha
        pass
        if hasattr(self, 'accelerator'):
            scaler = self.accelerator.scaler
            current_model = model
            while hasattr(current_model, 'model'):
                current_model.accelerator_scaler = scaler
                current_model = current_model.model
            current_model.accelerator_scaler = scaler
        pass
        if hasattr(self, 'train'):
            self.train = MethodType(prepare_for_training_mode(self.__class__.train), self)
        pass
        if hasattr(self, 'llm') and self.llm is not None and hasattr(self.llm, 'get_tokenizer'):
            _vllm_tok = self.llm.get_tokenizer()
            _pc = getattr(self, 'processing_class', None) or getattr(self, 'tokenizer', None)
            if _vllm_tok is not None and _pc is not None and getattr(_pc, 'chat_template', None) is not None and getattr(_vllm_tok, 'chat_template', None) is None:
                _vllm_tok.chat_template = _pc.chat_template
        pass
        
pass


if hasattr(logger, "addFilter"):
    import logging
    class HideLoggingMessage(logging.Filter):
        def __init__(self, text): self.text = text
        def filter(self, x): return not (self.text in x.getMessage())
    pass
    logger.addFilter(HideLoggingMessage("`use_cache=True`"))