pixtral example

---------

Co-authored-by: Sunny <sunny@Sunnys-MacBook-Air.local>
Co-authored-by: sunny <sunnyliu19981005@gmail.com>

docs/input_output.qmd

@@ -205,7 +205,7 @@ ds = load_from_disk(f'last_run_prepared/{directory[0]}/')
     hi there!.  goodbye  farewell</s>
 ```
 
-We can check that the right tokens are ingored by comparing the labels
+We can check that the right tokens are ignored by comparing the labels
 to each token:
 
 ```python

docs/multimodal.qmd

@@ -0,0 +1,28 @@
+# MultiModal / Vision Language Models (BETA)
+
+### Supported Models
+
+- Mllama, i.e. llama with vision models
+
+### Usage
+
+Currently multimodal support is limited and doesn't have full feature parity. To finetune a multimodal Llama w/ LoRA,
+you'll need to use the following in YAML in combination with the rest of the required hyperparams.
+
+```yaml
+base_model: alpindale/Llama-3.2-11B-Vision-Instruct
+processor_type: AutoProcessor
+skip_prepare_dataset: true
+
+chat_template: llama3_2_vision
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+    field_messages: messages
+remove_unused_columns: false
+sample_packing: false
+
+# only finetune the Language model, leave the vision model and vision tower frozen
+lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+```

examples/llama-3-vision/lora-11b.yaml

@@ -0,0 +1,63 @@
+base_model: alpindale/Llama-3.2-11B-Vision-Instruct
+processor_type: AutoProcessor
+strict: false
+
+# these 3 lines are needed for now to handle vision chat templates w images
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+
+chat_template: llama3_2_vision
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+    field_messages: messages
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+adapter: lora
+lora_model_dir:
+
+sequence_len: 8192
+pad_to_sequence_len: false
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+local_rank:
+logging_steps: 1
+flash_attention: true
+eager_attention:
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:

examples/pixtral/lora-12b.yaml

@@ -0,0 +1,65 @@
+base_model: mistral-community/pixtral-12b
+processor_type: AutoProcessor
+
+load_in_8bit: true
+strict: false
+
+# these 3 lines are needed for now to handle vision chat templates w images
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+
+chat_template: llama3_2_vision
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+    field_messages: messages
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+adapter: lora
+lora_model_dir:
+
+sequence_len: 8192
+pad_to_sequence_len: false
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+local_rank:
+logging_steps: 1
+flash_attention: true
+eager_attention:
+
+warmup_ratio: 0.1
+evals_per_epoch: 1
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:

requirements.txt

@@ -1,9 +1,9 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
-peft==0.12.0
-transformers @ git+https://github.com/huggingface/transformers.git@0963229e287501bed52ae1dabc17922524de6992
+peft==0.13.0
+transformers==4.45.1
 tokenizers>=0.19.1
-bitsandbytes==0.43.3
+bitsandbytes==0.44.0
 accelerate==0.34.2
 datasets==2.21.0
 deepspeed==0.14.4
@@ -34,7 +34,7 @@ tensorboard
 python-dotenv==1.0.1
 autoawq>=0.2.5
 triton>=2.3.0
-liger-kernel==0.2.1
+liger-kernel==0.3.0
 
 mamba-ssm==1.2.0.post1
 

src/axolotl/cli/__init__.py

@@ -30,6 +30,7 @@ from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
 from axolotl.integrations.base import PluginManager
 from axolotl.logging_config import configure_logging
 from axolotl.train import TrainDatasetMeta
+from axolotl.utils.chat_templates import chat_templates
 from axolotl.utils.config import (
     normalize_cfg_datasets,
     normalize_config,
@@ -39,7 +40,7 @@ from axolotl.utils.data import load_prepare_dpo_datasets, prepare_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process
 from axolotl.utils.mlflow_ import setup_mlflow_env_vars
-from axolotl.utils.models import load_tokenizer
+from axolotl.utils.models import load_processor, load_tokenizer
 from axolotl.utils.tokenization import check_dataset_labels
 from axolotl.utils.trainer import prepare_opinionated_env, prepare_optim_env
 from axolotl.utils.wandb_ import setup_wandb_env_vars
@@ -234,7 +235,8 @@ def do_inference_gradio(
 
     model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
     prompter = cli_args.prompter
-    default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
+    # default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
+    default_tokens: Dict[str, str] = {}
 
     for token, symbol in default_tokens.items():
         # If the token isn't already specified in the config, add it
@@ -242,10 +244,13 @@ def do_inference_gradio(
             tokenizer.add_special_tokens({token: symbol})
 
     prompter_module = None
+    chat_template_str = None
     if prompter:
         prompter_module = getattr(
             importlib.import_module("axolotl.prompters"), prompter
         )
+    elif cfg.chat_template:
+        chat_template_str = chat_templates(cfg.chat_template)
 
     model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
@@ -259,7 +264,24 @@ def do_inference_gradio(
             )
         else:
             prompt = instruction.strip()
-        batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
+
+        if chat_template_str:
+            batch = tokenizer.apply_chat_template(
+                [
+                    {
+                        "role": "user",
+                        "content": prompt,
+                    }
+                ],
+                return_tensors="pt",
+                add_special_tokens=True,
+                add_generation_prompt=True,
+                chat_template=chat_template_str,
+                tokenize=True,
+                return_dict=True,
+            )
+        else:
+            batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
 
         model.eval()
         with torch.no_grad():
@@ -282,6 +304,7 @@ def do_inference_gradio(
             streamer = TextIteratorStreamer(tokenizer)
             generation_kwargs = {
                 "inputs": batch["input_ids"].to(cfg.device),
+                "attention_mask": batch["attention_mask"].to(cfg.device),
                 "generation_config": generation_config,
                 "streamer": streamer,
             }
@@ -407,9 +430,12 @@ def load_datasets(
     cli_args: TrainerCliArgs,
 ) -> TrainDatasetMeta:
     tokenizer = load_tokenizer(cfg)
+    processor = load_processor(cfg, tokenizer=tokenizer) if cfg.processor_type else None
 
     train_dataset, eval_dataset, total_num_steps, prompters = prepare_dataset(
-        cfg, tokenizer
+        cfg,
+        tokenizer,
+        processor=processor,
     )
 
     if cli_args.debug or cfg.debug:

src/axolotl/core/trainer_builder.py

@@ -21,6 +21,7 @@ from typing import Any, Dict, List, Literal, Optional, Type, Union
 import torch
 import transformers
 from datasets import Dataset
+from peft.optimizers import create_loraplus_optimizer
 from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
@@ -45,7 +46,6 @@ from trl import (
 )
 from trl.trainer.utils import pad_to_length
 
-from axolotl.loraplus import create_loraplus_optimizer
 from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
 from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
 from axolotl.utils import is_mlflow_available
@@ -61,12 +61,14 @@ from axolotl.utils.callbacks import (
     log_prediction_callback_factory,
 )
 from axolotl.utils.callbacks.lisa import lisa_callback_factory
+from axolotl.utils.chat_templates import chat_templates
 from axolotl.utils.collators import (
     BatchSamplerDataCollatorForSeq2Seq,
     DataCollatorForSeq2Seq,
     MambaDataCollator,
     V2BatchSamplerDataCollatorForSeq2Seq,
 )
+from axolotl.utils.collators.mm_chat import MultiModalChatDataCollator
 from axolotl.utils.models import ensure_dtype
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 from axolotl.utils.schedulers import (
@@ -250,6 +252,10 @@ class AxolotlTrainingMixins:
             "help": "workaround to pass an alternate lr scheduler to the HF trainer"
         },
     )
+    chat_template: Optional[str] = field(
+        default=None,
+        metadata={"help": "Chat template converting chat messages to text"},
+    )
 
 
 @dataclass
@@ -456,14 +462,14 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
             if self.args.loraplus_lr_ratio is not None:
                 loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
                 loraplus_lr_embedding = getattr(
-                    self.args, "loraplus_lr_embedding", None
+                    self.args, "loraplus_lr_embedding", 1e-6
                 )
                 self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
                     opt_model,
                     optimizer_cls,
-                    optimizer_kwargs,
-                    loraplus_lr_ratio,
-                    loraplus_lr_embedding,
+                    loraplus_lr_ratio=loraplus_lr_ratio,
+                    loraplus_lr_embedding=loraplus_lr_embedding,
+                    **optimizer_kwargs,
                 )
             elif self.args.alternate_optimizer == "optimi_adamw":
                 from optimi import AdamW
@@ -969,9 +975,9 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
             self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
                 opt_model,
                 optimizer_cls,
-                optimizer_kwargs,
-                loraplus_lr_ratio,
-                loraplus_lr_embedding,
+                loraplus_lr_ratio=loraplus_lr_ratio,
+                loraplus_lr_embedding=loraplus_lr_embedding,
+                **optimizer_kwargs,
             )
 
         if is_sagemaker_mp_enabled():
@@ -1043,10 +1049,11 @@ class TrainerBuilderBase(abc.ABC):
     _model_ref = None
     _peft_config = None
 
-    def __init__(self, cfg, model, tokenizer):
+    def __init__(self, cfg, model, tokenizer, processor=None):
         self.cfg = cfg
         self.model = model
         self.tokenizer = tokenizer
+        self.processor = processor
 
         # in case the model supports tagging, add the axolotl tag.
         # This makes sure the tag is correctly pushed even if a user calls
@@ -1417,6 +1424,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         report_to = []
         if self.cfg.use_wandb:
             report_to.append("wandb")
+            if self.cfg.wandb_name:
+                training_arguments_kwargs["run_name"] = self.cfg.wandb_name
         if self.cfg.use_mlflow:
             report_to.append("mlflow")
         if self.cfg.use_tensorboard:
@@ -1513,6 +1522,10 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         )
         training_arguments_kwargs["model_type"] = self.cfg.model_config_type
         training_arguments_kwargs["pretraining"] = bool(self.cfg.pretraining_dataset)
+        if self.cfg.chat_template:
+            training_arguments_kwargs["chat_template"] = chat_templates(
+                self.cfg.chat_template
+            )
 
         if self.cfg.rl == "orpo":
             training_arguments_kwargs["orpo_alpha"] = self.cfg.orpo_alpha
@@ -1574,6 +1587,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         )
         training_args = self.hook_post_create_training_args(training_args)
 
+        # unset run_name so wandb sets up experiment names
+        if self.cfg.use_wandb and training_args.run_name == training_args.output_dir:
+            training_args.run_name = (  # pylint: disable=attribute-defined-outside-init
+                None
+            )
+
         data_collator_kwargs = {
             "padding": True,  # True/"longest" is the default
         }
@@ -1653,7 +1672,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             else:
                 collator = BatchSamplerDataCollatorForSeq2Seq
         else:
-            collator = DataCollatorForSeq2Seq
+            if self.cfg.processor_type and self.processor:
+                collator = MultiModalChatDataCollator
+                kwargs["processor"] = self.processor
+                kwargs["chat_template"] = training_args.chat_template
+            else:
+                collator = DataCollatorForSeq2Seq
 
         return collator(
             self.tokenizer,

src/axolotl/loraplus.py

@@ -1,133 +0,0 @@
-"""Module for LoRA+"""
-
-# MIT License
-#
-# Copyright (c) 2024 nikhil-ghosh-berkeley
-# https://github.com/nikhil-ghosh-berkeley/loraplus
-
-import logging
-from functools import reduce
-
-from peft.tuners import lora
-from torch import nn
-from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
-from transformers.trainer_pt_utils import get_parameter_names
-
-LOG = logging.getLogger("axolotl.loraplus")
-
-
-def get_module(name, opt_model):
-    """
-    Retrieve a module from a model using its parameter name.
-    Args:
-        name (str): Full name of the parameter, typically including module path.
-        opt_model (torch.nn.Module): The model from which to retrieve the module.
-
-    Returns:
-        Module corresponding to the given name.
-    """
-    parent_idx = 2 if "lora" in name else 1
-    module_names = name.split(sep=".")[:-parent_idx]
-    module = reduce(getattr, module_names, opt_model)
-    return module
-
-
-def create_loraplus_optimizer(
-    opt_model,
-    optimizer_cls,
-    optimizer_kwargs,
-    loraplus_lr_ratio,
-    loraplus_lr_embedding=None,
-):
-    """
-    Creates an optimizer for the given model, applying LoRA-specific learning rate adjustments to different parameter groups.
-
-    Args:
-        opt_model (torch.nn.Module): The model for which the optimizer is being created.
-        optimizer_cls (class): The class of the optimizer to be used (e.g., torch.optim.Adam).
-        optimizer_kwargs (dict): A dictionary of keyword arguments for the optimizer's initialization.
-        loraplus_lr_ratio (float): The learning rate ratio to be applied to LoRA parameters.
-        loraplus_lr_embedding (float, optional): A specific learning rate for embedding parameters, with a default value if not provided.
-
-    Returns:
-        An instance of the specified optimizer class configured with the model's parameters organized into groups with custom learning rates.
-    """
-
-    assert loraplus_lr_ratio is not None, "loraplus_lr_ratio must be provided."
-
-    if loraplus_lr_embedding is None:
-        loraplus_lr_embedding = 1e-6
-
-    decay_parameters = get_parameter_names(opt_model, ALL_LAYERNORM_LAYERS)
-    decay_parameters = [name for name in decay_parameters if "bias" not in name]
-    param_groups = {
-        "groupA": {},
-        "groupB": {},
-        "groupB_no_decay": {},
-        "embedding": {},
-    }
-
-    for name, param in opt_model.named_parameters():
-        if not param.requires_grad:
-            continue
-
-        module = get_module(name, opt_model)
-        if isinstance(module, lora.Embedding):
-            param_groups["embedding"][name] = param
-        elif "lora_B" in name or param.ndim == 1:
-            if name in decay_parameters:
-                param_groups["groupB"][name] = param
-            else:
-                param_groups["groupB_no_decay"][name] = param
-        else:
-            param_groups["groupA"][name] = param
-
-    assigned_param_groups = ""
-    for group, group_params in param_groups.items():
-        assigned_param_groups += f"{group}\n {list(group_params.keys())}\n\n"
-    LOG.info(assigned_param_groups)
-
-    lr = optimizer_kwargs["lr"]  # pylint: disable=invalid-name
-    weight_decay = optimizer_kwargs.get("weight_decay", 0.0)
-
-    optimizer_grouped_parameters = [
-        {
-            "params": list(param_groups["groupA"].values()),
-            "weight_decay": weight_decay,
-            "lr": lr,
-        },
-        {
-            "params": list(param_groups["embedding"].values()),
-            "weight_decay": weight_decay,
-            "lr": loraplus_lr_embedding,
-        },
-        {
-            "params": list(param_groups["groupB"].values()),
-            "weight_decay": weight_decay,
-            "lr": lr * loraplus_lr_ratio,
-        },
-        {
-            "params": list(param_groups["groupB_no_decay"].values()),
-            "weight_decay": 0.0,
-            "lr": lr * loraplus_lr_ratio,
-        },
-    ]
-
-    optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
-    if optimizer_cls.__name__ == "Adam8bit":
-        import bitsandbytes
-
-        manager = bitsandbytes.optim.GlobalOptimManager.get_instance()
-
-        skipped = 0
-        for module in opt_model.modules():
-            if isinstance(module, nn.Embedding):
-                skipped += sum(
-                    {p.data_ptr(): p.numel() for p in module.parameters()}.values()
-                )
-                LOG.info(f"skipped {module}: {skipped/2**20}M params")
-                manager.register_module_override(module, "weight", {"optim_bits": 32})
-                LOG.debug(f"bitsandbytes: will optimize {module} in fp32")
-        LOG.info(f"skipped: {skipped/2**20}M params")
-
-    return optimizer

src/axolotl/monkeypatch/attention/mllama.py

@@ -0,0 +1,229 @@
+"""
+Monkeypatch for Vision Llama for FA2 support
+"""
+# pylint: disable=duplicate-code
+
+from typing import Optional, Tuple
+
+import torch
+from flash_attn.flash_attn_interface import flash_attn_func
+from transformers.cache_utils import Cache
+from transformers.modeling_flash_attention_utils import _flash_attention_forward
+from transformers.models.mllama.configuration_mllama import MllamaTextConfig
+from transformers.models.mllama.modeling_mllama import (
+    MllamaTextCrossAttention,
+    MllamaTextSelfAttention,
+    apply_rotary_pos_emb,
+    repeat_kv,
+)
+from transformers.utils import is_flash_attn_greater_or_equal_2_10
+
+
+class MllamaTextCrossFlashAttention2(MllamaTextCrossAttention):
+    """
+    Mllama flash cross-attention module. This module inherits from `MllamaTextCrossAttention` and
+    implements the forward pass using Flash Attention for improved performance.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # Check if flash attention version is greater or equal to 2.1
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cross_attention_states: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Cache] = None,
+        attention_mask: Optional[  # pylint: disable=unused-argument
+            torch.Tensor
+        ] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,  # pylint: disable=unused-argument
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        query_states = self.q_norm(query_states)
+
+        if cross_attention_states is not None:
+            key_states = self.k_proj(cross_attention_states)
+            value_states = self.v_proj(cross_attention_states)
+            key_states = key_states.view(
+                bsz, -1, self.num_key_value_heads, self.head_dim
+            ).transpose(1, 2)
+            value_states = value_states.view(
+                bsz, -1, self.num_key_value_heads, self.head_dim
+            ).transpose(1, 2)
+            key_states = repeat_kv(key_states, self.num_key_value_groups)
+            value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+            key_states = self.k_norm(key_states)
+            if past_key_value is not None:
+                key_states, value_states = past_key_value.update(
+                    key_states,
+                    value_states,
+                    self.layer_idx,
+                    {"cache_position": cache_position},
+                )
+        elif cache_position[0] != 0:
+            key_states, value_states = (
+                past_key_value.key_cache[self.layer_idx],
+                past_key_value.value_cache[self.layer_idx],
+            )
+        else:
+            raise ValueError(
+                "Cross attention layer can't find neither `cross_attn_states` nor cached values for key/values!"
+            )
+
+        # Transpose to get the expected layout for flash attention
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        # Apply Flash Attention
+        dropout_rate = self.dropout if self.training else 0.0
+        output = flash_attn_func(
+            query_states,
+            key_states,
+            value_states,
+            dropout_p=dropout_rate,
+            softmax_scale=None,
+            causal=False,
+            return_attn_probs=output_attentions,
+        )
+
+        attn_output = output.contiguous().view(bsz, q_len, -1)
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class MllamaTextSelfFlashAttention2(MllamaTextSelfAttention):
+    """
+    Mllama flash self-attention module. This module inherits from `MllamaTextSelfAttention` and
+    implements the forward pass using Flash Attention for improved performance.
+    """
+
+    def __init__(self, config: MllamaTextConfig, layer_idx: int, *args, **kwargs):
+        super().__init__(config, layer_idx, *args, **kwargs)
+
+        # Check if flash attention version is greater or equal to 2.1
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,  # pylint: disable=unused-argument
+        past_key_value=None,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,  # pylint: disable=unused-argument
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x num_heads x head_dim
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(
+            query_states, key_states, cos, sin
+        )
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
+            )
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        # Transpose to get the expected layout for flash attention
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        dropout_rate = self.dropout if self.training else 0.0
+
+        # Handle potential silent casting to float32
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = (
+                    self.config._pre_quantization_dtype  # pylint: disable=protected-access
+                )
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = _flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=dropout_rate,
+            use_top_left_mask=self._flash_attn_uses_top_left_mask,
+            is_causal=True,
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+def patch_mllama():
+    from transformers.models.mllama.modeling_mllama import (
+        MLLAMA_TEXT_ATTENTION_CLASSES,
+        MLLAMA_TEXT_CROSS_ATTENTION_CLASSES,
+        MLLAMA_VISION_ATTENTION_CLASSES,
+        MllamaPreTrainedModel,
+    )
+
+    MllamaPreTrainedModel._supports_flash_attn_2 = (  # pylint: disable=protected-access
+        True
+    )
+    MLLAMA_TEXT_ATTENTION_CLASSES["flash_attention_2"] = MllamaTextSelfFlashAttention2
+    MLLAMA_TEXT_CROSS_ATTENTION_CLASSES[
+        "flash_attention_2"
+    ] = MllamaTextCrossFlashAttention2
+    # fallback to SDPA
+    MLLAMA_VISION_ATTENTION_CLASSES[
+        "flash_attention_2"
+    ] = MLLAMA_VISION_ATTENTION_CLASSES["sdpa"]

src/axolotl/monkeypatch/multipack.py

@@ -10,6 +10,7 @@ from axolotl.monkeypatch.mixtral import patch_mixtral_moe_forward_zero3
 from axolotl.monkeypatch.utils import get_unpad_data
 
 SUPPORTED_MULTIPACK_MODEL_TYPES = [
+    "mllama_text_model",
     "llama",
     "mistral",
     "mixtral",

src/axolotl/monkeypatch/stablelm_attn_hijack_flash.py

@@ -16,6 +16,7 @@
 # This code is based off the following work:
 # https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
 # https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
+# pylint: disable=duplicate-code
 """ PyTorch StableLM Epoch model. """
 import importlib
 import math

src/axolotl/prompt_strategies/__init__.py

@@ -9,7 +9,7 @@ from axolotl.prompt_strategies.user_defined import UserDefinedDatasetConfig
 LOG = logging.getLogger("axolotl.prompt_strategies")
 
 
-def load(strategy, tokenizer, cfg, ds_cfg):
+def load(strategy, tokenizer, cfg, ds_cfg, processor=None):
     try:
         load_fn = "load"
         if strategy.split(".")[-1].startswith("load_"):
@@ -24,6 +24,8 @@ def load(strategy, tokenizer, cfg, ds_cfg):
             sig = inspect.signature(func)
             if "ds_cfg" in sig.parameters:
                 load_kwargs["ds_cfg"] = ds_cfg
+            if "processor" in sig.parameters:
+                load_kwargs["processor"] = processor
         return func(tokenizer, cfg, **load_kwargs)
     except ModuleNotFoundError:
         return None

src/axolotl/prompt_strategies/chat_template.py

@@ -5,6 +5,8 @@ HF Chat Templates prompt strategy
 import logging
 from typing import Any, Dict, List, Optional
 
+from transformers import ProcessorMixin
+
 from axolotl.prompt_tokenizers import PromptTokenizingStrategy
 from axolotl.prompters import IGNORE_TOKEN_ID, Prompter
 from axolotl.utils.chat_templates import chat_templates
@@ -20,6 +22,7 @@ class ChatTemplatePrompter(Prompter):
     def __init__(
         self,
         tokenizer,
+        processor=None,
         chat_template=None,
         max_length=2048,
         message_field_role: str = "from",
@@ -44,11 +47,12 @@ class ChatTemplatePrompter(Prompter):
         self.message_field_training = message_field_training
         self.message_field_training_detail = message_field_training_detail
         self.tokenizer = tokenizer
+        self.processor: ProcessorMixin = processor
         self.chat_template = chat_template
         self.max_length = max_length
         self.drop_system_message = drop_system_message
 
-    def build_prompt(self, conversation, add_generation_prompt=False):
+    def build_prompt(self, conversation, add_generation_prompt=False, images=None):
         turns = [
             {
                 "role": self.roles[t[self.message_field_role]],
@@ -61,6 +65,28 @@ class ChatTemplatePrompter(Prompter):
         if self.drop_system_message and turns[0]["role"] == "system":
             turns = turns[1:]
 
+        if self.processor:
+            text = self.processor.apply_chat_template(
+                turns,
+                chat_template=self.chat_template,
+                tokenize=False,
+                add_generation_prompt=add_generation_prompt,
+            )
+            batch = self.processor(
+                text=text,
+                images=images,
+                return_tensors="pt",
+                truncation=True,
+                max_length=self.max_length,
+            )
+            # workaround since processor works in batches instead of single examples
+            for k, val in batch.items():
+                if k in ["pixel_values"]:
+                    batch[k] = val.tolist()
+                else:
+                    batch[k] = val.squeeze().tolist()
+            return batch
+
         return self.tokenizer.apply_chat_template(
             turns,
             truncation=True,
@@ -191,6 +217,7 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
         super().__init__(prompter, tokenizer, train_on_inputs, sequence_len)
         self.roles_to_train = roles_to_train if roles_to_train is not None else []
         self.train_on_eos = train_on_eos
+        self.images = "images"
 
     @property
     def messages(self):
@@ -209,10 +236,21 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
             and not self.prompter.message_field_training_detail
         ):
             turns = self.get_conversation_thread(prompt)
+            images = self.get_images(prompt)
             prompt_ids = self.prompter.build_prompt(
-                turns[:-1], add_generation_prompt=True
+                turns[:-1],
+                add_generation_prompt=True,
+                images=images,
             )
-            input_ids = self.prompter.build_prompt(turns)
+            tokenized_res = self.prompter.build_prompt(turns, images=images)
+            tokenized_prompt = {}
+            if isinstance(tokenized_res, list):
+                input_ids = prompt_ids + tokenized_res[len(prompt_ids) :]
+                tokenized_prompt["input_ids"] = input_ids
+                tokenized_prompt["attention_mask"] = [1] * len(input_ids)
+            else:
+                input_ids = tokenized_res["input_ids"]
+                tokenized_prompt = tokenized_res
 
             if not self.train_on_inputs:
                 user_prompt_len = len(prompt_ids)
@@ -220,17 +258,9 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
             else:
                 labels = input_ids
 
-            tokenized_prompt = {
-                "input_ids": input_ids,
-                "labels": labels,
-                "attention_mask": [1] * len(input_ids),
-            }
+            tokenized_prompt["labels"] = labels
 
             return tokenized_prompt
-        LOG.info(self.roles_to_train)
-        LOG.info(self.train_on_eos)
-        LOG.info(self.prompter.message_field_training)
-        LOG.info(self.prompter.message_field_training_detail)
 
         turns = prompt[self.messages]
         input_ids = self.prompter.build_prompt(turns)
@@ -368,15 +398,18 @@ class ChatTemplateStrategy(PromptTokenizingStrategy):
     def get_conversation_thread(self, prompt):
         return prompt[self.messages]
 
+    def get_images(self, prompt):
+        return prompt.get(self.images, None)
 
-def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
+
+def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None, processor=None):
     ds_cfg = ds_cfg or {}
 
     prompter_params = {
         "tokenizer": tokenizer,
         "chat_template": chat_templates(ds_cfg.get("chat_template", "chatml")),
-        "message_field_role": ds_cfg.get("message_field_role", "from"),
-        "message_field_content": ds_cfg.get("message_field_content", "value"),
+        "message_field_role": ds_cfg.get("message_field_role", "role"),
+        "message_field_content": ds_cfg.get("message_field_content", "content"),
         "message_field_training": ds_cfg.get("message_field_training", None),
         "message_field_training_detail": ds_cfg.get(
             "message_field_training_detail",
@@ -386,6 +419,7 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
         "drop_system_message": ds_cfg.get("drop_system_message", False),
         # we need to add one for detecting sequences with exceeding the `sequence_len` limit.
         "max_length": cfg.sequence_len + 1,
+        "processor": processor,
     }
 
     strategy_params = {

src/axolotl/train.py

@@ -24,7 +24,7 @@ from axolotl.core.tokenizer_utils import fix_untrained_tokens
 from axolotl.logging_config import configure_logging
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.freeze import freeze_layers_except
-from axolotl.utils.models import load_model, load_tokenizer
+from axolotl.utils.models import load_model, load_processor, load_tokenizer
 from axolotl.utils.trainer import setup_trainer
 
 try:
@@ -69,6 +69,9 @@ def train(
         main_process_only=True,
     )
     tokenizer = load_tokenizer(cfg)
+    processor = None
+    if cfg.is_multimodal:
+        processor = load_processor(cfg, tokenizer)
 
     train_dataset = dataset_meta.train_dataset
     eval_dataset = dataset_meta.eval_dataset
@@ -96,7 +99,9 @@ def train(
     LOG.debug(msg)
     # we wait unitl the last possible moment to setup Accelerator
     Accelerator()
-    model, peft_config = load_model(cfg, tokenizer, inference=cli_args.inference)
+    model, peft_config = load_model(
+        cfg, tokenizer, processor=processor, inference=cli_args.inference
+    )
     model.generation_config.do_sample = True
 
     model_ref = None
@@ -122,6 +127,7 @@ def train(
         eval_dataset,
         (model, model_ref, peft_config),
         tokenizer,
+        processor,
         total_num_steps,
     )
 

src/axolotl/utils/collators/__init__.py

@@ -0,0 +1,10 @@
+"""
+shared axolotl collators for multipack, mamba, multimodal
+"""
+from .batching import (  # noqa: F401
+    BatchSamplerDataCollatorForSeq2Seq,
+    DataCollatorForSeq2Seq,
+    PretrainingBatchSamplerDataCollatorForSeq2Seq,
+    V2BatchSamplerDataCollatorForSeq2Seq,
+)
+from .mamba import MambaDataCollator  # noqa: F401

src/axolotl/utils/collators/batching.py

@@ -1,17 +1,14 @@
 """
 DataCollator for axolotl to pad labels and position_ids for packed sequences
 """
+
 from dataclasses import dataclass
-from typing import Any, Dict, Optional, Sequence, Union
+from typing import Any, Optional, Union
 
 import numpy as np
-import torch
-import transformers
 from transformers import PreTrainedTokenizerBase
 from transformers.utils import PaddingStrategy
 
-IGNORE_INDEX = -100
-
 
 @dataclass
 class DataCollatorForSeq2Seq:
@@ -183,34 +180,6 @@ class V2BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
         return super().__call__(out_features, return_tensors=return_tensors)
 
 
-@dataclass
-class MambaDataCollator:
-    """
-    Collator for State Space Models (Mamba)
-    """
-
-    tokenizer: transformers.PreTrainedTokenizer
-
-    def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
-        input_ids, labels = tuple(
-            [torch.LongTensor(instance[key]) for instance in instances]
-            for key in ("input_ids", "labels")
-        )
-        input_ids = torch.nn.utils.rnn.pad_sequence(
-            input_ids,
-            batch_first=True,
-            padding_value=self.tokenizer.pad_token_id,
-        )
-        labels = torch.nn.utils.rnn.pad_sequence(
-            labels, batch_first=True, padding_value=IGNORE_INDEX
-        )
-
-        return {
-            "input_ids": input_ids,
-            "labels": labels,
-        }
-
-
 @dataclass
 class PretrainingBatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
     """

src/axolotl/utils/collators/core.py

@@ -0,0 +1,4 @@
+"""
+basic shared collator constants
+"""
+IGNORE_INDEX = -100

src/axolotl/utils/collators/mamba.py

@@ -0,0 +1,38 @@
+"""
+collators for Mamba
+"""
+from dataclasses import dataclass
+from typing import Dict, Sequence
+
+import torch
+import transformers
+
+from axolotl.utils.collators.core import IGNORE_INDEX
+
+
+@dataclass
+class MambaDataCollator:
+    """
+    Collator for State Space Models (Mamba)
+    """
+
+    tokenizer: transformers.PreTrainedTokenizer
+
+    def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
+        input_ids, labels = tuple(
+            [torch.LongTensor(instance[key]) for instance in instances]
+            for key in ("input_ids", "labels")
+        )
+        input_ids = torch.nn.utils.rnn.pad_sequence(
+            input_ids,
+            batch_first=True,
+            padding_value=self.tokenizer.pad_token_id,
+        )
+        labels = torch.nn.utils.rnn.pad_sequence(
+            labels, batch_first=True, padding_value=IGNORE_INDEX
+        )
+
+        return {
+            "input_ids": input_ids,
+            "labels": labels,
+        }

src/axolotl/utils/collators/mm_chat.py

@@ -0,0 +1,77 @@
+"""
+Collators for multi-modal chat messages and packing
+"""
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Union
+
+from transformers import PreTrainedTokenizerBase, ProcessorMixin
+from transformers.data.data_collator import DataCollatorMixin
+from transformers.utils import PaddingStrategy
+
+
+@dataclass
+class MultiModalChatDataCollator(DataCollatorMixin):
+    """
+    Collator for multi-modal chat messages
+    """
+
+    tokenizer: PreTrainedTokenizerBase
+    processor: ProcessorMixin
+    return_tensors: str = "pt"
+    chat_template: Optional[str] = None
+    packing: bool = False
+    max_images: int = -1
+    padding: Union[bool, str, PaddingStrategy] = True
+    pad_to_multiple_of: Optional[int] = None
+
+    def __post_init__(self):
+        if self.packing:
+            raise ValueError("Packing is currently not supported.")
+
+    def torch_call(
+        self, examples: List[Union[List[int], Any, Dict[str, Any]]]
+    ) -> Dict[str, Any]:
+        # Handle dict or lists with proper padding and conversion to tensor.
+
+        return self.__class__.process_rows(
+            examples, self.processor, self.chat_template, self.max_images
+        )
+
+    @staticmethod
+    def process_rows(examples, processor, chat_template, max_images, length_only=False):
+        # HINT: use `_torch_collate_batch` to stack and pad tensors
+        # see also DataCollatorWithFlattening and DefaultDataCollator
+
+        # *** This is COPIED from the trl example sft_vlm.py code ***
+        # use this as a starting point
+
+        # Get the texts and images, and apply the chat template
+        texts = [
+            processor.apply_chat_template(
+                example["messages"], chat_template=chat_template, tokenize=False
+            )
+            for example in examples
+        ]
+        images = [example["images"] for example in examples]
+
+        if max_images > 0:
+            images = [img_batch[:max_images] for img_batch in images]
+
+        # Tokenize the texts and process the images
+        batch = processor(text=texts, images=images, return_tensors="pt", padding=True)
+
+        # The labels are the input_ids, and we mask the padding tokens in the loss computation
+        labels = batch["input_ids"].clone()
+        labels[labels == processor.tokenizer.pad_token_id] = -100  #
+        # Ignore the image token index in the loss computation (model specific)
+        image_token_id = processor.tokenizer.convert_tokens_to_ids(
+            processor.image_token
+        )
+        labels[labels == image_token_id] = -100
+        batch["labels"] = labels
+
+        if length_only:
+            return {
+                "length": [len(sample["input_ids"]) for sample in batch["input_ids"]]
+            }
+        return batch

src/axolotl/utils/config/__init__.py

@@ -121,15 +121,36 @@ def normalize_config(cfg):
         cfg.base_model_config = cfg.base_model
 
     model_config = load_model_config(cfg)
-    cfg.model_config_type = model_config.model_type
 
     cfg.tokenizer_config = (
         cfg.tokenizer_config or cfg.base_model_config or cfg.base_model
     )
 
+    cfg.is_multimodal = (
+        hasattr(model_config, "model_type")
+        and model_config.model_type in ["llava", "mllama"]
+        or any(
+            multimodal_name in cfg.base_model.lower()
+            for multimodal_name in [
+                "pixtral",
+            ]
+        )
+        or cfg.is_multimodal
+    )
+    if cfg.is_multimodal:
+        cfg.processor_config = (
+            cfg.processor_config or cfg.base_model_config or cfg.base_model
+        )
+        model_config = model_config.text_config
+
+    cfg.model_config_type = model_config.model_type
+
     # figure out if the model is llama
     cfg.is_llama_derived_model = (
-        (hasattr(model_config, "model_type") and model_config.model_type == "llama")
+        (
+            hasattr(model_config, "model_type")
+            and model_config.model_type == ["llama", "mllama_text_model"]
+        )
         or cfg.is_llama_derived_model
         or "llama" in cfg.base_model.lower()
         or (cfg.type_of_model and "llama" in cfg.type_of_model.lower())

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -188,6 +188,7 @@ class ChatTemplate(str, Enum):
     gemma = "gemma"  # pylint: disable=invalid-name
     cohere = "cohere"  # pylint: disable=invalid-name
     llama3 = "llama3"  # pylint: disable=invalid-name
+    llama3_2_vision = "llama3_2_vision"  # pylint: disable=invalid-name
     phi_3 = "phi_3"  # pylint: disable=invalid-name
     phi_35 = "phi_35"  # pylint: disable=invalid-name
     deepseek_v2 = "deepseek_v2"  # pylint: disable=invalid-name
@@ -228,11 +229,12 @@ class LoraConfig(BaseModel):
     lora_r: Optional[int] = None
     lora_alpha: Optional[int] = None
     lora_fan_in_fan_out: Optional[bool] = None
-    lora_target_modules: Optional[List[str]] = None
+    lora_target_modules: Optional[Union[str, List[str]]] = None
     lora_target_linear: Optional[bool] = None
     lora_modules_to_save: Optional[List[str]] = None
     lora_dropout: Optional[float] = 0.0
     peft_layers_to_transform: Optional[List[int]] = None
+    peft_layers_pattern: Optional[List[str]] = None
     peft: Optional[PeftConfig] = None
     peft_use_dora: Optional[bool] = None
     peft_use_rslora: Optional[bool] = None
@@ -298,6 +300,13 @@ class LoraConfig(BaseModel):
                     raise ValueError("Require cfg.load_in_4bit to be True for qlora")
         return self
 
+    @field_validator("loraplus_lr_embedding")
+    @classmethod
+    def convert_loraplus_lr_embedding(cls, loraplus_lr_embedding):
+        if loraplus_lr_embedding and isinstance(loraplus_lr_embedding, str):
+            loraplus_lr_embedding = float(loraplus_lr_embedding)
+        return loraplus_lr_embedding
+
 
 class ReLoRAConfig(BaseModel):
     """ReLoRA configuration subset"""
@@ -321,6 +330,9 @@ class ModelInputConfig(BaseModel):
     tokenizer_type: Optional[str] = Field(
         default=None, metadata={"help": "transformers tokenizer class"}
     )
+    processor_type: Optional[str] = Field(
+        default=None, metadata={"help": "transformers processor class"}
+    )
     trust_remote_code: Optional[bool] = None
 
     model_kwargs: Optional[Dict[str, Any]] = None
@@ -523,6 +535,7 @@ class AxolotlInputConfig(
     dataset_prepared_path: Optional[str] = None
     dataset_shard_num: Optional[int] = None
     dataset_shard_idx: Optional[int] = None
+    skip_prepare_dataset: Optional[bool] = False
 
     pretraining_dataset: Optional[  # type: ignore
         conlist(Union[PretrainingDataset, SFTDataset], min_length=1)
@@ -990,6 +1003,18 @@ class AxolotlInputConfig(
 
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_mm_prepare(cls, data):
+        if data.get("skip_prepare_dataset"):
+            if data.get("remove_unused_columns") is None:
+                LOG.info(
+                    "setting `remove_unused_columns: false` for skip_prepare_dataset"
+                )
+                data["remove_unused_columns"] = False
+
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def check_warmup(cls, data):
@@ -1017,12 +1042,20 @@ class AxolotlInputConfig(
         return neftune_noise_alpha
 
     @model_validator(mode="after")
-    def check(self):
+    def check_rl_beta(self):
         if self.dpo_beta and not self.rl_beta:
             self.rl_beta = self.dpo_beta
             del self.dpo_beta
         return self
 
+    @model_validator(mode="after")
+    def check_simpo_warmup(self):
+        if self.rl == "simpo" and self.warmup_ratio:
+            raise ValueError(
+                "warmup_ratio is not supported with the simpo trainer. Please use `warmup_steps` instead"
+            )
+        return self
+
     @model_validator(mode="before")
     @classmethod
     def check_frozen(cls, data):
@@ -1037,6 +1070,15 @@ class AxolotlInputConfig(
 
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_peft_layers_pattern(cls, data):
+        if data.get("peft_layers_pattern") and not data.get("peft_layers_to_transform"):
+            raise ValueError(
+                "peft_layers_pattern requires peft_layers_to_transform to be set"
+            )
+        return data
+
     @model_validator(mode="after")
     def check_fft_possible_bad_config(self):
         if (

src/axolotl/utils/data/sft.py

@@ -51,20 +51,31 @@ from axolotl.utils.trainer import (
 LOG = logging.getLogger("axolotl")
 
 
-def prepare_dataset(cfg, tokenizer):
+def prepare_dataset(cfg, tokenizer, processor=None):
     prompters = []
     if not cfg.pretraining_dataset:
         with zero_first(is_local_main_process()):
             if cfg.test_datasets:
                 train_dataset, _, prompters = load_prepare_datasets(
-                    tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH, split="train"
+                    tokenizer,
+                    cfg,
+                    DEFAULT_DATASET_PREPARED_PATH,
+                    split="train",
+                    processor=processor,
                 )
                 _, eval_dataset, _ = load_prepare_datasets(
-                    tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH, split="test"
+                    tokenizer,
+                    cfg,
+                    DEFAULT_DATASET_PREPARED_PATH,
+                    split="test",
+                    processor=processor,
                 )
             else:
                 train_dataset, eval_dataset, prompters = load_prepare_datasets(
-                    tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH
+                    tokenizer,
+                    cfg,
+                    DEFAULT_DATASET_PREPARED_PATH,
+                    processor=processor,
                 )
     else:
         path = cfg.pretraining_dataset
@@ -123,6 +134,7 @@ def load_tokenized_prepared_datasets(
     cfg,
     default_dataset_prepared_path,
     split="train",
+    processor=None,
 ) -> Tuple[DatasetDict, List[Prompter]]:
     cfg_datasets = cfg.test_datasets if split == "test" else cfg.datasets
     tokenizer_name = cfg.tokenizer_config
@@ -180,6 +192,7 @@ def load_tokenized_prepared_datasets(
         cfg.dataset_prepared_path
         and any(prepared_ds_path.glob("*"))
         and not cfg.is_preprocess
+        and not cfg.skip_prepare_dataset
     ):
         LOG.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
         dataset = load_from_disk(str(prepared_ds_path))
@@ -423,12 +436,16 @@ def load_tokenized_prepared_datasets(
                 dataset=ds,
                 d_base_type=d_base_type,
                 d_prompt_style=d_prompt_style,
+                processor=processor,
             )
             datasets.append(dataset_wrapper)
             prompters.append(dataset_prompter)
 
-        LOG.info("merging datasets")
-        dataset = concatenate_datasets(datasets)
+        if len(datasets) == 1:
+            dataset = datasets[0]
+        else:
+            LOG.info("merging datasets")
+            dataset = concatenate_datasets(datasets)
 
         if len(datasets) > 1:
             if cfg.shuffle_merged_datasets:
@@ -437,9 +454,10 @@ def load_tokenized_prepared_datasets(
             else:
                 LOG.debug("NOT shuffling merged datasets")
 
-        dataset, _ = process_datasets_for_packing(cfg, dataset, None)
+        if not cfg.skip_prepare_dataset:
+            dataset, _ = process_datasets_for_packing(cfg, dataset, None)
 
-        if cfg.local_rank == 0:
+        if cfg.local_rank == 0 and not cfg.skip_prepare_dataset:
             LOG.info(f"Saving merged prepared dataset to disk... {prepared_ds_path}")
             dataset.save_to_disk(str(prepared_ds_path))
             if cfg.push_dataset_to_hub:
@@ -478,9 +496,14 @@ def load_prepare_datasets(
     cfg,
     default_dataset_prepared_path,
     split="train",
+    processor=None,
 ) -> Tuple[Dataset, Dataset, List[Prompter]]:
     dataset, prompters = load_tokenized_prepared_datasets(
-        tokenizer, cfg, default_dataset_prepared_path, split=split
+        tokenizer,
+        cfg,
+        default_dataset_prepared_path,
+        split=split,
+        processor=processor,
     )
 
     if cfg.dataset_shard_num and cfg.dataset_shard_idx is not None:
@@ -546,6 +569,7 @@ def get_dataset_wrapper(
     d_base_type,
     dataset,
     d_prompt_style=None,
+    processor=None,
 ):
     dataset_wrapper = None
     dataset_prompter = None
@@ -578,7 +602,11 @@ def get_dataset_wrapper(
             dataset,
             **ds_kwargs,
         )
-    elif ds_strategy := load(config_dataset.type, tokenizer, cfg, config_dataset):
+    elif cfg.skip_prepare_dataset:
+        dataset_wrapper = dataset
+    elif ds_strategy := load(
+        config_dataset.type, tokenizer, cfg, config_dataset, processor=processor
+    ):
         dataset_prompter = UnsupportedPrompter()
         dataset_wrapper = TokenizedPromptDataset(
             ds_strategy,

src/axolotl/utils/models.py

@@ -28,12 +28,17 @@ from transformers import (  # noqa: F401
     AddedToken,
     AutoConfig,
     AutoModelForCausalLM,
+    AutoModelForVision2Seq,
+    AutoProcessor,
     AutoTokenizer,
     AwqConfig,
     BitsAndBytesConfig,
     GPTQConfig,
+    LlavaForConditionalGeneration,
+    MllamaForConditionalGeneration,
     PreTrainedModel,
     PreTrainedTokenizerBase,
+    ProcessorMixin,
 )
 from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
 
@@ -80,6 +85,9 @@ def get_module_class_from_name(module, name):
 
 
 def check_model_config(cfg: DictDefault, model_config: Union[AutoConfig, DictDefault]):
+    if cfg.is_multimodal:
+        model_config = model_config.text_config
+
     quant_config_exists = (
         hasattr(model_config, "quantization_config")
         and model_config.quantization_config
@@ -299,11 +307,31 @@ def load_tokenizer(cfg):
     return tokenizer
 
 
+def load_processor(cfg: DictDefault, tokenizer: PreTrainedTokenizerBase):
+    processor_kwargs: Dict[str, Any] = {}  # do we actually need this?
+
+    processor_cls = AutoProcessor
+    if cfg.processor_type:
+        processor_cls = getattr(transformers, cfg.processor_type)
+
+    processor = processor_cls.from_pretrained(
+        cfg.processor_config,
+        trust_remote_code=cfg.trust_remote_code or False,
+        tokenizer=tokenizer,
+        **processor_kwargs,
+    )
+
+    return processor
+
+
 def load_model(
     cfg: DictDefault,
     tokenizer: PreTrainedTokenizerBase,
+    *,
+    processor: ProcessorMixin = None,  # pylint: disable=unused-argument
     inference: bool = False,
     reference_model: bool = False,
+    **kwargs,  # pylint: disable=unused-argument
 ) -> Tuple[PreTrainedModel, Optional[PeftConfig]]:
     """
     Load a model for a given configuration and tokenizer.
@@ -319,12 +347,23 @@ def load_model(
     plugin_manager = PluginManager.get_instance()
     plugin_manager.pre_model_load(cfg)
 
+    if cfg.is_multimodal:
+        text_model_config = model_config.text_config
+    else:
+        text_model_config = model_config
+
     # TODO refactor as a kwarg
     load_in_8bit = cfg.load_in_8bit
 
     if cfg.gradient_checkpointing == "unsloth":
         transformers.modeling_utils.checkpoint = hf_grad_checkpoint_unsloth_wrapper
 
+    if hasattr(model_config, "model_type") and model_config.model_type == "mllama":
+        if cfg.flash_attention:
+            from axolotl.monkeypatch.attention.mllama import patch_mllama
+
+            patch_mllama()
+
     if hasattr(model_config, "model_type") and model_config.model_type == "btlm":
         if cfg.flash_attention:
             from axolotl.monkeypatch.btlm_attn_hijack_flash import (
@@ -461,6 +500,19 @@ def load_model(
     max_memory = cfg.max_memory
     device_map = cfg.device_map
 
+    AutoModelLoader = AutoModelForCausalLM  # pylint: disable=invalid-name
+    if cfg.is_multimodal:
+        if model_config.model_type == "llava":
+            AutoModelLoader = (  # pylint: disable=invalid-name
+                LlavaForConditionalGeneration
+            )
+        elif model_config.model_type == "mllama":
+            AutoModelLoader = (  # pylint: disable=invalid-name
+                MllamaForConditionalGeneration
+            )
+        else:
+            AutoModelLoader = AutoModelForVision2Seq  # pylint: disable=invalid-name
+
     if cfg.gpu_memory_limit:
         gpu_memory_limit = (
             str(cfg.gpu_memory_limit) + "GiB"
@@ -478,7 +530,7 @@ def load_model(
         from accelerate import infer_auto_device_map
 
         with init_empty_weights():
-            model_canvas = AutoModelForCausalLM.from_config(
+            model_canvas = AutoModelLoader.from_config(
                 model_config, trust_remote_code=cfg.trust_remote_code or False
             )
         model_canvas.tie_weights()
@@ -633,6 +685,8 @@ def load_model(
             quantization_config = (
                 quantization_config or model_kwargs["quantization_config"]
             )
+            if cfg.is_multimodal:
+                model_config.text_config = text_model_config
             model = load_sharded_model_quant(
                 base_model,
                 model_config,
@@ -651,7 +705,9 @@ def load_model(
                 if "device_map" in model_kwargs:
                     del model_kwargs["device_map"]
 
-            model = AutoModelForCausalLM.from_pretrained(
+            if cfg.is_multimodal:
+                model_config.text_config = text_model_config
+            model = AutoModelLoader.from_pretrained(
                 base_model,
                 config=model_config,
                 **model_kwargs,
@@ -690,13 +746,17 @@ def load_model(
             and not cfg.trust_remote_code
         ):
             if cfg.gptq:
-                model = AutoModelForCausalLM.from_pretrained(
+                if cfg.is_multimodal:
+                    model_config.text_config = text_model_config
+                model = AutoModelLoader.from_pretrained(
                     base_model,
                     config=model_config,
                     trust_remote_code=cfg.trust_remote_code or False,
                     **model_kwargs,
                 )
             else:
+                if cfg.is_multimodal:
+                    model_config.text_config = text_model_config
                 model = getattr(transformers, model_type).from_pretrained(
                     base_model,
                     config=model_config,
@@ -707,21 +767,23 @@ def load_model(
             # Shouldn't be a problem most of the time. will obviously error if the model doesn't support this
             # when training starts
             if (
-                hasattr(model_config, "max_seq_len")
-                and model_config.max_seq_len
+                hasattr(text_model_config, "max_seq_len")
+                and text_model_config.max_seq_len
                 and cfg.sequence_len > model_config.max_seq_len
             ):
-                model_config.max_seq_len = cfg.sequence_len
+                text_model_config.max_seq_len = cfg.sequence_len
                 LOG.warning(f"increasing context length to {cfg.sequence_len}")
             elif (
-                hasattr(model_config, "max_sequence_length")
-                and model_config.max_sequence_length
-                and cfg.sequence_len > model_config.max_sequence_length
+                hasattr(text_model_config, "max_sequence_length")
+                and text_model_config.max_sequence_length
+                and cfg.sequence_len > text_model_config.max_sequence_length
             ):
-                model_config.max_sequence_length = cfg.sequence_len
+                text_model_config.max_sequence_length = cfg.sequence_len
                 LOG.warning(f"increasing context length to {cfg.sequence_len}")
             if cfg.gptq:
-                model = AutoModelForCausalLM.from_pretrained(
+                if cfg.is_multimodal:
+                    model_config.text_config = text_model_config
+                model = AutoModelLoader.from_pretrained(
                     base_model,
                     config=model_config,
                     trust_remote_code=cfg.trust_remote_code or False,
@@ -734,7 +796,9 @@ def load_model(
                     if "device_map" in model_kwargs:
                         del model_kwargs["device_map"]
 
-                model = AutoModelForCausalLM.from_pretrained(
+                if cfg.is_multimodal:
+                    model_config.text_config = text_model_config
+                model = AutoModelLoader.from_pretrained(
                     base_model,
                     config=model_config,
                     trust_remote_code=cfg.trust_remote_code or False,
@@ -1016,12 +1080,17 @@ def load_lora(model, cfg, inference=False, config_only=False):
 
     from peft import LoraConfig, get_peft_model
 
-    lora_target_modules = list(cfg.lora_target_modules or [])
+    lora_target_modules = cfg.lora_target_modules or []
 
     if cfg.lora_target_linear:
         linear_names = find_all_linear_names(model)
         LOG.info(f"found linear modules: {repr(sorted(linear_names))}")
-        lora_target_modules = list(set(lora_target_modules + linear_names))
+        lora_target_modules_as_list = (
+            lora_target_modules
+            if isinstance(lora_target_modules, list)
+            else [lora_target_modules]
+        )
+        lora_target_modules = list(set(lora_target_modules_as_list + linear_names))
 
     lora_config_kwargs = {}
     loftq_bits = cfg.peft and cfg.peft.loftq_config and cfg.peft.loftq_config.loftq_bits
@@ -1040,11 +1109,13 @@ def load_lora(model, cfg, inference=False, config_only=False):
         lora_alpha=cfg.lora_alpha,
         target_modules=lora_target_modules,
         layers_to_transform=cfg.peft_layers_to_transform,
+        layers_pattern=cfg.peft_layers_pattern,
         lora_dropout=cfg.lora_dropout,
         fan_in_fan_out=cfg.lora_fan_in_fan_out,
         modules_to_save=cfg.lora_modules_to_save if cfg.lora_modules_to_save else None,
         bias="none",
-        task_type="CAUSAL_LM",
+        # task_type="CAUSAL_LM",
+        task_type="CONDITIONAL_GENERATION" if cfg.is_multimodal else "CAUSAL_LM",
         **lora_config_kwargs,
     )
 

src/axolotl/utils/trainer.py

@@ -306,7 +306,7 @@ def process_pretraining_datasets_for_packing(
 
 
 def calculate_total_num_steps(cfg, train_dataset, update=True):
-    if not cfg.total_num_tokens:
+    if not cfg.total_num_tokens and not cfg.skip_prepare_dataset:
         total_num_tokens = np.sum(
             train_dataset.data.column("input_ids")
             .to_pandas()
@@ -319,7 +319,11 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
 
     skip_estimates = cfg.model_config_type == "mamba"
 
-    if not skip_estimates and not cfg.total_supervised_tokens:
+    if (
+        not skip_estimates
+        and not cfg.total_supervised_tokens
+        and not cfg.skip_prepare_dataset
+    ):
         total_supervised_tokens = (
             train_dataset.data.column("labels")
             .to_pandas()
@@ -478,13 +482,15 @@ def prepare_opinionated_env(cfg):
         os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
 
-def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps):
+def setup_trainer(
+    cfg, train_dataset, eval_dataset, model, tokenizer, processor, total_num_steps
+):
     if cfg.rl in ["dpo", "ipo", "orpo", "kto", "simpo"]:
-        trainer_builder = HFRLTrainerBuilder(cfg, model[0], tokenizer)
+        trainer_builder = HFRLTrainerBuilder(cfg, model[0], tokenizer, processor)
         trainer_builder.model_ref = model[1]
         trainer_builder.peft_config = model[2]
     else:
-        trainer_builder = HFCausalTrainerBuilder(cfg, model[0], tokenizer)
+        trainer_builder = HFCausalTrainerBuilder(cfg, model[0], tokenizer, processor)
 
     trainer_builder.train_dataset = train_dataset
     trainer_builder.eval_dataset = eval_dataset

tests/prompt_strategies/test_chat_templates.py

@@ -73,7 +73,7 @@ class TestAssistantChatTemplateLlama3:
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
                 llama3_tokenizer,
-                chat_templates("llama3"),
+                chat_template=chat_templates("llama3"),
                 message_field_role="role",
                 message_field_content="content",
                 roles={
@@ -113,7 +113,7 @@ class TestAssistantChatTemplateLlama3:
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
                 phi35_tokenizer,
-                chat_templates("phi_35"),
+                chat_template=chat_templates("phi_35"),
                 message_field_role="role",
                 message_field_content="content",
                 roles={
@@ -171,7 +171,7 @@ class TestAssistantChatTemplateLlama3:
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
                 llama3_tokenizer,
-                chat_templates("llama3"),
+                chat_template=chat_templates("llama3"),
                 message_field_role="role",
                 message_field_content="content",
                 message_field_training="training",
@@ -227,8 +227,11 @@ class TestSharegptChatTemplateLlama3:
 
     def test_llama3_assistant(self, llama3_tokenizer, sharegpt_dataset):
         LOG.info("Testing ShareGPT style datasets with llama-3 assistant prompts")
+        # pylint: disable=duplicate-code
         strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
+            ChatTemplatePrompter(
+                llama3_tokenizer, chat_template=chat_templates("llama3")
+            ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
             train_on_eos="none",
@@ -277,8 +280,11 @@ class TestSharegptChatTemplateLlama3:
 
     def test_llama3_human(self, llama3_tokenizer, sharegpt_dataset):
         LOG.info("Testing ShareGPT style datasets with llama-3 human prompts")
+        # pylint: disable=duplicate-code
         strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
+            ChatTemplatePrompter(
+                llama3_tokenizer, chat_template=chat_templates("llama3")
+            ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
             train_on_eos="none",
@@ -327,8 +333,11 @@ class TestSharegptChatTemplateLlama3:
 
     def test_llama3_system_human(self, llama3_tokenizer, basic_dataset):
         LOG.info("Testing ShareGPT style datasets with llama-3 system/human prompts")
+        # pylint: disable=duplicate-code
         strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
+            ChatTemplatePrompter(
+                llama3_tokenizer, chat_template=chat_templates("llama3")
+            ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
             train_on_eos="none",

tests/prompt_strategies/test_chat_templates_advanced.py

@@ -34,7 +34,9 @@ class TestChatTemplateConfigurations:
     def test_train_on_inputs_true(self, llama3_tokenizer, basic_dataset):
         LOG.info("Testing with train_on_inputs=True")
         strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
+            ChatTemplatePrompter(
+                llama3_tokenizer, chat_template=chat_templates("llama3")
+            ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=True,
             sequence_len=512,
@@ -77,7 +79,9 @@ class TestChatTemplateConfigurations:
     def test_train_on_inputs_false(self, llama3_tokenizer, basic_dataset):
         LOG.info("Testing with train_on_inputs=False")
         strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
+            ChatTemplatePrompter(
+                llama3_tokenizer, chat_template=chat_templates("llama3")
+            ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
             sequence_len=512,
@@ -118,7 +122,9 @@ class TestChatTemplateConfigurations:
     def test_roles_to_train_assistant_only(self, llama3_tokenizer, basic_dataset):
         LOG.info("Testing roles_to_train with assistant only")
         strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
+            ChatTemplatePrompter(
+                llama3_tokenizer, chat_template=chat_templates("llama3")
+            ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
             sequence_len=512,
@@ -144,7 +150,9 @@ class TestChatTemplateConfigurations:
     def test_roles_to_train_all(self, llama3_tokenizer, basic_dataset):
         LOG.info("Testing roles_to_train with all roles")
         strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
+            ChatTemplatePrompter(
+                llama3_tokenizer, chat_template=chat_templates("llama3")
+            ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=True,
             sequence_len=512,
@@ -175,7 +183,9 @@ class TestChatTemplateConfigurations:
     def test_empty_roles_to_train(self, llama3_tokenizer, basic_dataset):
         LOG.info("Testing with empty roles_to_train")
         strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
+            ChatTemplatePrompter(
+                llama3_tokenizer, chat_template=chat_templates("llama3")
+            ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
             sequence_len=512,
@@ -194,7 +204,9 @@ class TestChatTemplateConfigurations:
     def test_train_on_eos_all(self, llama3_tokenizer, basic_dataset):
         LOG.info("Testing with train_on_eos='all'")
         strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
+            ChatTemplatePrompter(
+                llama3_tokenizer, chat_template=chat_templates("llama3")
+            ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
             sequence_len=512,
@@ -219,7 +231,9 @@ class TestChatTemplateConfigurations:
     def test_train_on_eos_turn(self, llama3_tokenizer, basic_dataset):
         LOG.info("Testing with train_on_eos='turn'")
         strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
+            ChatTemplatePrompter(
+                llama3_tokenizer, chat_template=chat_templates("llama3")
+            ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
             sequence_len=512,
@@ -267,7 +281,9 @@ class TestChatTemplateConfigurations:
     def test_train_on_eos_last(self, llama3_tokenizer, basic_dataset):
         LOG.info("Testing with train_on_eos='last'")
         strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
+            ChatTemplatePrompter(
+                llama3_tokenizer, chat_template=chat_templates("llama3")
+            ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
             sequence_len=512,
@@ -298,7 +314,9 @@ class TestChatTemplateConfigurations:
     def test_train_on_eos_none(self, llama3_tokenizer, basic_dataset):
         LOG.info("Testing with train_on_eos='none'")
         strategy = ChatTemplateStrategy(
-            ChatTemplatePrompter(llama3_tokenizer, chat_templates("llama3")),
+            ChatTemplatePrompter(
+                llama3_tokenizer, chat_template=chat_templates("llama3")
+            ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
             sequence_len=512,
@@ -324,7 +342,9 @@ class TestChatTemplateConfigurations:
         LOG.info("Testing with drop_system_message=True")
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
-                llama3_tokenizer, chat_templates("llama3"), drop_system_message=True
+                llama3_tokenizer,
+                chat_template=chat_templates("llama3"),
+                drop_system_message=True,
             ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
@@ -350,7 +370,9 @@ class TestChatTemplateConfigurations:
         }
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
-                llama3_tokenizer, chat_templates("llama3"), roles=custom_roles
+                llama3_tokenizer,
+                chat_template=chat_templates("llama3"),
+                roles=custom_roles,
             ),
             tokenizer=llama3_tokenizer,
             train_on_inputs=False,
@@ -402,7 +424,7 @@ class TestChatTemplateConfigurations:
         strategy = ChatTemplateStrategy(
             ChatTemplatePrompter(
                 llama3_tokenizer,
-                chat_templates("llama3"),
+                chat_template=chat_templates("llama3"),
                 message_field_training="train",
                 message_field_training_detail="train_detail",
             ),