Add support for the Training Method for finetuning, and for Direct-Preference Optimization (DPO)

src/together/cli/api/finetune.py

@@ -110,6 +110,18 @@ def fine_tuning(ctx: click.Context) -> None:
     default="all-linear",
     help="Trainable modules for LoRA adapters. For example, 'all-linear', 'q_proj,v_proj'",
 )
+@click.option(
+    "--training-method",
+    type=click.Choice(["sft", "dpo"]),
+    default="sft",
+    help="Training method to use. Options: sft (supervised fine-tuning), dpo (Direct Preference Optimization)",
+)
+@click.option(
+    "--dpo-beta",
+    type=float,
+    default=0.1,
+    help="Beta parameter for DPO training (only used when '--training-method' is 'dpo')",
+)
 @click.option(
     "--suffix", type=str, default=None, help="Suffix for the fine-tuned model name"
 )
@@ -166,6 +178,8 @@ def create(
     wandb_name: str,
     confirm: bool,
     train_on_inputs: bool | Literal["auto"],
+    training_method: str,
+    dpo_beta: float,
     from_checkpoint: str,
 ) -> None:
     """Start fine-tuning"""
@@ -195,6 +209,8 @@ def create(
         wandb_project_name=wandb_project_name,
         wandb_name=wandb_name,
         train_on_inputs=train_on_inputs,
+        training_method=training_method,
+        dpo_beta=dpo_beta,
         from_checkpoint=from_checkpoint,
     )
 

src/together/constants.py

@@ -39,12 +39,18 @@ class DatasetFormat(enum.Enum):
     GENERAL = "general"
     CONVERSATION = "conversation"
     INSTRUCTION = "instruction"
+    PREFERENCE_OPENAI = "preference_openai"
 
 
 JSONL_REQUIRED_COLUMNS_MAP = {
     DatasetFormat.GENERAL: ["text"],
     DatasetFormat.CONVERSATION: ["messages"],
     DatasetFormat.INSTRUCTION: ["prompt", "completion"],
+    DatasetFormat.PREFERENCE_OPENAI: [
+        "input",
+        "preferred_output",
+        "non_preferred_output",
+    ],
 }
 REQUIRED_COLUMNS_MESSAGE = ["role", "content"]
 POSSIBLE_ROLES_CONVERSATION = ["system", "user", "assistant"]

src/together/resources/finetune.py

@@ -23,6 +23,8 @@
     TrainingType,
     FinetuneLRScheduler,
     FinetuneLinearLRSchedulerArgs,
+    TrainingMethodDPO,
+    TrainingMethodSFT,
     FinetuneCheckpoint,
 )
 from together.types.finetune import (
@@ -39,6 +41,12 @@
 _FT_JOB_WITH_STEP_REGEX = r"^ft-[\dabcdef-]+:\d+$"
 
 
+AVAILABLE_TRAINING_METHODS = {
+    TrainingMethodSFT().method,
+    TrainingMethodDPO().method,
+}
+
+
 def createFinetuneRequest(
     model_limits: FinetuneTrainingLimits,
     training_file: str,
@@ -64,8 +72,11 @@ def createFinetuneRequest(
     wandb_project_name: str | None = None,
     wandb_name: str | None = None,
     train_on_inputs: bool | Literal["auto"] = "auto",
+    training_method: str = "sft",
+    dpo_beta: float | None = None,
     from_checkpoint: str | None = None,
 ) -> FinetuneRequest:
+
     if batch_size == "max":
         log_warn_once(
             "Starting from together>=1.3.0, "
@@ -113,11 +124,20 @@ def createFinetuneRequest(
     if weight_decay is not None and (weight_decay < 0):
         raise ValueError("Weight decay should be non-negative")
 
+    if training_method not in AVAILABLE_TRAINING_METHODS:
+        raise ValueError(
+            f"training_method must be one of {', '.join(AVAILABLE_TRAINING_METHODS)}"
+        )
+
     lrScheduler = FinetuneLRScheduler(
         lr_scheduler_type="linear",
         lr_scheduler_args=FinetuneLinearLRSchedulerArgs(min_lr_ratio=min_lr_ratio),
     )
 
+    training_method_cls: TrainingMethodSFT | TrainingMethodDPO = TrainingMethodSFT()
+    if training_method == "dpo":
+        training_method_cls = TrainingMethodDPO(dpo_beta=dpo_beta)
+
     finetune_request = FinetuneRequest(
         model=model,
         training_file=training_file,
@@ -138,6 +158,7 @@ def createFinetuneRequest(
         wandb_project_name=wandb_project_name,
         wandb_name=wandb_name,
         train_on_inputs=train_on_inputs,
+        training_method=training_method_cls,
         from_checkpoint=from_checkpoint,
     )
 
@@ -240,6 +261,8 @@ def create(
         verbose: bool = False,
         model_limits: FinetuneTrainingLimits | None = None,
         train_on_inputs: bool | Literal["auto"] = "auto",
+        training_method: str = "sft",
+        dpo_beta: float | None = None,
         from_checkpoint: str | None = None,
     ) -> FinetuneResponse:
         """
@@ -286,6 +309,9 @@ def create(
                 For datasets with the "messages" field (conversational format) or "prompt" and "completion" fields
                 (Instruction format), inputs will be masked.
                 Defaults to "auto".
+            training_method (str, optional): Training method. Defaults to "sft".
+                Supported methods: "sft", "dpo".
+            dpo_beta (float, optional): DPO beta parameter. Defaults to None.
             from_checkpoint (str, optional): The checkpoint identifier to continue training from a previous fine-tuning job.
                 The format: {$JOB_ID/$OUTPUT_MODEL_NAME}:{$STEP}.
                 The step value is optional, without it the final checkpoint will be used.
@@ -300,7 +326,6 @@ def create(
 
         if model_limits is None:
             model_limits = self.get_model_limits(model=model)
-
         finetune_request = createFinetuneRequest(
             model_limits=model_limits,
             training_file=training_file,
@@ -326,6 +351,8 @@ def create(
             wandb_project_name=wandb_project_name,
             wandb_name=wandb_name,
             train_on_inputs=train_on_inputs,
+            training_method=training_method,
+            dpo_beta=dpo_beta,
             from_checkpoint=from_checkpoint,
         )
 
@@ -344,7 +371,6 @@ def create(
             ),
             stream=False,
         )
-
         assert isinstance(response, TogetherResponse)
 
         return FinetuneResponse(**response.data)
@@ -608,6 +634,8 @@ async def create(
         verbose: bool = False,
         model_limits: FinetuneTrainingLimits | None = None,
         train_on_inputs: bool | Literal["auto"] = "auto",
+        training_method: str = "sft",
+        dpo_beta: float | None = None,
         from_checkpoint: str | None = None,
     ) -> FinetuneResponse:
         """
@@ -654,6 +682,9 @@ async def create(
                 For datasets with the "messages" field (conversational format) or "prompt" and "completion" fields
                 (Instruction format), inputs will be masked.
                 Defaults to "auto".
+            training_method (str, optional): Training method. Defaults to "sft".
+                Supported methods: "sft", "dpo".
+            dpo_beta (float, optional): DPO beta parameter. Defaults to None.
             from_checkpoint (str, optional): The checkpoint identifier to continue training from a previous fine-tuning job.
                 The format: {$JOB_ID/$OUTPUT_MODEL_NAME}:{$STEP}.
                 The step value is optional, without it the final checkpoint will be used.
@@ -694,6 +725,8 @@ async def create(
             wandb_project_name=wandb_project_name,
             wandb_name=wandb_name,
             train_on_inputs=train_on_inputs,
+            training_method=training_method,
+            dpo_beta=dpo_beta,
             from_checkpoint=from_checkpoint,
         )
 

src/together/types/__init__.py

@@ -31,6 +31,8 @@
     FileType,
 )
 from together.types.finetune import (
+    TrainingMethodDPO,
+    TrainingMethodSFT,
     FinetuneCheckpoint,
     FinetuneDownloadResult,
     FinetuneLinearLRSchedulerArgs,
@@ -81,6 +83,8 @@
     "TrainingType",
     "FullTrainingType",
     "LoRATrainingType",
+    "TrainingMethodDPO",
+    "TrainingMethodSFT",
     "RerankRequest",
     "RerankResponse",
     "FinetuneTrainingLimits",

src/together/types/finetune.py

@@ -135,6 +135,31 @@ class LoRATrainingType(TrainingType):
     type: str = "Lora"
 
 
+class TrainingMethod(BaseModel):
+    """
+    Training method type
+    """
+
+    method: str
+
+
+class TrainingMethodSFT(TrainingMethod):
+    """
+    Training method type for SFT training
+    """
+
+    method: Literal["sft"] = "sft"
+
+
+class TrainingMethodDPO(TrainingMethod):
+    """
+    Training method type for DPO training
+    """
+
+    method: Literal["dpo"] = "dpo"
+    dpo_beta: float | None = None
+
+
 class FinetuneRequest(BaseModel):
     """
     Fine-tune request type
@@ -178,6 +203,10 @@ class FinetuneRequest(BaseModel):
     training_type: FullTrainingType | LoRATrainingType | None = None
     # train on inputs
     train_on_inputs: StrictBool | Literal["auto"] = "auto"
+    # training method
+    training_method: TrainingMethodSFT | TrainingMethodDPO = Field(
+        default_factory=TrainingMethodSFT
+    )
     # from step
     from_checkpoint: str