Add instruction and conversational data support (#211)

* Add format checks

* add tests

* add train on inputs flag

* style

* PR feedback

* style

* more tests

* enhance logic

* enhance logic

* pr feedback part 1

* style and fixed

* pr feedback

* style

* style

* fix typing

* change to strict boolean

* error out on train_on_inputs

* use "auto" directly

* add system message

* version bump

* Update src/together/cli/api/finetune.py

Co-authored-by: Max Ryabinin <mryabinin0@gmail.com>

---------

Co-authored-by: Max Ryabinin <mryabinin0@gmail.com>

Author: artek0chumak

pyproject.toml

@@ -12,7 +12,7 @@ build-backend = "poetry.masonry.api"
 
 [tool.poetry]
 name = "together"
-version = "1.3.3"
+version = "1.3.4"
 authors = [
     "Together AI <support@together.ai>"
 ]

src/together/cli/api/finetune.py

@@ -11,8 +11,13 @@
 from tabulate import tabulate
 
 from together import Together
-from together.cli.api.utils import INT_WITH_MAX
-from together.utils import finetune_price_to_dollars, log_warn, parse_timestamp
+from together.cli.api.utils import BOOL_WITH_AUTO, INT_WITH_MAX
+from together.utils import (
+    finetune_price_to_dollars,
+    log_warn,
+    log_warn_once,
+    parse_timestamp,
+)
 from together.types.finetune import DownloadCheckpointType, FinetuneTrainingLimits
 
 
@@ -93,6 +98,13 @@ def fine_tuning(ctx: click.Context) -> None:
     default=False,
     help="Whether to skip the launch confirmation message",
 )
+@click.option(
+    "--train-on-inputs",
+    type=BOOL_WITH_AUTO,
+    default="auto",
+    help="Whether to mask the user messages in conversational data or prompts in instruction data. "
+    "`auto` will automatically determine whether to mask the inputs based on the data format.",
+)
 def create(
     ctx: click.Context,
     training_file: str,
@@ -112,6 +124,7 @@ def create(
     suffix: str,
     wandb_api_key: str,
     confirm: bool,
+    train_on_inputs: bool | Literal["auto"],
 ) -> None:
     """Start fine-tuning"""
     client: Together = ctx.obj
@@ -133,6 +146,7 @@ def create(
         lora_trainable_modules=lora_trainable_modules,
         suffix=suffix,
         wandb_api_key=wandb_api_key,
+        train_on_inputs=train_on_inputs,
     )
 
     model_limits: FinetuneTrainingLimits = client.fine_tuning.get_model_limits(
@@ -150,6 +164,10 @@ def create(
             "batch_size": model_limits.lora_training.max_batch_size,
             "learning_rate": 1e-3,
         }
+        log_warn_once(
+            f"The default LoRA rank for {model} has been changed to {default_values['lora_r']} as the max available.\n"
+            f"Also, the default learning rate for LoRA fine-tuning has been changed to {default_values['learning_rate']}."
+        )
         for arg in default_values:
             arg_source = ctx.get_parameter_source("arg")  # type: ignore[attr-defined]
             if arg_source == ParameterSource.DEFAULT:
@@ -186,22 +204,7 @@ def create(
 
     if confirm or click.confirm(_CONFIRMATION_MESSAGE, default=True, show_default=True):
         response = client.fine_tuning.create(
-            training_file=training_file,
-            model=model,
-            n_epochs=n_epochs,
-            validation_file=validation_file,
-            n_evals=n_evals,
-            n_checkpoints=n_checkpoints,
-            batch_size=batch_size,
-            learning_rate=learning_rate,
-            warmup_ratio=warmup_ratio,
-            lora=lora,
-            lora_r=lora_r,
-            lora_dropout=lora_dropout,
-            lora_alpha=lora_alpha,
-            lora_trainable_modules=lora_trainable_modules,
-            suffix=suffix,
-            wandb_api_key=wandb_api_key,
+            **training_args,
             verbose=True,
         )
 

src/together/cli/api/utils.py

@@ -27,4 +27,25 @@ def convert(
             )
 
 
+class BooleanWithAutoParamType(click.ParamType):
+    name = "boolean_or_auto"
+
+    def convert(
+        self, value: str, param: click.Parameter | None, ctx: click.Context | None
+    ) -> bool | Literal["auto"] | None:
+        if value == "auto":
+            return "auto"
+        try:
+            return bool(value)
+        except ValueError:
+            self.fail(
+                _("{value!r} is not a valid {type}.").format(
+                    value=value, type=self.name
+                ),
+                param,
+                ctx,
+            )
+
+
 INT_WITH_MAX = AutoIntParamType()
+BOOL_WITH_AUTO = BooleanWithAutoParamType()

src/together/constants.py

@@ -1,3 +1,5 @@
+import enum
+
 # Session constants
 TIMEOUT_SECS = 600
 MAX_SESSION_LIFETIME_SECS = 180
@@ -29,3 +31,20 @@
 
 # expected columns for Parquet files
 PARQUET_EXPECTED_COLUMNS = ["input_ids", "attention_mask", "labels"]
+
+
+class DatasetFormat(enum.Enum):
+    """Dataset format enum."""
+
+    GENERAL = "general"
+    CONVERSATION = "conversation"
+    INSTRUCTION = "instruction"
+
+
+JSONL_REQUIRED_COLUMNS_MAP = {
+    DatasetFormat.GENERAL: ["text"],
+    DatasetFormat.CONVERSATION: ["messages"],
+    DatasetFormat.INSTRUCTION: ["prompt", "completion"],
+}
+REQUIRED_COLUMNS_MESSAGE = ["role", "content"]
+POSSIBLE_ROLES_CONVERSATION = ["system", "user", "assistant"]

src/together/resources/finetune.py

@@ -43,6 +43,7 @@ def createFinetuneRequest(
     lora_trainable_modules: str | None = "all-linear",
     suffix: str | None = None,
     wandb_api_key: str | None = None,
+    train_on_inputs: bool | Literal["auto"] = "auto",
 ) -> FinetuneRequest:
     if batch_size == "max":
         log_warn_once(
@@ -95,6 +96,7 @@ def createFinetuneRequest(
         training_type=training_type,
         suffix=suffix,
         wandb_key=wandb_api_key,
+        train_on_inputs=train_on_inputs,
     )
 
     return finetune_request
@@ -125,6 +127,7 @@ def create(
         wandb_api_key: str | None = None,
         verbose: bool = False,
         model_limits: FinetuneTrainingLimits | None = None,
+        train_on_inputs: bool | Literal["auto"] = "auto",
     ) -> FinetuneResponse:
         """
         Method to initiate a fine-tuning job
@@ -137,7 +140,7 @@ def create(
             n_evals (int, optional): Number of evaluation loops to run. Defaults to 0.
             n_checkpoints (int, optional): Number of checkpoints to save during fine-tuning.
                 Defaults to 1.
-            batch_size (int, optional): Batch size for fine-tuning. Defaults to max.
+            batch_size (int or "max"): Batch size for fine-tuning. Defaults to max.
             learning_rate (float, optional): Learning rate multiplier to use for training
                 Defaults to 0.00001.
             warmup_ratio (float, optional): Warmup ratio for learning rate scheduler.
@@ -154,6 +157,12 @@ def create(
                 Defaults to False.
             model_limits (FinetuneTrainingLimits, optional): Limits for the hyperparameters the model in Fine-tuning.
                 Defaults to None.
+            train_on_inputs (bool or "auto"): Whether to mask the user messages in conversational data or prompts in instruction data.
+                "auto" will automatically determine whether to mask the inputs based on the data format.
+                For datasets with the "text" field (general format), inputs will not be masked.
+                For datasets with the "messages" field (conversational format) or "prompt" and "completion" fields
+                (Instruction format), inputs will be masked.
+                Defaults to "auto".
 
         Returns:
             FinetuneResponse: Object containing information about fine-tuning job.
@@ -184,6 +193,7 @@ def create(
             lora_trainable_modules=lora_trainable_modules,
             suffix=suffix,
             wandb_api_key=wandb_api_key,
+            train_on_inputs=train_on_inputs,
         )
 
         if verbose:
@@ -436,6 +446,7 @@ async def create(
         wandb_api_key: str | None = None,
         verbose: bool = False,
         model_limits: FinetuneTrainingLimits | None = None,
+        train_on_inputs: bool | Literal["auto"] = "auto",
     ) -> FinetuneResponse:
         """
         Async method to initiate a fine-tuning job
@@ -465,6 +476,12 @@ async def create(
                 Defaults to False.
             model_limits (FinetuneTrainingLimits, optional): Limits for the hyperparameters the model in Fine-tuning.
                 Defaults to None.
+            train_on_inputs (bool or "auto"): Whether to mask the user messages in conversational data or prompts in instruction data.
+                "auto" will automatically determine whether to mask the inputs based on the data format.
+                For datasets with the "text" field (general format), inputs will not be masked.
+                For datasets with the "messages" field (conversational format) or "prompt" and "completion" fields
+                (Instruction format), inputs will be masked.
+                Defaults to "auto".
 
         Returns:
             FinetuneResponse: Object containing information about fine-tuning job.
@@ -495,6 +512,7 @@ async def create(
             lora_trainable_modules=lora_trainable_modules,
             suffix=suffix,
             wandb_api_key=wandb_api_key,
+            train_on_inputs=train_on_inputs,
         )
 
         if verbose:

src/together/types/finetune.py

@@ -3,7 +3,7 @@
 from enum import Enum
 from typing import List, Literal
 
-from pydantic import Field, validator, field_validator
+from pydantic import StrictBool, Field, validator, field_validator
 
 from together.types.abstract import BaseModel
 from together.types.common import (
@@ -163,6 +163,7 @@ class FinetuneRequest(BaseModel):
     # weights & biases api key
     wandb_key: str | None = None
     training_type: FullTrainingType | LoRATrainingType | None = None
+    train_on_inputs: StrictBool | Literal["auto"] = "auto"
 
 
 class FinetuneResponse(BaseModel):
@@ -230,6 +231,7 @@ class FinetuneResponse(BaseModel):
     # training file metadata
     training_file_num_lines: int | None = Field(None, alias="TrainingFileNumLines")
     training_file_size: int | None = Field(None, alias="TrainingFileSize")
+    train_on_inputs: StrictBool | Literal["auto"] | None = "auto"
 
     @field_validator("training_type")
     @classmethod