installing axolotl prior to quartodoc build

.github/workflows/docs.yml

@@ -20,9 +20,12 @@ jobs:
           uses: actions/setup-python@v5
           with:
             python-version: '3.11'
-        - name: install dependencies
+        - name: Install dependencies
           run: |
-            python3 -m pip install jupyter
+            python3 -m pip install jupyter quartodoc
+            python3 -m pip install -e .
+        - name: Build autodoc
+          run: quartodoc build
         - name: Publish to GitHub Pages (and render)
           uses: quarto-dev/quarto-actions/publish@v2
           with:

README.md

@@ -97,7 +97,7 @@ That's it! Check out our [Getting Started Guide](https://axolotl-ai-cloud.github
 - [Multi-GPU Training](https://axolotl-ai-cloud.github.io/axolotl/docs/multi-gpu.html)
 - [Multi-Node Training](https://axolotl-ai-cloud.github.io/axolotl/docs/multi-node.html)
 - [Multipacking](https://axolotl-ai-cloud.github.io/axolotl/docs/multipack.html)
-- [API Reference](https://axolotl-ai-cloud.github.io/axolotl/api/) - Auto-generated code documentation
+- [API Reference](https://axolotl-ai-cloud.github.io/axolotl/docs/api/) - Auto-generated code documentation
 - [FAQ](https://axolotl-ai-cloud.github.io/axolotl/docs/faq.html) - Frequently asked questions
 
 ## 🤝 Getting Help

_quarto.yml

@@ -124,6 +124,18 @@ quartodoc:
         - utils.data.pretraining
         - utils.data.sft
         - utils.gradient_checkpointing.unsloth
+    - title: Schemas
+      desc: Pydantic data models for Axolotl config
+      contents:
+        - utils.schemas.config
+        - utils.schemas.model
+        - utils.schemas.training
+        - utils.schemas.datasets
+        - utils.schemas.peft
+        - utils.schemas.trl
+        - utils.schemas.integrations
+        - utils.schemas.enums
+        - utils.schemas.utils
     - title: Integrations
       desc: Third-party integrations and extensions
       contents:
@@ -195,12 +207,8 @@ website:
             - docs/inference.qmd
             - docs/cli.qmd
             - docs/config.qmd
-
-        - section: "Reference"
-          contents:
-            - docs/config.qmd
             - text: "API Reference"
-              href: docs/api/index.qmd
+              href: docs/api
 
         - section: "Dataset Formats"
           contents: docs/dataset-formats/*

requirements-dev.txt

@@ -2,3 +2,5 @@ pre-commit
 black
 mypy
 types-requests
+quartodoc
+jupyter

src/axolotl/cli/main.py

@@ -25,7 +25,7 @@ from axolotl.cli.utils import (
 )
 from axolotl.integrations.lm_eval.cli import lm_eval
 from axolotl.utils import set_pytorch_cuda_alloc_conf
-from axolotl.utils.config.models.input.v0_4_1 import AxolotlInputConfig
+from axolotl.utils.schemas.config import AxolotlInputConfig
 
 
 @click.group()

src/axolotl/core/trainer_builder.py

@@ -13,9 +13,7 @@
 # limitations under the License.
 
 # pylint: disable=too-many-lines
-"""
+"""Builder for the training args and trainer"""
-Builder for the training args and trainer
-"""
 
 import abc
 import importlib
@@ -85,8 +83,8 @@ from axolotl.utils.collators import (
     V2BatchSamplerDataCollatorForSeq2Seq,
 )
 from axolotl.utils.collators.mm_chat import MultiModalChatDataCollator
-from axolotl.utils.config.models.input.v0_4_1 import CustomSupportedOptimizers
 from axolotl.utils.models import ensure_dtype
+from axolotl.utils.schemas.enums import CustomSupportedOptimizers
 
 try:
     import torch._dynamo  # pylint: disable=ungrouped-imports

src/axolotl/core/trainers/grpo/__init__.py

@@ -9,7 +9,7 @@ import logging
 from trl.trainer.grpo_trainer import RewardFunc
 
 from axolotl.core.trainers.grpo.trainer import AxolotlGRPOTrainer
-from axolotl.utils.config.models.input.v0_4_1.trl import TRLConfig
+from axolotl.utils.schemas.trl import TRLConfig
 
 LOG = logging.getLogger("axolotl")
 

src/axolotl/integrations/config.py

@@ -11,19 +11,17 @@
 # the License.
 
 """
-module to handle merging the plugins' input arguments with the base configurations.
+Module to handle merging the plugins' input arguments with the base configurations.
 
-this was moved here to prevent circular imports
+This was moved here to prevent circular imports.
 """
 
 from typing import Any, Dict, List
 
-from axolotl.utils.config.models.input.v0_4_1 import (
+from axolotl.utils.schemas.config import (
     AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
 )
-from axolotl.utils.config.models.input.v0_4_1 import (
+from axolotl.utils.schemas.config import AxolotlInputConfig as AxolotlInputConfigBase
-    AxolotlInputConfig as AxolotlInputConfigBase,
-)
 
 
 def merge_input_args():

src/axolotl/prompt_strategies/chat_template.py

@@ -13,7 +13,7 @@ from axolotl.prompt_strategies.jinja_template_analyzer import JinjaTemplateAnaly
 from axolotl.prompt_tokenizers import PromptTokenizingStrategy
 from axolotl.prompters import IGNORE_TOKEN_ID, Prompter
 from axolotl.utils.chat_templates import get_chat_template_from_config
-from axolotl.utils.config.models.input.v0_4_1 import DatasetConfig
+from axolotl.utils.schemas.datasets import DatasetConfig
 
 # Configure the logger
 LOG = logging.getLogger("axolotl")

src/axolotl/prompt_strategies/dpo/chat_template.py

@@ -3,7 +3,7 @@ DPO prompt strategies for using tokenizer chat templates.
 """
 
 from axolotl.utils.chat_templates import extract_chat_template_args, get_chat_template
-from axolotl.utils.config.models.input.v0_4_1 import handle_legacy_message_fields_logic
+from axolotl.utils.schemas.utils import handle_legacy_message_fields_logic
 
 
 def default(

src/axolotl/utils/callbacks/__init__.py

@@ -33,7 +33,6 @@ from trl.models import unwrap_model_for_generation
 from axolotl.utils import is_comet_available, is_mlflow_available
 from axolotl.utils.bench import log_gpu_memory_usage
 from axolotl.utils.callbacks.perplexity import Perplexity
-from axolotl.utils.config.models.input.v0_4_1 import AxolotlInputConfig
 from axolotl.utils.distributed import (
     barrier,
     broadcast_dict,
@@ -43,6 +42,7 @@ from axolotl.utils.distributed import (
     is_main_process,
     zero_first,
 )
+from axolotl.utils.schemas.config import AxolotlInputConfig
 
 if TYPE_CHECKING:
     from axolotl.core.trainer_builder import AxolotlTrainingArguments

src/axolotl/utils/config/__init__.py

@@ -12,19 +12,13 @@ from transformers.utils.import_utils import is_torch_npu_available
 from axolotl.integrations.base import PluginManager
 from axolotl.integrations.config import merge_input_args
 from axolotl.utils.bench import log_gpu_memory_usage
-from axolotl.utils.config.models.input.v0_4_1 import (
-    AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
-)
-from axolotl.utils.config.models.input.v0_4_1 import (
-    AxolotlInputConfig as AxolotlInputConfigBase,
-)
-from axolotl.utils.config.models.input.v0_4_1 import (
-    DPODataset,
-    KTODataset,
-    SFTDataset,
-)
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.models import load_model_config
+from axolotl.utils.schemas.config import (
+    AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
+)
+from axolotl.utils.schemas.config import AxolotlInputConfig as AxolotlInputConfigBase
+from axolotl.utils.schemas.datasets import DPODataset, KTODataset, SFTDataset
 
 LOG = logging.getLogger("axolotl")
 

src/axolotl/utils/schemas/datasets.py

@@ -0,0 +1,165 @@
+"""Pydantic models for datasets-related configuration"""
+
+from pydantic import BaseModel, model_validator
+
+from axolotl.utils.schemas.enums import ChatTemplate
+from axolotl.utils.schemas.utils import handle_legacy_message_fields_logic
+
+
+class UserDefinedPrompterType(BaseModel):
+    """Structure for user defined prompt types"""
+
+    system_prompt: str | None = None
+    system_format: str | None = None
+    field_system: str | None = None
+    field_instruction: str | None = None
+    field_input: str | None = None
+    field_output: str | None = None
+
+    format: str | None = None
+    no_input_format: str | None = None
+    field: str | None = None
+
+
+class SFTDataset(BaseModel):
+    """SFT configuration subset"""
+
+    path: str | None = None
+    split: str | None = None
+    type: str | UserDefinedPrompterType | None = None
+    input_transform: str | None = None
+    shards: int | None = None
+    shards_idx: int | None = None
+    preprocess_shards: int | None = None
+    conversation: str | None = None
+    # Do not make this too strict or it will break the validator to choose different dataset class
+    chat_template: ChatTemplate | str | None = None
+    chat_template_jinja: str | None = None
+    data_files: str | list[str] | None = None
+    input_format: str | None = None
+    name: str | None = None
+    ds_type: str | None = None
+    train_on_split: str | None = None
+    field: str | None = None
+    field_human: str | None = None
+    field_model: str | None = None
+    field_messages: str | None = None
+    # deprecated, use message_property_mappings
+    message_field_role: str | None = None
+    # deprecated, use message_property_mappings
+    message_field_content: str | None = None
+    message_property_mappings: dict[str, str] | None = None
+    message_field_training: str | None = None
+    message_field_training_detail: str | None = None
+    logprobs_field: str | None = None
+    temperature: float | None = None
+    roles_to_train: list[str] | None = None
+    train_on_eos: str | None = None
+    roles: dict[str, list[str]] | None = None
+    drop_system_message: bool | None = None
+    trust_remote_code: bool | None = False
+    revision: str | None = None
+
+    @model_validator(mode="before")
+    @classmethod
+    def handle_legacy_message_fields(cls, data):
+        """Handle backwards compatibility between legacy message field mapping and new property mapping system."""
+        return handle_legacy_message_fields_logic(data)
+
+    @model_validator(mode="before")
+    @classmethod
+    # pylint: disable=duplicate-code
+    def check_chat_template_config(cls, data):
+        if isinstance(data, BaseModel):
+            data = data.model_dump()
+
+        # Set chat_template to tokenizer_default if not set
+        if data.get("type") == "chat_template" and not data.get("chat_template"):
+            data["chat_template"] = ChatTemplate.tokenizer_default
+
+        # if chat_template is set to jinja, chat_template_jinja is required
+        if data.get("chat_template") == ChatTemplate.jinja and not data.get(
+            "chat_template_jinja"
+        ):
+            raise ValueError(
+                "chat_template_jinja is required when chat_template is set to jinja"
+            )
+
+        # If chat_template_jinja is set, set chat_template to jinja
+        if data.get("chat_template_jinja") and not data.get("chat_template"):
+            data["chat_template"] = ChatTemplate.jinja
+
+        return data
+
+
+class PretrainingDataset(BaseModel):
+    """Pretraining dataset configuration subset"""
+
+    name: str | None = None
+    path: str | None = None
+    split: str | None = "train"
+    text_column: str | None = "text"
+    type: str | None = "pretrain"
+    trust_remote_code: bool | None = False
+    data_files: str | None = None
+    skip: int | None = None
+
+
+class UserDefinedDPOType(BaseModel):
+    """User defined typing for DPO"""
+
+    field_system: str | None = None
+    field_prompt: str | None = None
+    field_chosen: str | None = None
+    field_rejected: str | None = None
+    prompt_format: str | None = None
+    chosen_format: str | None = None
+    rejected_format: str | None = None
+
+
+class DPODataset(BaseModel):
+    """DPO configuration subset"""
+
+    path: str | None = None
+    split: str | None = None
+    type: UserDefinedDPOType | str | None = None
+    data_files: list[str] | None = None
+    revision: str | None = None
+    field_messages: str | None = None
+
+
+class StepwiseSupervisedDataset(BaseModel):
+    """Stepwise supervised dataset configuration subset"""
+
+    path: str | None = None
+    split: str | None = None
+    data_files: list[str] | None = None
+    revision: str | None = None
+    step_separator: str | None = None
+    max_completion_length: int | None = None
+    train_on_last_step_only: bool | None = None
+
+
+class UserDefinedKTOType(BaseModel):
+    """User defined typing for KTO"""
+
+    field_system: str | None = None
+    field_prompt: str | None = None
+    field_completion: str | None = None
+    field_label: bool | None = None
+    prompt_format: str | None = None
+    completion_format: str | None = None
+
+
+class KTODataset(BaseModel):
+    """KTO configuration subset"""
+
+    path: str | None = None
+    split: str | None = None
+    type: UserDefinedKTOType | str | None = None
+    data_files: list[str] | None = None
+    trust_remote_code: bool | None = False
+    revision: str | None = None
+
+
+DatasetConfig = SFTDataset | DPODataset | KTODataset | StepwiseSupervisedDataset

src/axolotl/utils/schemas/deprecated.py

@@ -0,0 +1,68 @@
+"""Pydantic models for deprecated and remapped configuration parameters"""
+
+import logging
+from typing import Any
+
+from pydantic import BaseModel, Field, field_validator
+
+LOG = logging.getLogger(__name__)
+
+
+class DeprecatedParameters(BaseModel):
+    """configurations that are deprecated"""
+
+    max_packed_sequence_len: int | None = None
+    rope_scaling: Any | None = None
+    noisy_embedding_alpha: float | None = None
+    dpo_beta: float | None = None
+    evaluation_strategy: str | None = None
+
+    @field_validator("max_packed_sequence_len")
+    @classmethod
+    def validate_max_packed_sequence_len(cls, max_packed_sequence_len):
+        if max_packed_sequence_len:
+            raise DeprecationWarning("`max_packed_sequence_len` is no longer supported")
+        return max_packed_sequence_len
+
+    @field_validator("rope_scaling")
+    @classmethod
+    def validate_rope_scaling(cls, rope_scaling):
+        if rope_scaling:
+            raise DeprecationWarning(
+                "`rope_scaling` is no longer supported, it should now be be a key under `model_config`"
+            )
+        return rope_scaling
+
+    @field_validator("noisy_embedding_alpha")
+    @classmethod
+    def validate_noisy_embedding_alpha(cls, noisy_embedding_alpha):
+        if noisy_embedding_alpha:
+            LOG.warning("noisy_embedding_alpha is deprecated, use neftune_noise_alpha")
+        return noisy_embedding_alpha
+
+    @field_validator("dpo_beta")
+    @classmethod
+    def validate_dpo_beta(cls, dpo_beta):
+        if dpo_beta is not None:
+            LOG.warning("dpo_beta is deprecated, use rl_beta instead")
+        return dpo_beta
+
+    @field_validator("evaluation_strategy")
+    @classmethod
+    def validate_evaluation_strategy(cls, evaluation_strategy):
+        if evaluation_strategy is not None:
+            LOG.warning("evaluation_strategy is deprecated, use eval_strategy instead")
+        return evaluation_strategy
+
+
+class RemappedParameters(BaseModel):
+    """Parameters that have been remapped to other names"""
+
+    overrides_of_model_config: dict[str, Any] | None = Field(
+        default=None, alias="model_config"
+    )
+    overrides_of_model_kwargs: dict[str, Any] | None = Field(
+        default=None, alias="model_kwargs"
+    )
+    type_of_model: str | None = Field(default=None, alias="model_type")
+    revision_of_model: str | None = Field(default=None, alias="model_revision")

src/axolotl/utils/schemas/enums.py

@@ -0,0 +1,49 @@
+"""Enums for Axolotl input config"""
+
+from enum import Enum
+
+
+class RLType(str, Enum):
+    """RL trainer type configuration subset"""
+
+    dpo = "dpo"  # pylint: disable=invalid-name
+    grpo = "grpo"  # pylint: disable=invalid-name
+    ipo = "ipo"  # pylint: disable=invalid-name
+    orpo = "orpo"  # pylint: disable=invalid-name
+    kto = "kto"  # pylint: disable=invalid-name
+    simpo = "simpo"  # pylint: disable=invalid-name
+
+
+class ChatTemplate(str, Enum):
+    """Chat templates configuration subset"""
+
+    alpaca = "alpaca"  # pylint: disable=invalid-name
+    chatml = "chatml"  # pylint: disable=invalid-name
+    mistral_v1 = "mistral_v1"  # pylint: disable=invalid-name
+    mistral_v2v3 = "mistral_v2v3"  # pylint: disable=invalid-name
+    mistral_v3_tekken = "mistral_v3_tekken"  # pylint: disable=invalid-name
+    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
+    deepseek_v3 = "deepseek_v3"  # pylint: disable=invalid-name
+    jamba = "jamba"  # pylint: disable=invalid-name
+    jinja = "jinja"  # pylint: disable=invalid-name
+    qwen_25 = "qwen_25"  # pylint: disable=invalid-name
+    tokenizer_default = "tokenizer_default"  # pylint: disable=invalid-name
+    exaone = "exaone"  # pylint: disable=invalid-name
+    metharme = "metharme"  # pylint: disable=invalid-name
+
+
+class CustomSupportedOptimizers(str, Enum):
+    """Custom supported optimizers"""
+
+    optimi_adamw = "optimi_adamw"  # pylint: disable=invalid-name
+    ao_adamw_4bit = "ao_adamw_4bit"  # pylint: disable=invalid-name
+    ao_adamw_8bit = "ao_adamw_8bit"  # pylint: disable=invalid-name
+    ao_adamw_fp8 = "ao_adamw_fp8"  # pylint: disable=invalid-name
+    adopt_adamw = "adopt_adamw"  # pylint: disable=invalid-name
+    muon = "muon"  # pylint: disable=invalid-name

src/axolotl/utils/schemas/integrations.py

@@ -0,0 +1,108 @@
+"""Pydantic models for Axolotl integrations"""
+
+import logging
+from typing import Any
+
+from pydantic import BaseModel, Field, model_validator
+
+LOG = logging.getLogger(__name__)
+
+
+class MLFlowConfig(BaseModel):
+    """MLFlow configuration subset"""
+
+    use_mlflow: bool | None = None
+    mlflow_tracking_uri: str | None = None
+    mlflow_experiment_name: str | None = None
+    mlflow_run_name: str | None = None
+    hf_mlflow_log_artifacts: bool | None = None
+
+
+class LISAConfig(BaseModel):
+    """LISA configuration subset"""
+
+    lisa_n_layers: int | None = Field(
+        default=None,
+        json_schema_extra={"description": "the number of activate layers in LISA"},
+    )
+    lisa_step_interval: int | None = Field(
+        default=None,
+        json_schema_extra={"description": "how often to switch layers in LISA"},
+    )
+    lisa_layers_attribute: str | None = Field(
+        default="model.layers",
+        json_schema_extra={"description": "path under the model to access the layers"},
+    )
+
+
+class WandbConfig(BaseModel):
+    """Wandb configuration subset"""
+
+    use_wandb: bool | None = None
+    wandb_name: str | None = None
+    wandb_run_id: str | None = None
+    wandb_mode: str | None = None
+    wandb_project: str | None = None
+    wandb_entity: str | None = None
+    wandb_watch: str | None = None
+    wandb_log_model: str | None = None
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_wandb_run(cls, data):
+        if data.get("wandb_run_id") and not data.get("wandb_name"):
+            data["wandb_name"] = data.get("wandb_run_id")
+
+            LOG.warning(
+                "wandb_run_id sets the ID of the run. If you would like to set the name, please use wandb_name instead."
+            )
+
+        return data
+
+
+class CometConfig(BaseModel):
+    """Comet configuration subset"""
+
+    use_comet: bool | None = None
+    comet_api_key: str | None = None
+    comet_workspace: str | None = None
+    comet_project_name: str | None = None
+    comet_experiment_key: str | None = None
+    comet_mode: str | None = None
+    comet_online: bool | None = None
+    comet_experiment_config: dict[str, Any] | None = None
+
+
+class GradioConfig(BaseModel):
+    """Gradio configuration subset"""
+
+    gradio_title: str | None = None
+    gradio_share: bool | None = None
+    gradio_server_name: str | None = None
+    gradio_server_port: int | None = None
+    gradio_max_new_tokens: int | None = None
+    gradio_temperature: float | None = None
+
+
+class RayConfig(BaseModel):
+    """Ray launcher configuration subset"""
+
+    use_ray: bool = Field(default=False)
+    ray_run_name: str | None = Field(
+        default=None,
+        json_schema_extra={
+            "help": "The training results will be saved at `saves/ray_run_name`."
+        },
+    )
+    ray_num_workers: int = Field(
+        default=1,
+        json_schema_extra={
+            "help": "The number of workers for Ray training. Default is 1 worker."
+        },
+    )
+    resources_per_worker: dict = Field(
+        default_factory=lambda: {"GPU": 1},
+        json_schema_extra={
+            "help": "The resources per worker for Ray training. Default is to use 1 GPU per worker."
+        },
+    )

src/axolotl/utils/schemas/model.py

@@ -0,0 +1,55 @@
+"""Pydantic models for model input / output, etc. configuration"""
+
+import logging
+
+from pydantic import BaseModel, Field, field_validator
+
+LOG = logging.getLogger(__name__)
+
+
+class ModelInputConfig(BaseModel):
+    """Model configuration subset"""
+
+    model_config = {"protected_namespaces": ()}
+
+    base_model: str
+    base_model_config: str | None = None
+    cls_model_config: str | None = None
+    tokenizer_config: str | None = None
+    tokenizer_use_fast: bool | None = None
+    tokenizer_legacy: bool | None = None
+    tokenizer_type: str | None = Field(
+        default=None, json_schema_extra={"description": "transformers tokenizer class"}
+    )
+    processor_type: str | None = Field(
+        default=None, json_schema_extra={"description": "transformers processor class"}
+    )
+    trust_remote_code: bool | None = None
+
+    @field_validator("trust_remote_code")
+    @classmethod
+    def hint_trust_remote_code(cls, trust_remote_code):
+        if trust_remote_code:
+            LOG.warning(
+                "`trust_remote_code` is set to true. Please make sure that you reviewed the remote code/model."
+            )
+        return trust_remote_code
+
+
+class ModelOutputConfig(BaseModel):
+    """model save configuration subset"""
+
+    output_dir: str = Field(default="./model-out")
+    hub_model_id: str | None = None
+    hub_strategy: str | None = None
+    save_safetensors: bool | None = True
+
+
+class SpecialTokensConfig(BaseModel):
+    """Special tokens configuration subset"""
+
+    bos_token: str | None = None
+    eos_token: str | None = None
+    pad_token: str | None = None
+    unk_token: str | None = None
+    additional_special_tokens: list[str] | None = None

src/axolotl/utils/schemas/peft.py

@@ -0,0 +1,132 @@
+"""Pydantic models for PEFT-related configuration"""
+
+from typing import Any
+
+from pydantic import BaseModel, Field, field_validator, model_validator
+
+
+class LoftQConfig(BaseModel):
+    """LoftQ configuration subset"""
+
+    loftq_bits: int = Field(
+        default=4, json_schema_extra={"description": "Quantization bits for LoftQ"}
+    )
+    # loftq_iter: int = Field(default=1, json_schema_extra={"description": "Alternating iterations for LoftQ"})
+
+
+class PeftConfig(BaseModel):
+    """peftq configuration subset"""
+
+    loftq_config: LoftQConfig | None = None
+
+
+class LoraConfig(BaseModel):
+    """Peft / LoRA configuration subset"""
+
+    load_in_8bit: bool | None = Field(default=False)
+    load_in_4bit: bool | None = Field(default=False)
+
+    adapter: str | None = None
+    lora_model_dir: str | None = None
+    lora_r: int | None = None
+    lora_alpha: int | None = None
+    lora_fan_in_fan_out: bool | None = None
+    lora_target_modules: str | list[str] | None = None
+    lora_target_linear: bool | None = None
+    lora_modules_to_save: list[str] | None = None
+    lora_dropout: float | None = 0.0
+    peft_layers_to_transform: list[int] | None = None
+    peft_layers_pattern: list[str] | None = None
+    peft: PeftConfig | None = None
+    peft_use_dora: bool | None = None
+    peft_use_rslora: bool | None = None
+    peft_layer_replication: list[tuple[int, int]] | None = None
+    peft_init_lora_weights: bool | str | None = None
+
+    qlora_sharded_model_loading: bool | None = Field(
+        default=False,
+        json_schema_extra={
+            "description": "load qlora model in sharded format for FSDP using answer.ai technique."
+        },
+    )
+    lora_on_cpu: bool | None = None
+    gptq: bool | None = None
+    bnb_config_kwargs: dict[str, Any] | None = None
+
+    loraplus_lr_ratio: float | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "loraplus learning rate ratio lr_B / lr_A. Recommended value is 2^4."
+        },
+    )
+    loraplus_lr_embedding: float | None = Field(
+        default=1e-6,
+        json_schema_extra={
+            "description": "loraplus learning rate for lora embedding layers."
+        },
+    )
+
+    merge_lora: bool | None = None
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_adapter(cls, data):
+        if (
+            not data.get("adapter")
+            and not data.get("inference")
+            and (data.get("load_in_8bit") or data.get("load_in_4bit"))
+        ):
+            raise ValueError(
+                "load_in_8bit and load_in_4bit are not supported without setting an adapter for training."
+                "If you want to full finetune, please turn off load_in_8bit and load_in_4bit."
+            )
+        return data
+
+    @model_validator(mode="after")
+    def validate_qlora(self):
+        if self.adapter == "qlora":
+            if self.merge_lora:
+                # can't merge qlora if loaded in 8bit or 4bit
+                if self.load_in_8bit:
+                    raise ValueError("Can't merge qlora if loaded in 8bit")
+
+                if self.gptq:
+                    raise ValueError("Can't merge qlora if gptq")
+
+                if self.load_in_4bit:
+                    raise ValueError("Can't merge qlora if loaded in 4bit")
+
+            else:
+                if self.load_in_8bit:
+                    raise ValueError("Can't load qlora in 8bit")
+
+                if self.gptq:
+                    raise ValueError("Can't load qlora if gptq")
+
+                if not self.load_in_4bit:
+                    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
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_lora_dropout(cls, data):
+        if data.get("adapter") is not None and data.get("lora_dropout") is None:
+            data["lora_dropout"] = 0.0
+        return data
+
+
+class ReLoRAConfig(BaseModel):
+    """ReLoRA configuration subset"""
+
+    relora_steps: int | None = None
+    relora_warmup_steps: int | None = None
+    relora_anneal_steps: int | None = None
+    relora_prune_ratio: float | None = None
+    relora_cpu_offload: bool | None = None

src/axolotl/utils/schemas/training.py

@@ -0,0 +1,99 @@
+"""Pydantic models for training hyperparameters"""
+
+import logging
+from typing import Any, Literal
+
+from pydantic import BaseModel, Field, field_validator
+from transformers import SchedulerType
+from transformers.training_args import OptimizerNames
+
+from axolotl.utils.schemas.enums import CustomSupportedOptimizers
+
+LOG = logging.getLogger(__name__)
+
+
+class LrGroup(BaseModel):
+    """Custom learning rate group configuration"""
+
+    name: str
+    modules: list[str]
+    lr: float
+
+
+class HyperparametersConfig(BaseModel):
+    """Training hyperparams configuration subset"""
+
+    gradient_accumulation_steps: int | None = Field(default=1)
+    micro_batch_size: int | None = Field(
+        default=1,
+        json_schema_extra={"description": "per gpu micro batch size for training"},
+    )
+    batch_size: int | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Total batch size, we do not recommended setting this manually"
+        },
+    )
+    eval_batch_size: int | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "per gpu micro batch size for evals, defaults to value of micro_batch_size"
+        },
+    )
+
+    auto_find_batch_size: bool | None = None
+
+    train_on_inputs: bool | None = False
+    group_by_length: bool | None = None
+
+    learning_rate: str | float
+    embedding_lr: float | None = None
+    embedding_lr_scale: float | None = None
+    weight_decay: float | None = 0.0
+    optimizer: (OptimizerNames | CustomSupportedOptimizers) | None = (
+        OptimizerNames.ADAMW_TORCH_FUSED
+    )
+    optim_args: (str | dict[str, Any]) | None = Field(
+        default=None,
+        json_schema_extra={"description": "Optional arguments to supply to optimizer."},
+    )
+    optim_target_modules: (list[str] | Literal["all_linear"]) | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "The target modules to optimize, i.e. the module names that you would like to train."
+        },
+    )
+    torchdistx_path: str | None = None
+    lr_scheduler: (SchedulerType | Literal["one_cycle"] | Literal["rex"]) | None = (
+        SchedulerType.COSINE
+    )
+    lr_scheduler_kwargs: dict[str, Any] | None = None
+    lr_quadratic_warmup: bool | None = None
+    cosine_min_lr_ratio: float | None = None
+    cosine_constant_lr_ratio: float | None = None
+    lr_div_factor: float | None = None
+    lr_groups: list[LrGroup] | None = None
+
+    adam_epsilon: float | None = None
+    adam_beta1: float | None = None
+    adam_beta2: float | None = None
+    max_grad_norm: float | None = None
+    num_epochs: float = Field(default=1.0)
+
+    @field_validator("batch_size")
+    @classmethod
+    def hint_batch_size_set(cls, batch_size):
+        if batch_size:
+            LOG.warning(
+                "%s\n%s",
+                "batch_size is not recommended. Please use gradient_accumulation_steps instead.",
+                "To calculate the equivalent gradient_accumulation_steps, divide batch_size / micro_batch_size / number of gpus.",
+            )
+        return batch_size
+
+    @field_validator("learning_rate")
+    @classmethod
+    def convert_learning_rate(cls, learning_rate):
+        if learning_rate and isinstance(learning_rate, str):
+            learning_rate = float(learning_rate)
+        return learning_rate

src/axolotl/utils/schemas/trl.py

@@ -1,8 +1,4 @@
-"""
+"""Pydantic models for TRL trainer configuration"""
-GRPO specific configuration args
-"""
-
-from typing import Optional
 
 from pydantic import BaseModel, Field
 
@@ -12,11 +8,11 @@ class TRLConfig(BaseModel):
     Input args for TRL.
     """
 
-    beta: Optional[float] = Field(
+    beta: float | None = Field(
         default=None,
         json_schema_extra={"description": "Beta for RL training"},
     )
-    max_completion_length: Optional[int] = Field(
+    max_completion_length: int | None = Field(
         default=None,
         json_schema_extra={
             "description": "Maximum length of the completion for RL training"
@@ -25,50 +21,50 @@ class TRLConfig(BaseModel):
 
     # GRPO specific args
     # Ref: https://github.com/huggingface/trl/blob/e3244d2d096ff1e2e248c931d06d39e165e20623/trl/trainer/grpo_config.py#L22
-    use_vllm: Optional[bool] = Field(
+    use_vllm: bool | None = Field(
         default=False,
         json_schema_extra={"description": "Whether to use VLLM for RL training"},
     )
-    vllm_device: Optional[str] = Field(
+    vllm_device: str | None = Field(
         default="auto",
         json_schema_extra={"description": "Device to use for VLLM"},
     )
-    vllm_gpu_memory_utilization: Optional[float] = Field(
+    vllm_gpu_memory_utilization: float | None = Field(
         default=0.9,
         json_schema_extra={"description": "GPU memory utilization for VLLM"},
     )
-    vllm_dtype: Optional[str] = Field(
+    vllm_dtype: str | None = Field(
         default="auto",
         json_schema_extra={"description": "Data type for VLLM"},
     )
-    vllm_max_model_len: Optional[int] = Field(
+    vllm_max_model_len: int | None = Field(
         default=None,
         json_schema_extra={
             "description": "Maximum length of the model context for VLLM"
         },
     )
 
-    reward_funcs: Optional[list[str]] = Field(
+    reward_funcs: list[str] | None = Field(
         default=None,
         json_schema_extra={"description": "List of reward functions to load"},
     )
-    reward_weights: Optional[list[float]] = Field(
+    reward_weights: list[float] | None = Field(
         default=None,
         json_schema_extra={
             "description": "Weights for each reward function. Must match the number of reward functions."
         },
     )
-    num_generations: Optional[int] = Field(
+    num_generations: int | None = Field(
         default=None,
         json_schema_extra={
             "description": "Number of generations to sample. The global batch size (num_processes * per_device_batch_size) must be divisible by this value."
         },
     )
-    log_completions: Optional[bool] = Field(
+    log_completions: bool | None = Field(
         default=False,
         json_schema_extra={"description": "Whether to log completions"},
     )
-    sync_ref_model: Optional[bool] = Field(
+    sync_ref_model: bool | None = Field(
         default=False,
         json_schema_extra={
             "description": (
@@ -77,13 +73,13 @@ class TRLConfig(BaseModel):
             )
         },
     )
-    ref_model_mixup_alpha: Optional[float] = Field(
+    ref_model_mixup_alpha: float | None = Field(
         default=0.9,
         json_schema_extra={
             "description": "Mixup alpha for the reference model. Requires `sync_ref_model=True`."
         },
     )
-    ref_model_sync_steps: Optional[int] = Field(
+    ref_model_sync_steps: int | None = Field(
         default=64,
         json_schema_extra={
             "description": "Sync steps for the reference model. Requires `sync_ref_model=True`."

src/axolotl/utils/schemas/utils.py

@@ -0,0 +1,79 @@
+"""Utilities for Axolotl Pydantic models"""
+
+import logging
+
+LOG = logging.getLogger(__name__)
+
+
+def handle_legacy_message_fields_logic(data: dict) -> dict:
+    """
+    Handle backwards compatibility between legacy message field mapping and new property mapping system.
+
+    Previously, the config only supported mapping 'role' and 'content' fields via dedicated config options:
+    - message_field_role: Mapped to the role field
+    - message_field_content: Mapped to the content field
+
+    The new system uses message_property_mappings to support arbitrary field mappings:
+    message_property_mappings:
+        role: source_role_field
+        content: source_content_field
+        additional_field: source_field
+
+    Args:
+        data: Dictionary containing configuration data
+
+    Returns:
+        Updated dictionary with message field mappings consolidated
+
+    Raises:
+        ValueError: If there are conflicts between legacy and new mappings
+    """
+    data = data.copy()  # Create a copy to avoid modifying the original
+
+    if data.get("message_property_mappings") is None:
+        data["message_property_mappings"] = {}
+
+    # Check for conflicts and handle role
+    if "message_field_role" in data:
+        LOG.warning(
+            "message_field_role is deprecated, use message_property_mappings instead. "
+            f"Example: message_property_mappings: {{role: {data['message_field_role']}}}"
+        )
+        if (
+            "role" in data["message_property_mappings"]
+            and data["message_property_mappings"]["role"] != data["message_field_role"]
+        ):
+            raise ValueError(
+                f"Conflicting message role fields: message_field_role='{data['message_field_role']}' "
+                f"conflicts with message_property_mappings.role='{data['message_property_mappings']['role']}'"
+            )
+        data["message_property_mappings"]["role"] = data["message_field_role"] or "role"
+
+        del data["message_field_role"]
+    elif "role" not in data["message_property_mappings"]:
+        data["message_property_mappings"]["role"] = "role"
+
+    # Check for conflicts and handle content
+    if "message_field_content" in data:
+        LOG.warning(
+            "message_field_content is deprecated, use message_property_mappings instead. "
+            f"Example: message_property_mappings: {{content: {data['message_field_content']}}}"
+        )
+        if (
+            "content" in data["message_property_mappings"]
+            and data["message_property_mappings"]["content"]
+            != data["message_field_content"]
+        ):
+            raise ValueError(
+                f"Conflicting message content fields: message_field_content='{data['message_field_content']}' "
+                f"conflicts with message_property_mappings.content='{data['message_property_mappings']['content']}'"
+            )
+        data["message_property_mappings"]["content"] = (
+            data["message_field_content"] or "content"
+        )
+
+        del data["message_field_content"]
+    elif "content" not in data["message_property_mappings"]:
+        data["message_property_mappings"]["content"] = "content"
+
+    return data

styles.css

@@ -14,7 +14,7 @@
 h1 {
     font-family: var(--font-title);
     font-weight: 400;
-    font-size: 5rem;
+    font-size: 3rem;
     line-height: 1.1;
     letter-spacing: -0.05em;
     font-feature-settings: "ss01" on;
@@ -24,7 +24,7 @@ h1 {
 h2 {
     font-family: var(--font-title);
     font-weight: 500;
-    font-size: 2rem;
+    font-size: 1.5rem;
     line-height: 1.2;
     letter-spacing: -0.03em;
     font-feature-settings: "ss01" on;
@@ -35,7 +35,7 @@ h3,
 h4 {
     font-family: var(--font-body);
     font-weight: 400;
-    font-size: 1.5rem;
+    font-size: 1.25rem;
     line-height: 1.5;
     letter-spacing: -0.02em;
 }

tests/patched/test_validation.py

@@ -11,10 +11,10 @@ from pydantic import ValidationError
 
 from axolotl.utils import is_comet_available
 from axolotl.utils.config import validate_config
-from axolotl.utils.config.models.input.v0_4_1 import AxolotlConfigWCapabilities
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.mlflow_ import setup_mlflow_env_vars
 from axolotl.utils.models import check_model_config
+from axolotl.utils.schemas.config import AxolotlConfigWCapabilities
 from axolotl.utils.wandb_ import setup_wandb_env_vars
 
 warnings.filterwarnings("error")

tests/test_validation_dataset.py

@@ -6,8 +6,8 @@ from typing import Optional
 import pytest
 
 from axolotl.utils.config import validate_config
-from axolotl.utils.config.models.input.v0_4_1 import ChatTemplate
 from axolotl.utils.dict import DictDefault
+from axolotl.utils.schemas.datasets import ChatTemplate
 
 warnings.filterwarnings("error")
 

tests/utils/test_models.py

@@ -119,7 +119,7 @@ class TestModelsUtils:
 
     def test_message_property_mapping(self):
         """Test message property mapping configuration validation"""
-        from axolotl.utils.config.models.input.v0_4_1 import SFTDataset
+        from axolotl.utils.schemas.datasets import SFTDataset
 
         # Test legacy fields are mapped orrectly
         dataset = SFTDataset(