Configuration Specification

A Config resource stores a named configuration blob – JSON or YAML – that parameterizes training, quantization, inference, or the Axolotl framework. Configs are content-deduplicated: adding a config with identical content and type to an existing one returns the existing config’s ID rather than creating a duplicate.

Source: ft/configs.py, ft/consts.py, ft/db/model.py

Config Types

ORM Schema

class Config(Base, MappedProtobuf, MappedDict):
    __tablename__ = "configs"
    id = Column(String, primary_key=True)       # UUID
    type = Column(String)                        # ConfigType enum value
    description = Column(String)                 # Model name (for family resolution) or format name
    config = Column(Text)                        # Serialized JSON or YAML string
    model_family = Column(String)                # Architecture family (e.g., "LlamaForCausalLM")
    is_default = Column(Integer, default=1)      # 1 = system/default, 0 = user-created

is_default Semantics

User-created configs always have is_default=0. The add_config() function sets this automatically.

Default Config Values

Defined in ft/consts.py:

DEFAULT_TRAINING_ARGUMENTS

{
    "num_train_epochs": 1,
    "optim": "paged_adamw_32bit",
    "per_device_train_batch_size": 1,
    "gradient_accumulation_steps": 4,
    "warmup_ratio": 0.03,
    "max_grad_norm": 0.3,
    "learning_rate": 0.0002,
    "fp16": true,
    "logging_steps": 1,
    "lr_scheduler_type": "constant",
    "disable_tqdm": true,
    "report_to": "mlflow",
    "ddp_find_unused_parameters": false
}

DEFAULT_BNB_CONFIG

{
    "load_in_4bit": true,
    "bnb_4bit_quant_type": "nf4",
    "bnb_4bit_compute_dtype": "float16",
    "bnb_4bit_use_double_quant": true,
    "quant_method": "bitsandbytes"
}

DEFAULT_LORA_CONFIG

{
    "r": 16,
    "lora_alpha": 32,
    "lora_dropout": 0.05,
    "bias": "none",
    "task_type": "CAUSAL_LM"
}

DEFAULT_GENERATIONAL_CONFIG

{
    "do_sample": true,
    "temperature": 0.8,
    "max_new_tokens": 60,
    "top_p": 1,
    "top_k": 50,
    "num_beams": 1,
    "repetition_penalty": 1.1,
    "max_length": null
}

Config Deduplication

add_config() implements content-addressed caching:

1. Parse the incoming config string: yaml.safe_load() for axolotl type, json.loads() for all others.
2. Re-serialize to a canonical form (yaml.dump() or json.dumps()).
3. Query existing configs of the same type (and same model_family if description is provided).
4. Compare parsed content of each existing config against the parsed request content.
5. If an identical config exists, return it. At most one duplicate is expected (asserted).
6. If no match, create a new Config with is_default=USER_CONFIGS (0).

When description is provided, it is interpreted as a model name: transform_name_to_family(description) resolves the HuggingFace architecture (e.g., "LlamaForCausalLM") and scopes the deduplication query to that family.

Model-Family-Specific Filtering

list_configs() applies model-aware filtering when model_id is present in the request:

1. Optionally filter by type if specified.
2. If model_id is provided, call get_configs_for_model_id():
   • Fetch the Model record and resolve huggingface_model_name.
   • Instantiate ModelMetadataFinder(model_hf_name) and call fetch_model_family_from_config().
   • Filter configs where model_family matches and is_default == 1.
   • If no model-specific defaults exist, fall back to returning all configs.
3. User configs (is_default=0) are not filtered by model family in get_configs_for_model_id() – they are returned when no model-specific defaults are found (fallback behavior).

Axolotl Config Template

The Axolotl config template is loaded from ft/config/axolotl/training_config/template.yaml via get_axolotl_training_config_template_yaml_str(). Axolotl dataset format configs are stored in ft/config/axolotl/dataset_formats/.

Protobuf Message

ConfigMetadata fields: id, type, description, config (serialized JSON/YAML string), model_family, is_default.