refactor(training): import BASE_DEFAULTS and PROFILES from projectdavid-common

Previously the training BASE_DEFAULTS and PROFILES dicts were duplicated across training_config_resolver.py (server-side) and unsloth_train.py (trainer-side TRAINER_FALLBACKS). Any change to a profile value had to be made in both files or drift was guaranteed.

Hoisted into projectdavid_common.constants.training_profiles (>=0.71.0). Both consumers now import the same objects. TRAINER_FALLBACKS is now a thin derivation that adds target_modules on top of BASE_DEFAULTS (target_modules promotion to API surface is Phase 2 item 1).

Author: Francisco

src/api/training/services/training_config_resolver.py

@@ -3,55 +3,9 @@
 
 from typing import Any, Dict, Optional
 
+from projectdavid_common.constants import BASE_DEFAULTS, PROFILES
 from projectdavid_common.schemas.training_schema import TrainingConfig, TrainingProfile
 
-# Canonical defaults. Represents the behaviour of the current codebase when
-# no config is supplied — the values currently hardcoded in unsloth_train.py
-# (SFTConfig + get_peft_model call sites). These are also the values baked
-# into PROFILES["standard"] for profile-scoped fields, so an empty config
-# reproduces the previous default-profile-standard behaviour.
-BASE_DEFAULTS: Dict[str, Any] = {
-    # Profile-scoped (overridable by profile preset):
-    "max_seq_length": 2048,
-    "per_device_train_batch_size": 2,
-    "gradient_accumulation_steps": 4,
-    "max_steps": 60,
-    "optim": "adamw_8bit",
-    # SFTConfig-scoped:
-    "learning_rate": 2e-4,
-    "warmup_steps": 2,
-    "weight_decay": 0.01,
-    "lr_scheduler_type": "linear",
-    "seed": 3407,
-    "logging_steps": 50,
-    "num_train_epochs": 3,
-    # PEFT-scoped:
-    "lora_r": 32,
-    "lora_alpha": 32,
-    "lora_dropout": 0.0,
-    "bias": "none",
-}
-
-# Must match PROFILES in unsloth_train.py. Kept duplicated for Phase 1;
-# Phase 2 cleanup should hoist this into a shared constants module imported
-# by both the resolver and the trainer.
-PROFILES: Dict[str, Dict[str, Any]] = {
-    "laptop": {
-        "max_seq_length": 1024,
-        "per_device_train_batch_size": 1,
-        "gradient_accumulation_steps": 8,
-        "max_steps": 12500,
-        "optim": "adamw_8bit",
-    },
-    "standard": {
-        "max_seq_length": 2048,
-        "per_device_train_batch_size": 2,
-        "gradient_accumulation_steps": 4,
-        "max_steps": 60,
-        "optim": "adamw_8bit",
-    },
-}
-
 
 def resolve_training_config(user_config: Optional[TrainingConfig]) -> Dict[str, Any]:
     """
@@ -67,6 +21,11 @@ def resolve_training_config(user_config: Optional[TrainingConfig]) -> Dict[str,
 
     The returned dict is the complete execution plan. Worker and trainer
     read from it without further resolution logic.
+
+    BASE_DEFAULTS and PROFILES are the canonical dicts exported from
+    projectdavid_common.constants — the trainer safety-net fallbacks in
+    unsloth_train.py import the same objects, so there is no possible
+    drift between resolver and trainer.
     """
     resolved: Dict[str, Any] = dict(BASE_DEFAULTS)
 

src/api/training/unsloth_train.py

@@ -14,6 +14,7 @@
 
 import unsloth  # noqa: F401 — must precede trl/transformers/peft
 from datasets import load_dataset
+from projectdavid_common.constants import BASE_DEFAULTS
 from transformers import TrainerCallback
 from trl import SFTConfig, SFTTrainer
 from unsloth import FastLanguageModel, is_bfloat16_supported
@@ -25,22 +26,10 @@
 #
 # target_modules is fixed here (not exposed via the API in Phase 1) — Phase 2
 # will add base-model-aware validation before it becomes user-tunable.
+
+
 TRAINER_FALLBACKS = {
-    "max_seq_length": 2048,
-    "per_device_train_batch_size": 2,
-    "gradient_accumulation_steps": 4,
-    "max_steps": 60,
-    "optim": "adamw_8bit",
-    "learning_rate": 2e-4,
-    "warmup_steps": 2,
-    "weight_decay": 0.01,
-    "lr_scheduler_type": "linear",
-    "seed": 3407,
-    "logging_steps": 50,
-    "lora_r": 32,
-    "lora_alpha": 32,
-    "lora_dropout": 0.0,
-    "bias": "none",
+    **BASE_DEFAULTS,
     "target_modules": [
         "q_proj",
         "k_proj",