loop.py: PEFT-based adapter resume (overrides stale base path)

physix/training/loop.py

@@ -229,17 +229,51 @@ def _load_model_and_tokenizer(
     rollouts produce meaningful reward signal instead of all scoring zero.
     """
     if config.lora_adapter_repo:
+        # Resume path: load the base model and attach the existing LoRA
+        # adapter via PEFT. We deliberately do NOT call
+        # ``FastLanguageModel.from_pretrained(model_name=adapter_repo)``
+        # because the adapter's ``adapter_config.json`` may carry a stale
+        # ``base_model_name_or_path`` pointing at a path that only existed
+        # inside the previous training container (e.g. ``/tmp/physix-sft/merged``).
+        # PEFT's ``load_adapter`` ignores that field — it adapts onto whatever
+        # base we hand it.
         _log.info(
-            "Resuming from existing LoRA adapter %s (warm-start, skipping fresh PEFT init)",
+            "Resuming from existing LoRA adapter %s on top of %s",
             config.lora_adapter_repo,
+            config.model_name,
         )
         model, tokenizer = FastLanguageModel.from_pretrained(
-            model_name=config.lora_adapter_repo,
+            model_name=config.model_name,
             max_seq_length=config.max_seq_length,
             load_in_4bit=True,
             dtype=None,
         )
-        FastLanguageModel.for_training(model)
+        # Wrap the base in a fresh trainable LoRA, then overwrite its
+        # weights with the saved adapter. We use the adapter's own r/alpha
+        # by relying on PEFT's ``load_adapter`` resolving from the repo's
+        # adapter_config.json. The dummy ``get_peft_model`` call is just to
+        # turn the model into a ``PeftModel`` instance whose ``load_adapter``
+        # method accepts a hub repo id.
+        model = FastLanguageModel.get_peft_model(
+            model,
+            r=config.lora_r,
+            lora_alpha=config.lora_alpha,
+            target_modules=[
+                "q_proj", "k_proj", "v_proj", "o_proj",
+                "gate_proj", "up_proj", "down_proj",
+            ],
+            bias="none",
+            use_gradient_checkpointing="unsloth",
+            random_state=config.seed,
+        )
+        # Overwrite the freshly-initialised LoRA weights with the saved ones.
+        # ``adapter_name='default'`` matches what ``get_peft_model`` creates.
+        model.load_adapter(
+            config.lora_adapter_repo,
+            adapter_name="default",
+            is_trainable=True,
+        )
+        _log.info("Adapter loaded; LoRA is trainable and ready for GRPO.")
         return model, tokenizer
 
     if config.sft_checkpoint: