fix: pass model as positional arg to satisfy type checker and update lockfile

Signed-off-by: mvansegbroeck <mvansegbroeck@gmail.com>

Author: mvansegbroeck

src/nemo_safe_synthesizer/generation/vllm_backend.py

@@ -203,8 +203,8 @@ def initialize(self, **kwargs) -> None:
         # check this when updating unsloth in the future.
         enforce_eager = self.config.training.use_unsloth is True
 
+        model = self.config.training.pretrained_model
         vllm_kwargs = dict(
-            model=self.config.training.pretrained_model,
             gpu_memory_utilization=max_vram,
             enable_lora=True,
             max_lora_rank=self.config.training.lora_r,
@@ -214,16 +214,16 @@ def initialize(self, **kwargs) -> None:
         # attention_config was added in vLLM 0.12+ but is not present in NGC
         # container builds (e.g. nvcr.io/nvidia/vllm:26.02-py3 ships 0.15.1 without it).
         # Fall back to VLLM_ATTENTION_BACKEND env var if the kwarg is not accepted.
-        with heartbeat("Model loading", logger_name=__name__, model=self.config.training.pretrained_model):
+        with heartbeat("Model loading", logger_name=__name__, model=model):
             if attention_config is not None:
                 try:
-                    self.llm = vLLM(**vllm_kwargs, attention_config=attention_config)
+                    self.llm = vLLM(model, **vllm_kwargs, attention_config=attention_config)
                 except TypeError:
                     if attn_backend not in (None, "auto"):
                         os.environ["VLLM_ATTENTION_BACKEND"] = attn_backend
-                    self.llm = vLLM(**vllm_kwargs)
+                    self.llm = vLLM(model, **vllm_kwargs)
             else:
-                self.llm = vLLM(**vllm_kwargs)
+                self.llm = vLLM(model, **vllm_kwargs)
 
     def _build_structured_output_params(self) -> StructuredOutputsParams | None:
         """Build structured output parameters based on generation config.