added a variable to set device map, placed model kws in another variable and passed as metadata

run_test.py

@@ -5,6 +5,7 @@ STORE_WEIGHTS = False
 FAKE_H100 = False
 TORCH_DTYPE = 'float64'
 USE_GPU = False
+DEVICE_MAP = 'auto'
 model_id, revision = sys.argv[1:]
 user, model = model_id.split('/')
 prompt = 'Once upon a time,'
@@ -38,9 +39,10 @@ max_memory = accelerate.utils.get_max_memory()
 if not USE_GPU:
     max_memory = {'cpu': max_memory['cpu']}
 max_memory['cpu'] //= 3
-model = Model.from_pretrained(model_id, revision=revision, trust_remote_code=True,
+model_kwparams = dict(pretrained_model_name_or_path=model_id, revision=revision, trust_remote_code=True,
     torch_dtype=getattr(torch, TORCH_DTYPE, TORCH_DTYPE), use_safetensors=True, low_cpu_mem_usage=True,
-    device_map='auto', max_memory=max_memory, offload_state_dict=True, offload_buffers=True)
+    device_map=DEVICE_MAP, max_memory=max_memory, offload_state_dict=True, offload_buffers=True)
+model = Model.from_pretrained(**model_kwparams)
 
 if config.model_type == 'deepseek_v3':
     model._supports_cache_class = False
@@ -50,11 +52,9 @@ pipe = transformers.pipeline('text-generation', model=model, config=config, toke
 SafeTensors = WritingSafeTensors(
         fn,
         prompt = prompt,
-        model_id = model_id,
-        revision = revision,
-        weights = STORE_WEIGHTS,
-        torch_dtype = TORCH_DTYPE,
-        gpu = USE_GPU,
+        store_weights = STORE_WEIGHTS,
+        use_gpu = USE_GPU,
+        **model_kwparams,
         **{
             f'{hw}:{idx}':
                 str(getattr(torch, hw).get_device_properties(idx))