Model Downloading, no GPU

Dockerfile

@@ -16,12 +16,13 @@ RUN --mount=type=cache,target=/root/.cache/pip \
 ADD src .
 
 ARG MODEL_NAME=""
+ENV MODEL_NAME=$MODEL_NAME
 ARG MODEL_BASE_PATH=""
-ARG TOKENIZER=""
+ENV MODEL_BASE_PATH=$MODEL_BASE_PATH
 
 # Conditionally run download_model.py
-RUN if [ -n "$MODEL_NAME" ] && [ -n "$MODEL_BASE_PATH"]; then \
-        python3.11 /download_model.py --model $MODEL_NAME --download_dir $MODEL_BASE_PATH --tokenizer $TOKENIZER; \
+RUN if [ -n "$MODEL_NAME" ] && [ -n "$MODEL_BASE_PATH" ]; then \
+        python3.11 /download_model.py --model $MODEL_NAME --download_dir $MODEL_BASE_PATH; \
     fi
 
 # Start the handler

src/download_model.py

@@ -1,22 +1,16 @@
 import argparse
-from vllm import LLMEngine, SamplingParams, AsyncEngineArgs, utils
+from vllm.model_executor.weight_utils import prepare_hf_model_weights
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--model", type=str)
     parser.add_argument("--download_dir", type=str)
-    parser.add_argument("--tokenizer", type=str, default=None)
 
     args = parser.parse_args()
     if not args.model or not args.download_dir:
         raise ValueError("Must specify model and download_dir")
 
-
-    engine_args = AsyncEngineArgs(
-    model=args.model,
-    download_dir=args.download_dir,
-    tokenizer=args.tokenizer,
-    dtype="auto"
-    )
-    
-    llm = LLMEngine.from_engine_args(engine_args)
+    prepare_hf_model_weights(
+        model_name_or_path = args.model,
+        cache_dir=args.download_dir,
+    )