Add script to export model to HF or local directory (LAION-AI#2028)

```
usage: export_model.py [-h] [--dtype DTYPE] [--hf_repo_name HF_REPO_NAME] [--auth_token AUTH_TOKEN] [--output_folder OUTPUT_FOLDER]
                       [--max_shard_size MAX_SHARD_SIZE] [--cache_dir CACHE_DIR]
                       model_name

positional arguments:
  model_name            checkpoint path or model name

options:
  -h, --help            show this help message and exit
  --dtype DTYPE         float16 or float32
  --hf_repo_name HF_REPO_NAME
                        Huggingface repository name
  --auth_token AUTH_TOKEN
                        User access token
  --output_folder OUTPUT_FOLDER
                        output folder path
  --max_shard_size MAX_SHARD_SIZE
  --cache_dir CACHE_DIR
  ```

Author: andreaskoepf

model/model_training/tools/export_model.py

@@ -0,0 +1,62 @@
+import argparse
+import sys
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("model_name", type=str, help="checkpoint path or model name")
+    parser.add_argument("--dtype", type=str, default="float16", help="float16 or float32")
+    parser.add_argument("--hf_repo_name", type=str, help="Huggingface repository name")
+    parser.add_argument("--auth_token", type=str, help="User access token")
+    parser.add_argument("--output_folder", type=str, help="output folder path")
+    parser.add_argument("--max_shard_size", type=str, default="10GB")
+    parser.add_argument("--cache_dir", type=str)
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+
+    if args.dtype in ("float16", "fp16"):
+        torch_dtype = torch.float16
+    elif args.dtype in ("float32", "fp32"):
+        torch_dtype = torch.float32
+    else:
+        print(f"Unsupported dtpye: {args.dtype}")
+        sys.exit(1)
+
+    if not args.hf_repo_name and not args.output_folder:
+        print(
+            "Please specify either `--hf_repo_name` to push to HF or `--output_folder` "
+            "to export the model to a local folder."
+        )
+        sys.exit(1)
+
+    print(f"Loading tokenizer '{args.model_name}' ...")
+    tokenizer = AutoTokenizer.from_pretrained(args.model_name)
+    print(f"{type(tokenizer).__name__} (vocab_size={len(tokenizer)})")
+
+    print(f"Loading model '{args.model_name}' ({args.dtype}) ...")
+    model = AutoModelForCausalLM.from_pretrained(args.model_name, torch_dtype=torch_dtype, cache_dir=args.cache_dir)
+    print(f"{type(model).__name__} (num_parameters={model.num_parameters()})")
+
+    if args.output_folder:
+        print(f"Saving model to: {args.output_folder}")
+        model.save_pretrained(args.output_folder, max_shard_size=args.max_shard_size)
+
+        print(f"Saving tokenizer to: {args.output_folder}")
+        tokenizer.save_pretrained(args.output_folder)
+
+    if args.hf_repo_name:
+        print("Uploading model to HF...")
+        model.push_to_hub(args.hf_repo_name, use_auth_token=args.auth_token, max_shard_size=args.max_shard_size)
+
+        print("Uploading tokenizer to HF...")
+        tokenizer.push_to_hub(args.hf_repo_name, use_auth_token=args.auth_token)
+
+
+if __name__ == "__main__":
+    main()