Hugging Face's logo

Spaces:
YuvrajSingh9886
/
StoryLlama
Paused

App Files Community
StoryLlama
File size: 4,745 Bytes 
5bb6ad4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
 
# import gdown
# import os
# import argparse

# def download_model(model_id, folder, filename):
#     os.makedirs(folder, exist_ok=True)
#     url = f"https://drive.google.com/uc?id={model_id}"
#     output_path = os.path.join(folder, filename)
#     print(f"Downloading model to {output_path}...")
#     gdown.download(url, output_path, quiet=False)
#     print("Download complete!")

# def main():
#     parser = argparse.ArgumentParser(description="Download models using gdown and organize them into appropriate folders.")
#     parser.add_argument("-P", "--pretrained", action="store_true", help="Download the pretrained model")
#     parser.add_argument("-F", "--sft", action="store_true", help="Download the fine-tuned model")
#     parser.add_argument("-D", "--dpo", action="store_true", help="Download the DPO model")
    
#     args = parser.parse_args()
    
#     pretrained_model_file_id = "1CwtDjbN6a7tt7mykywxAANHBTvdSr-98"
#     fine_tuned_model_id = "10bsea7_MFXw6T967iCrp6zSGMfqDljHf"
#     dpo_model_file_id = "1hIzV_VVdvmplQQuaH9QQCcmUbfolFjyh"
    
#     if args.pretrained:
#         download_model(pretrained_model_file_id, "weights/pretrained", "pretrained_model.pt")
#     if args.sft:
#         download_model(fine_tuned_model_id, "weights/fine_tuned", "fine_tuned_model.pt")
#     if args.dpo:
#         download_model(dpo_model_file_id, "weights/DPO", "dpo_model.pt")

# if __name__ == "__main__":
#     main()


# import os
# import argparse

# def download_model(model_id, folder, filename, access_token):
#     os.makedirs(folder, exist_ok=True)
#     output_path = os.path.join(folder, filename)
    
#     url = f"https://www.googleapis.com/drive/v3/files/{model_id}?alt=media"
#     command = f"curl -H \"Authorization: Bearer {access_token}\" {url} -o {output_path}"
    
#     print(f"Downloading model to {output_path}...")
#     os.system(command)
#     print("Download complete!")

# def main():
#     parser = argparse.ArgumentParser(description="Download models using Google Drive API and organize them into appropriate folders.")
#     parser.add_argument("-P", "--pretrained", action="store_true", help="Download the pretrained model")
#     parser.add_argument("-F", "--sft", action="store_true", help="Download the fine-tuned model")
#     parser.add_argument("-D", "--dpo", action="store_true", help="Download the DPO model")
#     parser.add_argument("--token", type=str, required=True, help="Google Drive API Access Token")
    
#     args = parser.parse_args()
    
#     pretrained_model_file_id = "1CwtDjbN6a7tt7mykywxAANHBTvdSr-98"
#     fine_tuned_model_id = "10bsea7_MFXw6T967iCrp6zSGMfqDljHf"
#     dpo_model_file_id = "1hIzV_VVdvmplQQuaH9QQCcmUbfolFjyh"
    
#     if args.pretrained:
#         download_model(pretrained_model_file_id, "weights/pretrained", "pretrained_model.pt", args.token)
#     if args.sft:
#         download_model(fine_tuned_model_id, "weights/fine_tuned", "fine_tuned_model.pt", args.token)
#     if args.dpo:
#         download_model(dpo_model_file_id, "weights/DPO", "dpo_model.pt", args.token)

# if __name__ == "__main__":
#     main()



# download_model_weight.py
import os
import argparse
from huggingface_hub import hf_hub_download, login

def download_model(repo_id, filename, cache_dir):

    try:
        model_path = hf_hub_download(
            repo_id=repo_id,
            filename=filename,
            cache_dir=cache_dir,
            resume_download=True,
            force_download=False,
            token=os.getenv("HF_TOKEN")
        )
       
        if os.path.exists(model_path) and os.path.getsize(model_path) > 1024*1024:
            return model_path
        raise ValueError("Downloaded file is too small or invalid")
    except Exception as e:
        print(f"Download failed: {str(e)}")
        raise

def main():
    parser = argparse.ArgumentParser(description="Download models from Hugging Face Hub")
    parser.add_argument("--model_type", 
                      choices=["pretrained"],
                      required=True,
                      help="Type of model to download")
    
    args = parser.parse_args()

    model_config = {

        "pretrained": {
            "repo_id": "YuvrajSingh9886/StoryLlama",
            "filename": "snapshot_4650.pt",
            "cache_dir": "weights/pretrained"
        }
    }

    config = model_config[args.model_type]
    os.makedirs(config["cache_dir"], exist_ok=True)
    
    print(f"Downloading {args.model_type} model...")
    model_path = download_model(
        config["repo_id"],
        config["filename"],
        config["cache_dir"]
    )
    print(f"Successfully downloaded to: {model_path}")

if __name__ == "__main__":

    login(token=os.getenv("HF_TOKEN"))
    main()