nest

README.md

@@ -8,7 +8,7 @@ sdk_version: 4.32.2
 app_file: app.py
 pinned: false
 license: mit
-startup_duration_timeout: 6h
+startup_duration_timeout: 5h
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -5,15 +5,13 @@ from trl import SFTTrainer
 from transformers import TrainingArguments
 from datasets import load_dataset
 import gradio as gr
-from huggingface_hub import HfApi, SpaceHardware
+from huggingface_hub import HfApi
 
 max_seq_length = 4096
 dtype = None
 load_in_4bit = True
 hf_token = os.getenv("Token")
 
-api = HfApi(token=hf_token)
-
 print("Starting model and tokenizer loading...")
 
 # Load the model and tokenizer
@@ -124,7 +122,7 @@ trainer = SFTTrainer(
         weight_decay=0.01,
         lr_scheduler_type="linear",
         seed=3407,
-        output_dir="outputs",
+        output_dir="outputs"
     ),
 )
 print("Trainer initialized.")
@@ -139,21 +137,12 @@ print("Model saved successfully.")
 
 print("Pushing the model to the hub...")
 model.push_to_hub_merged(
-    "CyberSentinel-16bit-initial",
+    "CyberSentinel-initial",
     tokenizer,
     save_method="merged_16bit",
     token=True
 )
 print("Model pushed to hub successfully.")
 
-api.pause_space(repo_id="dad1909/CyberCode")
-
-print("Model is pause as initial.")
-
-# api.duplicate_space(
-#     from_id="original_space_id",
-#     secrets=[
-#         {"key": "HF_TOKEN", "value": "hf_api_***"},
-#         # Add more secrets if needed
-#     ],
-# )
+api = HfApi()
+api.pause_space(repo_id="dad1909/CyberCode")