Add cloning examples

Author: jsun-m

civitai-clone/.beta9ignore

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+# Generated by Beta9 SDK
+.beta9ignore
+pyproject.toml
+.git
+.idea
+.python-version
+.vscode
+.venv
+venv
+__pycache__
+.DS_Store
+.config
+drive/MyDrive
+.coverage
+.pytest_cache
+.ipynb
+.ruff_cache
+.dockerignore
+.ipynb_checkpoints
+.env.local
+.envrc
+**/__pycache__/
+**/.pytest_cache/
+**/node_modules/
+**/.venv/
+*.pyc
+.next/
+.circleci

civitai-clone/app.py

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+import os
+import requests
+import urllib.parse
+import json
+from beta9 import function, Volume, Image
+
+@function(
+  app="volume-imports",
+  name="civitai-clone-model",
+  secrets=["CIVITAI_API_KEY"],
+  memory="4gb",
+  image=Image(
+    python_packages=["requests"]
+  ),
+  volumes=[Volume(name="civitai_models", mount_path="/civitai_models")]
+)
+def handler(*, url: str):
+  if not url:
+    raise ValueError("url is required")
+  
+  print(f"Downloading model from {url}")
+  model_id = urllib.parse.urlparse(url).path.split("/")[2]
+  print(f"Model ID: {model_id}")
+  
+  try:
+    response = requests.get(
+      f"https://civitai.com/api/v1/models/{model_id}",
+      headers={
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {os.getenv('CIVITAI_API_KEY')}"
+      },
+    )
+    response.raise_for_status()
+    data  = response.json()
+    
+    latest_version = data["modelVersions"][0]
+    download_url = latest_version["downloadUrl"]
+    print(f"Download URL: {download_url}")
+    
+    save_path = f"/civitai_models/{data['name'].replace(' ', '_')}"
+    with requests.get(download_url, 
+                     headers={
+                       "Content-Type": "application/json",
+                       "Authorization": f"Bearer {os.getenv('CIVITAI_API_KEY')}"
+                     },
+                     stream=True) as response:
+        response.raise_for_status()
+        total_size = int(response.headers.get('content-length', 0))
+        print(f"Total file size: {total_size / (1024*1024):.2f} MB")
+        
+        with open(save_path, 'wb') as f:
+            for chunk in response.iter_content(chunk_size=8192):
+                if chunk:
+                    f.write(chunk)
+    
+    print("Model downloaded successfully")  
+    return {
+      "model_id": model_id,
+      "downloaded_path": save_path
+    }
+    
+  except Exception as e:
+    print(f"Failed to get model ID: {str(e)}")
+    raise Exception(f"Failed to get model ID: {str(e)}")
+
+if __name__ == "__main__":
+  handler(url="https://civitai.com/models/1224788/prefect-illustrious-xl")

huggingface-clone/.beta9ignore

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+# Generated by Beta9 SDK
+.beta9ignore
+pyproject.toml
+.git
+.idea
+.python-version
+.vscode
+.venv
+venv
+__pycache__
+.DS_Store
+.config
+drive/MyDrive
+.coverage
+.pytest_cache
+.ipynb
+.ruff_cache
+.dockerignore
+.ipynb_checkpoints
+.env.local
+.envrc
+**/__pycache__/
+**/.pytest_cache/
+**/node_modules/
+**/.venv/
+*.pyc
+.next/
+.circleci

huggingface-clone/app.py

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+from beta9 import function, Volume, Image
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+@function(
+  app="volume-imports",
+  name="huggingface-clone-model",
+  secrets=["HUGGINGFACE_TOKEN"],
+  memory="8gb",
+  image=Image(
+    python_packages=["torch","transformers"]
+  ),
+  volumes=[Volume(name="huggingface_models", mount_path="/huggingface_models")]
+)
+def handler(*, model_name: str = ""):
+    if not model_name:
+        raise ValueError("model_name is required")
+    
+    print(f"Downloading model: {model_name}")
+    
+    try:
+        # Download model and tokenizer
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        
+        # Save to local volume
+        save_path = f"/huggingface_models/{model_name.replace('/', '_')}"
+        model.save_pretrained(save_path)
+        tokenizer.save_pretrained(save_path)
+        print(f"Model and tokenizer saved to: {save_path}")
+        
+        return {
+            "model_name": model_name,
+            "saved_path": save_path
+        }
+    except Exception as e:
+        print(f"Failed to download model: {str(e)}")
+        raise Exception(f"Failed to download model: {str(e)}")
+
+if __name__ == "__main__":
+    handler(model_name="distilbert/distilgpt2")