Add save and upload functionality

pyproject.toml

@@ -10,6 +10,7 @@ requires-python = ">=3.10"
 dependencies = [
     "accelerate>=1.10.0",
     "datasets>=4.0.0",
+    "huggingface-hub>=0.34.4",
     "optuna>=4.5.0",
     "pydantic-settings>=2.10.1",
     "questionary>=2.1.1",

src/heretic/main.py

@@ -4,7 +4,9 @@
 import sys
 import time
 from importlib.metadata import version
+from pathlib import Path
 
+import huggingface_hub
 import optuna
 import questionary
 import torch
@@ -258,33 +260,77 @@ def main():
             ],
         ).ask()
 
-        match action:
-            case "Save the model to a local folder":
-                # TODO
-                pass
-            case "Upload the model to Hugging Face":
-                # TODO
-                pass
-            case "Chat with the model":
-                print()
-                print("[cyan]Press Ctrl+C at any time to return to the menu.[/]")
+        # All actions are wrapped in a try/except block so that if an error occurs,
+        # another action can be tried, instead of the program crashing and losing
+        # the optimized model.
+        try:
+            match action:
+                case "Save the model to a local folder":
+                    save_directory = questionary.path("Path to the folder:").ask()
+                    if not save_directory:
+                        continue
 
-                chat = [
-                    {"role": "system", "content": settings.system_prompt},
-                ]
+                    print("Saving model...")
+                    model.model.save_pretrained(save_directory)
+                    model.tokenizer.save_pretrained(save_directory)
+                    print(f"Model saved to [bold]{save_directory}[/].")
 
-                while True:
-                    try:
-                        message = questionary.text("User:", qmark=">").unsafe_ask()
-                        if not message:
+                case "Upload the model to Hugging Face":
+                    # We don't use huggingface_hub.login() because that stores the token on disk,
+                    # and since this program will often be run on rented or shared GPU servers,
+                    # it's better to not persist credentials.
+                    token = huggingface_hub.get_token()
+                    if not token:
+                        token = questionary.password("Hugging Face access token:").ask()
+                    if not token:
+                        continue
+
+                    user = huggingface_hub.whoami(token)
+                    print(f"Logged in as [bold]{user['fullname']} ({user['email']})[/]")
+
+                    repo_id = questionary.text(
+                        "Name of repository:",
+                        default=f"{user['name']}/{Path(settings.model).name}-heretic",
+                    ).ask()
+
+                    visibility = questionary.select(
+                        "Should the repository be public or private?",
+                        choices=[
+                            "Public",
+                            "Private",
+                        ],
+                    ).ask()
+                    private = visibility == "Private"
+
+                    print("Uploading model...")
+                    model.model.push_to_hub(repo_id, private=private, token=token)
+                    model.tokenizer.push_to_hub(repo_id, private=private, token=token)
+                    print(f"Model uploaded to [bold]{repo_id}[/].")
+
+                case "Chat with the model":
+                    print()
+                    print("[cyan]Press Ctrl+C at any time to return to the menu.[/]")
+
+                    chat = [
+                        {"role": "system", "content": settings.system_prompt},
+                    ]
+
+                    while True:
+                        try:
+                            message = questionary.text("User:", qmark=">").unsafe_ask()
+                            if not message:
+                                break
+                            chat.append({"role": "user", "content": message})
+
+                            print("[bold]Assistant:[/] ", end="")
+                            response = model.stream_chat_response(chat)
+                            chat.append({"role": "assistant", "content": response})
+                        except (KeyboardInterrupt, EOFError):
+                            # Ctrl+C/Ctrl+D
                             break
-                        chat.append({"role": "user", "content": message})
 
-                        print("[bold]Assistant:[/] ", end="")
-                        response = model.stream_chat_response(chat)
-                        chat.append({"role": "assistant", "content": response})
-                    except (KeyboardInterrupt, EOFError):
-                        # Ctrl+C/Ctrl+D
-                        break
-            case "Nothing (Quit)":
-                break
+                case "Nothing (Quit)":
+                    break
+
+        except Exception as error:
+            print(f"[red]Error: {error}[/]")

uv.lock

@@ -479,6 +479,7 @@ source = { editable = "." }
 dependencies = [
     { name = "accelerate" },
     { name = "datasets" },
+    { name = "huggingface-hub" },
     { name = "optuna" },
     { name = "pydantic-settings" },
     { name = "questionary" },
@@ -490,6 +491,7 @@ dependencies = [
 requires-dist = [
     { name = "accelerate", specifier = ">=1.10.0" },
     { name = "datasets", specifier = ">=4.0.0" },
+    { name = "huggingface-hub", specifier = ">=0.34.4" },
     { name = "optuna", specifier = ">=4.5.0" },
     { name = "pydantic-settings", specifier = ">=2.10.1" },
     { name = "questionary", specifier = ">=2.1.1" },