Create colab.ipynb

colab.ipynb

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+# -*- coding: utf-8 -*-
+"""L3.1-Dark-Reasoning-LewdPlay-evo-Hermes-R1-Uncensored-8B.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/DavidAU/L3.1-Dark-Reasoning-LewdPlay-evo-Hermes-R1-Uncensored-8B.ipynb
+
+# Local Inference on GPU
+
+Model page: https://huggingface.co/DavidAU/L3.1-Dark-Reasoning-LewdPlay-evo-Hermes-R1-Uncensored-8B
+
+"""
+
+# Install transformers library
+# This command should be run in a Colab cell, not directly in a Python script.
+# !pip install -U transformers
+
+from transformers import pipeline
+
+# Load the model
+pipeline = pipeline("text-generation", model="DavidAU/L3.1-Dark-Reasoning-LewdPlay-evo-Hermes-R1-Uncensored-8B")
+
+def chat_with_model(prompt):
+    messages = [
+        {"role": "user", "content": prompt},
+    ]
+    response = pipeline(messages)
+    # The response structure might vary, so we'll try to extract the content safely.
+    # This assumes the last generated text is the bot's response.
+    if response and isinstance(response, list) and len(response) > 0:
+        generated_text = response[0].get("generated_text")
+        if generated_text and isinstance(generated_text, list) and len(generated_text) > 0:
+            last_message = generated_text[-1]
+            if isinstance(last_message, dict) and "content" in last_message:
+                return last_message["content"]
+    return "Sorry, I couldn't generate a response."
+
+# Example usage in a Colab cell:
+# print(chat_with_model("Hello, how are you?"))
+# print(chat_with_model("Tell me a story."))
+
+# To make it interactive in a Colab environment, you would typically use
+# input() within a loop in a separate cell, or use Colab's form features.
+# For a simple demonstration within the notebook:
+
+user_query_1 = "Hello, how are you?"
+bot_response_1 = chat_with_model(user_query_1)
+print(f"User: {user_query_1}")
+print(f"Bot: {bot_response_1}")
+
+user_query_2 = "What can you do?"
+bot_response_2 = chat_with_model(user_query_2)
+print(f"User: {user_query_2}")
+print(f"Bot: {bot_response_2}")
+
+