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| import transformers | |
| import torch | |
| model_id = "yodayo-ai/nephra_v1.0" | |
| pipeline = transformers.pipeline( | |
| "text-generation", | |
| model=model_id, | |
| model_kwargs={"torch_dtype": torch.bfloat16}, | |
| device_map="auto", | |
| offload_folder="offload", # Only provided here during model initialization | |
| ) | |
| # Define characters and traits | |
| characters = [ | |
| {"name": "Alex", | |
| "description": "Alex is a young and ambitious adventurer, full of energy and a thirst for new discoveries. Always ready to face any challenge, he is driven by a desire to explore uncharted places.", | |
| "traits": "brave, energetic, optimistic, determined"}, | |
| {"name": "Maya", | |
| "description": "Maya is a wise and experienced sorceress, with deep knowledge in magic and ancient rituals. She is known for her calm demeanor, analytical mind, and ability to find solutions in difficult situations.", | |
| "traits": "calm, thoughtful, intuitive, attentive"}, | |
| {"name": "Victor", | |
| "description": "Victor is a former warrior who gave up fighting for inner peace and harmony. His life experience and pursuit of justice make him a reliable friend and mentor.", | |
| "traits": "serious, thoughtful, fair, balanced"} | |
| ] | |
| def generate_response(character_name, user_input, max_length=100, temperature=0.7, top_p=0.85, repetition_penalty=1.1): | |
| # Find the character data | |
| character = next((c for c in characters if c["name"] == character_name), None) | |
| if not character: | |
| return "Character not found." | |
| # Create the prompt text | |
| prompt_text = (f"You are {character_name}, {character['description']}. Traits: {character['traits']}. " | |
| f"In response to the question '{user_input}', respond {random.choice(['inspired', 'with doubt', 'joyfully', 'thoughtfully', 'skeptically'])}. Please complete the response.") | |
| # Generate response | |
| outputs = pipeline( | |
| prompt_text, | |
| max_new_tokens=max_length, | |
| do_sample=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| repetition_penalty=repetition_penalty | |
| ) | |
| return outputs[0]['generated_text'] | |
| # Gradio Interface | |
| import gradio as gr | |
| iface = gr.Interface( | |
| fn=generate_response, | |
| inputs=[ | |
| gr.Dropdown([c["name"] for c in characters], label="Choose Character"), | |
| gr.Textbox(lines=2, placeholder="Enter your text here..."), | |
| gr.Slider(20, 200, step=1, value=100, label="Max Length"), | |
| gr.Slider(0.1, 1.0, step=0.1, value=0.7, label="Temperature"), | |
| gr.Slider(0.1, 1.0, step=0.05, value=0.85, label="Top-p"), | |
| gr.Slider(1.0, 2.0, step=0.1, value=1.1, label="Repetition Penalty") | |
| ], | |
| outputs="text", | |
| title="Roleplaying Model Demo", | |
| description="Generate responses based on the chosen character's traits." | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() |