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import spaces |
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import gradio as gr |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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title = """# Minitron-8B-Base Story Generator""" |
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description = """ |
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# Minitron |
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Minitron is a family of small language models (SLMs) obtained by pruning [NVIDIA's](https://huggingface.co/nvidia) Nemotron-4 15B model. We prune model embedding size, attention heads, and MLP intermediate dimension, following which, we perform continued training with distillation to arrive at the final models. |
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# Short Story Generator |
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Welcome to the Short Story Generator! This application helps you create unique short stories based on your inputs. |
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**Instructions:** |
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1. **Main Character:** Describe the main character of your story. For example, "a brave knight" or "a curious cat". |
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2. **Setting:** Describe the setting where your story takes place. For example, "in an enchanted forest" or "in a bustling city". |
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3. **Plot Twist:** Add an interesting plot twist to make the story exciting. For example, "discovers a hidden treasure" or "finds a secret portal to another world". |
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After filling in these details, click the "Submit" button, and a short story will be generated for you. |
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""" |
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inputs = [ |
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gr.Textbox(label="Main Character", placeholder="e.g. a brave knight"), |
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gr.Textbox(label="Setting", placeholder="e.g. in an enchanted forest"), |
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gr.Textbox(label="Plot Twist", placeholder="e.g. discovers a hidden treasure"), |
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gr.Slider(minimum=1, maximum=2048, value=64, step=1, label="Max new tokens"), |
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), |
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gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"), |
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] |
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outputs = gr.Textbox(label="Generated Story") |
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model_path = "nvidia/Minitron-8B-Base" |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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device='cuda' |
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dtype=torch.bfloat16 |
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model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=dtype, device_map=device) |
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def create_prompt(instruction): |
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PROMPT = '''Below is an instruction that describes a task.\n\nWrite a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:''' |
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return PROMPT.format(instruction=instruction) |
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@spaces.GPU |
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def respond(message, history, system_message, max_tokens, temperature, top_p): |
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prompt = create_prompt(message) |
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device) |
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output_ids = model.generate(input_ids, max_length=50, num_return_sequences=1) |
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output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True) |
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return output_text |
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@spaces.GPU |
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def generate_story(character, setting, plot_twist, max_tokens, temperature, top_p): |
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"""Define the function to generate the story.""" |
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prompt = f"Write a short story with the following details:\nMain character: {character}\nSetting: {setting}\nPlot twist: {plot_twist}\n\nStory:" |
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device) |
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output_ids = model.generate(input_ids, max_length=max_tokens, num_return_sequences=1, temperature=temperature, top_p=top_p) |
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output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True) |
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return output_text |
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demo = gr.Interface( |
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fn=generate_story, |
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inputs=inputs, |
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outputs=outputs, |
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title="Short Story Generator", |
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description=description |
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) |
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if __name__ == "__main__": |
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demo.launch() |
