|
from collections import namedtuple |
|
|
|
import spaces |
|
import gradio as gr |
|
import torch |
|
from transformers import AutoTokenizer, AutoModelForCausalLM |
|
|
|
title = """# Minitron Story Generator""" |
|
description = """ |
|
# Minitron |
|
|
|
Minitron is a family of small language models (SLMs) obtained by pruning [NVIDIA's](https://huggingface.co/nvidia) Nemotron-4 15B model, LLaMA3.1-8B or Mistral NeMO models. |
|
We prune model the number of transformer blocks, embedding size, attention heads, and MLP intermediate dimension, following which, we perform continued training with distillation to arrive at the final models. |
|
|
|
# Short Story Generator |
|
Welcome to the Short Story Generator! This application helps you create unique short stories based on your inputs. |
|
|
|
This application will show you the output of several models in the Minitron family. Outputs are shown side by side so you can compare them. |
|
|
|
**Instructions:** |
|
1. **Main Character:** Describe the main character of your story. For example, "a brave knight" or "a curious cat". |
|
2. **Setting:** Describe the setting where your story takes place. For example, "in an enchanted forest" or "in a bustling city". |
|
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". |
|
|
|
After filling in these details, click the "Submit" button, and a short story will be generated for you. |
|
""" |
|
|
|
inputs = [ |
|
gr.Textbox(label="Main Character", placeholder="e.g. a brave knight"), |
|
gr.Textbox(label="Setting", placeholder="e.g. in an enchanted forest"), |
|
gr.Textbox(label="Plot Twist", placeholder="e.g. discovers a hidden treasure"), |
|
gr.Slider(minimum=1, maximum=2048, value=64, step=1, label="Max new tokens"), |
|
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), |
|
gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"), |
|
] |
|
|
|
Model = namedtuple('Model', ['name', 'llm', 'tokenizer']) |
|
|
|
model_paths = [ |
|
"nvidia/Llama-3.1-Minitron-4B-Width-Base", |
|
"nvidia/Llama-3.1-Minitron-4B-Depth-Base", |
|
"nvidia/Mistral-NeMo-Minitron-8B-Base", |
|
] |
|
|
|
device='cuda' |
|
dtype=torch.bfloat16 |
|
|
|
|
|
models = [ |
|
Model( |
|
name=p.split("/")[-1], |
|
llm=AutoModelForCausalLM.from_pretrained(p, torch_dtype=dtype, device_map=device), |
|
tokenizer=AutoTokenizer.from_pretrained(p), |
|
) for p in model_paths |
|
] |
|
|
|
outputs = [ |
|
gr.Textbox(label=f"Generated Story ({model.name})") for model in models |
|
] |
|
|
|
|
|
def create_prompt(instruction): |
|
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:''' |
|
return PROMPT.format(instruction=instruction) |
|
|
|
|
|
@spaces.GPU |
|
def generate_story(character, setting, plot_twist, max_tokens, temperature, top_p): |
|
"""Define the function to generate the story.""" |
|
prompt = f"Write a short story with the following details:\nMain character: {character}\nSetting: {setting}\nPlot twist: {plot_twist}\n\nStory:" |
|
|
|
output_texts = [] |
|
|
|
for model in models: |
|
input_ids = model.tokenizer.encode(prompt, return_tensors="pt").to(model.llm.device) |
|
output_ids = model.llm.generate(input_ids, max_length=max_tokens, num_return_sequences=1, temperature=temperature, top_p=top_p) |
|
output_text = model.tokenizer.decode(output_ids[0], skip_special_tokens=True) |
|
output_texts.append(output_text[len(prompt):]) |
|
|
|
return output_texts |
|
|
|
|
|
|
|
demo = gr.Interface( |
|
fn=generate_story, |
|
inputs=inputs, |
|
outputs=outputs, |
|
title="Short Story Generator", |
|
description=description |
|
) |
|
|
|
if __name__ == "__main__": |
|
demo.launch() |
