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---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Requirements
```python
!pip install gradio
!pip install -U xformers --index-url https://download.pytorch.org/whl/cu121
!pip install "unsloth[kaggle-new] @ git+https://github.com/unslothai/unsloth.git"
import os
os.environ["WANDB_DISABLED"] = "true"
```
## Gradio App
```python
import gradio as gr
from transformers import AutoTokenizer
from peft import AutoPeftModelForCausalLM
import torch
# Assuming the model and tokenizer are correctly set up as per your provided code.
def summarize_text(text):
load_in_4bit = True
model = AutoPeftModelForCausalLM.from_pretrained(
"DisgustingOzil/Mistral_summarizer",
load_in_4bit=load_in_4bit,
torch_dtype=torch.float16,
).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("DisgustingOzil/Mistral_summarizer")
summary_prompt = f"""Below is a text that needs to be summarized. Based on the input, write a good summary which summarize all main points.
### Text:
{text}
### Summary:
""" # The summary part is left empty for generation
inputs = tokenizer([summary_prompt], return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200, use_cache=True)
summary = tokenizer.batch_decode(outputs, skip_special_tokens=True)
summary_start_index = summary[0].find("### Summary:")
summary_text = summary[0][summary_start_index:].replace("### Summary:", "").strip()
return summary_text
# Define the Gradio interface
iface = gr.Interface(
fn=summarize_text,
inputs=gr.Textbox(lines=10, label="Input Text"),
outputs=gr.Textbox(label="Summary"),
title="Text Summarization",
description="Enter text to summarize based on Maxwell's equations and related concepts."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
```
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