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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
# Replace with your model name
#MODEL_NAME = "ssirikon/Gemma7b-bnb-Unsloth"
#MODEL_NAME = "unsloth/gemma-7b-bnb-4bit"
MODEL_NAME = "unsloth/mistral-7b-bnb-4bit"
# Load the model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
device_map="auto",
torch_dtype=torch.float16,
load_in_4bit=True, # Load the model in 4-bit precision
# Removed the unsupported argument
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
# **Change 1: Set `llm_int8_skip_modules` to avoid deep copy**
#model.quantization_config.llm_int8_skip_modules = ['lm_head']
# Create a pipeline for text generation
generator = pipeline(
task="summarization",
model=model,
tokenizer=tokenizer,
max_new_tokens=50, # Adjust as needed
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
def generate_text(email):
result = generator("Generate a subject line for the following email.\n"+email)
return result[0]["generated_text"]
# Create a Gradio interface
demo = gr.Interface(
fn=generate_text,
inputs=gr.Textbox(lines=5, label="Enter your Email here:"),
outputs=gr.Textbox(label="Generated Subject"),
title="Email Subject Generation demo",
description="Enter an email and let the model generate the subject for you!",
)
demo.launch(debug=True) |