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# prompt: create a sreamlit app on finetuned llm
import streamlit as st
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
# Load Base Gemma Model (Required for Adapter)
base_model_path = "google/gemma-3-1b-it"
model = AutoModelForCausalLM.from_pretrained(
base_model_path,
load_in_4bit=True, # Efficient memory usage
device_map="auto" # Automatically maps to GPU if available
)
# Load LoRA Adapter
model = PeftModel.from_pretrained(model, "fine_tuned_gemma_3_1b/")
# Load the fine-tuned model and tokenizer
# model_path = "fine_tuned_gemma_3_1b" # Replace with the actual path to your model directory
tokenizer = AutoTokenizer.from_pretrained(model_path)
# model = AutoModelForCausalLM.from_pretrained(model_path)
# Create a text generation pipeline
text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
st.title("Fine-tuned Gemma 3.1B LLM")
# Create a text input box for the user
user_input = st.text_area("Enter your prompt:")
if st.button("Generate Text"):
if user_input:
# Generate text based on user input
output = text_generator(user_input, max_length=150, num_return_sequences=1)
generated_text = output[0]['generated_text']
# Display the generated text
st.write("Generated Text:")
st.write(generated_text)
else:
st.warning("Please enter a prompt.") |