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app.py

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+# prompt: create a sreamlit app on finetuned llm
+
+import streamlit as st
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+
+# 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.")

requirements.txt

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+streamlit
+git+https://github.com/huggingface/[email protected]
+transformers
+torch