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import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
# Load the base model and the fine-tuned model
@st.cache_resource
def load_model():
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-3B-Instruct")
model = PeftModel.from_pretrained(base_model, "mohamedyd/Natural-Coder-3B-Instruct-V1")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-3B-Instruct")
return model, tokenizer
model, tokenizer = load_model()
# Streamlit app
st.title("Natural-Coder-3B-Instruct-V1 Model Interaction")
# Text input for user prompt
user_input = st.text_area("Enter your prompt here:", height=150)
# Button to generate response
if st.button("Generate Response"):
if user_input:
# Tokenize the input
inputs = tokenizer(user_input, return_tensors="pt")
# Generate response
outputs = model.generate(**inputs, max_length=512, num_return_sequences=1)
# Decode the output
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Display the response
st.write("Model Response:")
st.write(response)
else:
st.write("Please enter a prompt to generate a response.") |