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import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
from huggingface_hub import login
import os
# Load the base model and adapter
@st.cache_resource
def load_model():
base_model_name = "Qwen/Qwen2.5-Coder-3B-Instruct" # Ensure this is the correct base model
adapter_model_name = "mohamedyd/Natural-Coder-3B-Instruct-V1"
# Load the base model
base_model = AutoModelForCausalLM.from_pretrained(base_model_name, trust_remote_code=True)
# Load the configuration
config = PeftConfig.from_pretrained(adapter_model_name)
# Load the PEFT adapter on top of the base model
model = PeftModel.from_pretrained(base_model, adapter_model_name, config=config)
# Load tokenizer from base model
tokenizer = AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True)
return model, tokenizer
model, tokenizer = load_model()
# Streamlit App UI
st.title("Natural-Coder-3B-Instruct-V1 Model Interaction")
user_input = st.text_area("Enter your prompt here:", height=150)
if st.button("Generate Response"):
if user_input:
inputs = tokenizer(user_input, return_tensors="pt")
outputs = model.generate(**inputs, max_length=512, num_return_sequences=1)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
st.write("Model Response:")
st.write(response)
else:
st.write("Please enter a prompt to generate a response.") |