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import streamlit as st
@st.cache_resource(show_spinner="Loading Model & Tokenizer")
def load_model():
# This is cached and will not run again and again.
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained(
"unsloth/Qwen2.5-0.5B", device_map="cpu", torch_dtype=torch.bfloat16)
m = PeftModel.from_pretrained(base_model, "mosama/Qwen2.5-0.5B-Pretraining-ar-eng-urd-LoRA-Adapters")
merged_model = m.merge_and_unload()
tokenizer = AutoTokenizer.from_pretrained("mosama/Qwen2.5-0.5B-Pretrained-ar-end-urd-500")
st.success('Model & Tokenizer Loaded Successfully!', icon="✅")
return merged_model, tokenizer
st.title("Qwen2.5-0.5B Arabic, English & Urdu Continuous Pretrained")
model, tokenizer = load_model()
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if not st.session_state.messages:
with st.chat_message("assistant", avatar="assistant"):
st.write("Hello 👋 I am an AI bot powered by Qwen 2.5 0.5B model.")
st.session_state.messages.append({"role": "assistant", "content": "Hello 👋 I am an AI bot powered by Qwen 2.5 0.5B model."})
st.session_state.state_chat_input = False
if prompt := st.chat_input("Say Something", key="input_1", disabled=st.session_state.state_chat_input):
# Display user message in chat message container
with st.chat_message("user"):
st.markdown(prompt)
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
if prompt or st.session_state.state_chat_input:
if st.session_state.state_chat_input:
with st.spinner(text="Generating response..."):
model_inputs = tokenizer(st.session_state.messages[-1]['content'], return_tensors="pt").to(model.device)
print(model_inputs)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=50,
repetition_penalty=1.2,
temperature=0.5,
do_sample=True,
top_p=0.9,
top_k=20
)
print("Generated Response!")
response = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
# Display assistant response in chat message container
with st.chat_message("assistant"):
st.markdown(response)
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": response})
st.session_state.state_chat_input = False
st.rerun()
else:
st.session_state.state_chat_input = True
st.rerun()
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