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import gradio as gr
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
# Repos
BASE_MODEL = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"
ADAPTER_REPO = "cheberle/autotrain-35swc-b4r9z"
# 1. Load the PEFT config to confirm the base model
peft_config = PeftConfig.from_pretrained(ADAPTER_REPO)
print("PEFT Base Model:", peft_config.base_model_name_or_path)
# 2. Load the tokenizer & base model
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(
BASE_MODEL,
revision="4831ee1375be5b4ff5a4abf7984e13628db44e35",
ignore_mismatched_sizes=True,
trust_remote_code=True,
device_map="auto",
)
# 3. Load your LoRA adapter weights onto the base model
model = PeftModel.from_pretrained(
base_model,
ADAPTER_REPO,
ignore_mismatched_sizes=True, # Add this parameter
)
def classify_text(text):
"""
Simple prompting approach: we ask the model to return a single classification label
(e.g., 'positive', 'negative', etc.).
You can refine this prompt, add chain-of-thought, or multiple classes as needed.
"""
prompt = f"Below is some text.\nText: {text}\nPlease classify the sentiment (positive or negative):"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=64)
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
return answer
with gr.Blocks() as demo:
gr.Markdown("## Qwen + LoRA Adapter: Text Classification Demo")
input_box = gr.Textbox(lines=3, label="Enter text")
output_box = gr.Textbox(lines=3, label="Model's generated output (classification)")
classify_btn = gr.Button("Classify")
classify_btn.click(fn=classify_text, inputs=input_box, outputs=output_box)
if __name__ == "__main__":
demo.launch() |