| import gradio as gr |
| import torch |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| from peft import PeftModel |
|
|
| |
| base_model_id = "microsoft/phi-2" |
| adapter_model_id = "username_kamu/Deeper-Logic-Phi2" |
|
|
| |
| tokenizer = AutoTokenizer.from_pretrained(base_model_id) |
| tokenizer.pad_token = tokenizer.eos_token |
|
|
| model = AutoModelForCausalLM.from_pretrained( |
| base_model_id, |
| torch_dtype=torch.float16, |
| device_map="auto", |
| trust_remote_code=True |
| ) |
|
|
| |
| model = PeftModel.from_pretrained(model, adapter_model_id) |
|
|
| def predict(message, history): |
| prompt = f"Instruct: {message}\nOutput:" |
| inputs = tokenizer(prompt, return_tensors="pt").to("cuda") |
| |
| with torch.no_grad(): |
| outputs = model.generate( |
| **inputs, |
| max_new_tokens=200, |
| temperature=0.7, |
| do_sample=True, |
| pad_token_id=tokenizer.eos_token_id |
| ) |
| |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| return response.split("Output:")[-1].strip() |
|
|
| |
| demo = gr.ChatInterface( |
| fn=predict, |
| title="Deeper-Logic AI", |
| description="Asisten Riset & Produktivitas Berbasis Phi-2 (Fine-tuned)", |
| theme="soft" |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
