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7ac9447 b3bf1c8 7ac9447 b3bf1c8 7ac9447 b3bf1c8 7ac9447 b3bf1c8 7ac9447 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
import gradio as gr
base_model_name = "meta-llama/Llama-2-7b-chat-hf"
adapter_name = "mostafa33/llama2-ecommerce-lora2"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
)
# Load model
base_model = AutoModelForCausalLM.from_pretrained(
base_model_name,
quantization_config=bnb_config,
device_map="auto"
)
model = PeftModel.from_pretrained(base_model, adapter_name)
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
tokenizer.pad_token = tokenizer.eos_token
def generate_response(prompt):
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=256,
temperature=0.7,
top_p=0.9,
do_sample=True,
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Gradio Interface
demo = gr.Interface(
fn=generate_response,
inputs=gr.Textbox(lines=4, label="Enter your product or query"),
outputs=gr.Textbox(label="Model Response"),
title="🦙 LLaMA2 LoRA Ecommerce Chatbot",
description="Fine-tuned LoRA model for ecommerce text generation and chat.",
)
demo.launch()
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