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import os
import torch
import pandas as pd
import numpy as np
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
# Set the Hugging Face home directory
os.environ['HF_HOME'] = '/app/.cache'
# Load the base model with device_map set to 'auto'
model = AutoModelForCausalLM.from_pretrained(
"SHASWATSINGH3101/Qwen2-0.5B-Instruct_lora_merge",
device_map='auto'
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("SHASWATSINGH3101/Qwen2-0.5B-Instruct_lora_merge")
tokenizer.pad_token = tokenizer.eos_token
def gen(model, p, maxlen=100, sample=True):
toks = tokenizer(p, return_tensors="pt").to(model.device)
res = model.generate(**toks, max_new_tokens=maxlen, do_sample=sample,
num_return_sequences=1, temperature=0.1, num_beams=1, top_p=0.95)
return tokenizer.batch_decode(res, skip_special_tokens=True)
def generate_letter(prompt):
seed = 42
set_seed(seed)
in_data = f"Instruct: {prompt}\n{prompt}\nOutput:\n"
# Generate response
peft_model_res = gen(model, in_data, 259)
peft_model_output = peft_model_res[0].split('Output:\n')[1]
# Extract the relevant parts of the output
prefix, success, result = peft_model_output.partition('#End')
return prefix.strip()
# Create Gradio interface
iface = gr.Interface(
fn=generate_letter,
inputs=gr.Textbox(lines=2, placeholder="Enter your prompt here..."),
outputs="text",
title="Letter Generator",
description="Generate a letter informing someone of potential legal action due to a dispute or violation.",
flagging_dir="/app/flagged" # Set the flagging directory
)
# Launch the app
iface.launch(server_name="0.0.0.0", server_port=7860) |