vdpappu/lora_stock_analysis

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
from peft import PeftModel
from typing import Optional
import time
import os

def generate_prompt(input_text: str, instruction: Optional[str] = None) -> str:
    text = f"### Question: {input_text}\n\n### Answer: "
    if instruction:
        text = f"### Instruction: {instruction}\n\n{text}"
    return text

huggingface_token = os.environ.get('HUGGINGFACE_TOKEN')

base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", token=huggingface_token)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b", token=huggingface_token)

lora_model = PeftModel.from_pretrained(base_model, "vdpappu/lora_stock_analysis")
merged_model = lora_model.merge_and_unload()

eos_token = '<eos>'
eos_token_id = tokenizer.encode(eos_token, add_special_tokens=False)[-1]

generation_config = GenerationConfig(
       eos_token_id=tokenizer.eos_token_id,
       min_length=5,
       max_length=200,
       do_sample=True,
       temperature=0.7,
       top_p=0.9,
       top_k=50,
       repetition_penalty=1.5,
       no_repeat_ngram_size=3,
       early_stopping=True
   )

question = """Assume the role as a seasoned stock option analyst with a strong track record in dissecting intricate option data to discern valuable
              insights into stock sentiment. Proficient in utilizing advanced statistical models and data visualization techniques to forecast
              market trends and make informed trading decisions. Adept at interpreting option Greeks, implied volatility, .. """
prompt = generate_prompt(input_text=question)

with torch.no_grad():
    inputs = tokenizer(prompt, return_tensors="pt")
    output = merged_model.generate(**inputs, generation_config=generation_config)
    response = tokenizer.decode(output[0], skip_special_tokens=True)

print(response)