pythia-financial-lora

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Model Details

Model Descriptio

• Developed by: [Haq Nawaz Malik]
• Model type: [Lora_adapter]
• Language(s) (NLP): [Text_gen_for_financial_purposes]
• Finetuned from model : [EleutherAI/pythia-410m]

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Direct Use

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/pythia-410m")
tokenizer.pad_token = tokenizer.eos_token

# Load base model
base_model = AutoModelForCausalLM.from_pretrained("EleutherAI/pythia-410m").eval().to("cuda" if torch.cuda.is_available() else "cpu")

# Load LoRA fine-tuned adapter from Hugging Face Hub
lora_model = PeftModel.from_pretrained(
    base_model,
    "Omarrran/pythia-financial-lora"
).eval().to(base_model.device)

# Define prompt
prompt = "### Instruction:\n What are Tesla's main risk factors?\n\n### Response:\n"
inputs = tokenizer(prompt, return_tensors="pt").to(base_model.device)

# Generate from base model
with torch.no_grad():
    base_output = base_model.generate(
        **inputs,
        max_new_tokens=1000,
        do_sample=True,
        temperature=0.7,
        top_p=0.95,
        repetition_penalty=1.1,
        eos_token_id=tokenizer.eos_token_id
    )

# Generate from fine-tuned model
with torch.no_grad():
    lora_output = lora_model.generate(
        **inputs,
        max_new_tokens=1000,
        do_sample=True,
        temperature=0.7,
        top_p=0.95,
        repetition_penalty=1.1,
        eos_token_id=tokenizer.eos_token_id
    )

# Decode responses
base_text = tokenizer.decode(base_output[0], skip_special_tokens=True)
lora_text = tokenizer.decode(lora_output[0], skip_special_tokens=True)

# Clean output (remove prompt part)
base_response = base_text.split("### Response:")[-1].strip()
lora_response = lora_text.split("### Response:")[-1].strip()

# Display both outputs
print("\n" + "="*80)
print("📉 BEFORE Fine-Tuning (Base Pythia Model)")
print("="*80)
print(format_response(base_response))

print("\n" + "="*80)
print("📈 AFTER Fine-Tuning (LoRA Adapter from Hugging Face)")
print("="*1180)
print(format_response(lora_response))

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