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---
license: mit
datasets:
- virattt/financial-qa-10K
language:
- en
metrics:
- accuracy
base_model:
- EleutherAI/pythia-410m
pipeline_tag: text-generation
tags:
- finance
---
---
--
## 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]
]
### 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))
```
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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