Emar7/phi4-finance-finetuned

Phi-4 Mini — Financial Analyst (Fine-Tuned)

A Phi-4 Mini 3.8B model fine-tuned on SEC 10-K financial Q&A data using QLoRA, transforming a general-purpose LLM into a focused financial analyst capable of answering questions about earnings reports, balance sheets, risk disclosures, and business performance.

Evaluation Results

Fine-tuned vs base Phi-4 Mini on 100 held-out test samples from virattt/financial-qa-10K:

Metric	Base Model	Fine-Tuned	Improvement
ROUGE-1	0.4657	0.7523	+61.6%
ROUGE-2	0.3560	0.6106	+71.5%
ROUGE-L	0.4242	0.7168	+69.0%

Training Details

Setting	Value
Base model	microsoft/Phi-4-mini-instruct
Dataset	virattt/financial-qa-10K
Training samples	~6,300
Method	QLoRA (4-bit quantization + LoRA adapters)
LoRA rank	16
LoRA alpha	32
Epochs	3
Learning rate	2e-4
Optimizer	paged_adamw_8bit
Hardware	2x NVIDIA Tesla T4 (Kaggle)
Training time	~4 hours

How to Use

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

base_model_id = "microsoft/Phi-4-mini-instruct"
adapter_id    = "Emar7/phi4-finance-finetuned"

tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)

model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(model, adapter_id)
model.eval()

def ask(question, context=""):
    user_content = f"{question}\n\nContext: {context}" if context else question
    prompt = f"""<|system|>
You are an expert financial analyst. Answer questions about company financials, 
earnings reports, and business performance accurately and concisely.<|end|>
<|user|>
{user_content}<|end|>
<|assistant|>
"""
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=256,
            temperature=0.1,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id,
        )
    return tokenizer.decode(
        outputs[0][inputs["input_ids"].shape[1]:],
        skip_special_tokens=True
    ).strip()

print(ask("What does a high debt-to-equity ratio indicate?"))

Example Output

Question: What does it mean when revenue grows but net income declines?

Base model: "When a company's revenue grows but its net income declines, it could mean..." (generic, unfocused)

Fine-tuned model: "This pattern typically indicates rising operating costs outpacing revenue growth. Key areas to investigate in the 10-K include: cost of goods sold trends, SG&A expense increases, higher interest expense from increased leverage, or one-time charges impacting the bottom line. Analysts refer to this as 'margin compression' and it warrants scrutiny of the company's pricing power and cost management." (structured, analyst-quality)

Project Links

• Training notebook: Kaggle
• GitHub repo: (coming soon)

Intended Use & Limitations

• Intended for: Financial Q&A, document analysis, earnings report interpretation
• Not intended for: Real investment advice or trading decisions
• Limitations: Trained on 10-K data only; performance on other financial document types (10-Q, 8-K) may vary
• Base model cutoff: June 2024

Author

Emmanuel Nwanguma — ML Engineer
Fine-tuned as a portfolio project demonstrating the full MLOps lifecycle: data → QLoRA training → evaluation → deployment.