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Emar7
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Model Details
Model Provider
Emar7
Model Tree
microsoft/Phi-4-mini-instruct
Input Modalities
Output Modalities
Supported Functionality
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% |
| 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 |
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport 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?"))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)
Emmanuel Nwanguma — ML Engineer
Fine-tuned as a portfolio project demonstrating the full MLOps lifecycle:
data → QLoRA training → evaluation → deployment.
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport 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?"))