phi3-mini-finance-lora-fp16/README.md

---
base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- phi3
- finance
- lora
- qlora
- finetuned
license: apache-2.0
language:
- en
pipeline_tag: text-generation
---

# 💹 Phi-3-mini Finance LoRA (fp16)

A domain-specialized version of Microsoft's **Phi-3-mini-4k-instruct**, fine-tuned on financial Q&A data using **LoRA + 4-bit NF4 quantization** — trained entirely on a free Google Colab T4 GPU.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

---

## 📋 Model Details

| Field | Details |
|---|---|
| **Developed by** | [somrajmondal](https://huggingface.co/somrajmondal) |
| **Base model** | unsloth/phi-3-mini-4k-instruct-bnb-4bit |
| **Model type** | Causal Language Model (Phi-3 architecture) |
| **Parameters** | 3.8B total / 29.8M trainable (0.78%) |
| **Language** | English |
| **License** | Apache 2.0 |
| **Fine-tuning method** | LoRA (Low-Rank Adaptation) |
| **Quantization** | 4-bit NF4 during training, merged to fp16 |

---

## 🎯 What This Model Does

This model is fine-tuned to answer **finance and investment questions** clearly and accurately. It was trained on the `gbharti/finance-alpaca` dataset covering topics like:

- Stock market concepts (P/E ratio, dividends, market cap)
- Investment strategies (ETFs, mutual funds, dollar-cost averaging)
- Fixed income (bonds, yields, interest rates)
- Personal finance (compound interest, savings, budgeting)
- Financial planning and portfolio diversification

---

## 🏋️ Training Details

| Setting | Value |
|---|---|
| **Dataset** | [gbharti/finance-alpaca](https://huggingface.co/datasets/gbharti/finance-alpaca) |
| **Training rows** | 5,000 |
| **Epochs** | 2 |
| **Total steps** | 1,250 |
| **Batch size** | 2 (effective: 8 with grad accumulation) |
| **Learning rate** | 2e-4 (cosine scheduler) |
| **Optimizer** | AdamW 8-bit |
| **LoRA rank (r)** | 16 |
| **LoRA alpha** | 16 |
| **LoRA dropout** | 0.05 |
| **LoRA target modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| **Max sequence length** | 1024 |
| **Final training loss** | 2.18 |
| **Peak VRAM used** | 3.69 GB |
| **Training hardware** | Google Colab Free T4 (15GB VRAM) |
| **Training time** | ~2 hours 20 minutes |
| **Framework** | Unsloth + HuggingFace TRL |

---

## 🚀 How to Use

### Quick Start (Transformers)

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "somrajmondal/phi3-mini-finance-lora-fp16"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)

question = "What is compound interest and why is it important?"

prompt = f"""<|user|>
{question}
<|end|>
<|assistant|>
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=300,
    do_sample=False,
    repetition_penalty=1.3,
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.eos_token_id,
)

response = tokenizer.decode(
    outputs[0][inputs["input_ids"].shape[1]:],
    skip_special_tokens=True
)
print(response)
```

### With Unsloth (Faster Inference)

```python
from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name     = "somrajmondal/phi3-mini-finance-lora-fp16",
    max_seq_length = 1024,
    dtype          = None,
    load_in_4bit   = True,   # set False for full fp16
)
FastLanguageModel.for_inference(model)

question = "What is the difference between a stock and a bond?"

prompt = f"""<|user|>
{question}
<|end|>
<|assistant|>
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(
    **inputs,
    max_new_tokens=300,
    do_sample=False,
    repetition_penalty=1.3,
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.eos_token_id,
)
response = tokenizer.decode(
    outputs[0][inputs["input_ids"].shape[1]:],
    skip_special_tokens=True
)
print(response)
```

---

## 💬 Prompt Format

This model uses the **Phi-3 chat template**. Always wrap your input like this:

```
<|user|>
Your finance question here
<|end|>
<|assistant|>
```

---

## 📊 Example Outputs

**Q: What is a P/E ratio?**
> The price-to-earnings (P/E) ratio measures a company's current share price relative to its earnings per share. A high P/E suggests investors expect future growth, while a low P/E may indicate an undervalued stock or slower expected growth.

**Q: What is dollar cost averaging?**
> Dollar cost averaging is an investment strategy where you invest a fixed amount of money at regular intervals, regardless of market conditions. This reduces the impact of volatility and removes the need to time the market.

---

## ⚠️ Limitations

- Trained on only 5,000 rows — may lack depth on niche financial topics
- Not suitable for real financial advice — always consult a professional
- May occasionally produce incomplete answers on complex multi-part questions
- Training loss of 2.18 indicates room for improvement with more epochs/data

---

## 🔧 Recommended Inference Settings

```python
# For factual / accurate answers (recommended)
do_sample          = False
repetition_penalty = 1.3
max_new_tokens     = 300

# For more creative / detailed answers
do_sample   = True
temperature = 0.3
top_p       = 0.9
```

---

## 📦 Training Framework

- [Unsloth](https://github.com/unslothai/unsloth) — 2x faster training, 60% less VRAM
- [HuggingFace TRL](https://github.com/huggingface/trl) — SFTTrainer
- [PEFT](https://github.com/huggingface/peft) — LoRA adapters
- [BitsAndBytes](https://github.com/TimDettmers/bitsandbytes) — 4-bit NF4 quantization

---

## 📄 Citation

If you use this model, please cite the base model and dataset:

```bibtex
@misc{phi3-mini-finance-lora,
  author    = {somrajmondal},
  title     = {Phi-3-mini Finance LoRA},
  year      = {2025},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/somrajmondal/phi3-mini-finance-lora-fp16}
}
```