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Model: somrajmondal/phi3-mini-finance-lora-fp16 Source: Original Platform
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README.md
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README.md
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---
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base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- phi3
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- finance
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- lora
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- qlora
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- finetuned
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-generation
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---
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# 💹 Phi-3-mini Finance LoRA (fp16)
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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.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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---
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## 📋 Model Details
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| Field | Details |
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|---|---|
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| **Developed by** | [somrajmondal](https://huggingface.co/somrajmondal) |
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| **Base model** | unsloth/phi-3-mini-4k-instruct-bnb-4bit |
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| **Model type** | Causal Language Model (Phi-3 architecture) |
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| **Parameters** | 3.8B total / 29.8M trainable (0.78%) |
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| **Language** | English |
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| **License** | Apache 2.0 |
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| **Fine-tuning method** | LoRA (Low-Rank Adaptation) |
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| **Quantization** | 4-bit NF4 during training, merged to fp16 |
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---
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## 🎯 What This Model Does
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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:
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- Stock market concepts (P/E ratio, dividends, market cap)
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- Investment strategies (ETFs, mutual funds, dollar-cost averaging)
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- Fixed income (bonds, yields, interest rates)
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- Personal finance (compound interest, savings, budgeting)
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- Financial planning and portfolio diversification
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---
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## 🏋️ Training Details
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| Setting | Value |
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|---|---|
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| **Dataset** | [gbharti/finance-alpaca](https://huggingface.co/datasets/gbharti/finance-alpaca) |
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| **Training rows** | 5,000 |
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| **Epochs** | 2 |
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| **Total steps** | 1,250 |
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| **Batch size** | 2 (effective: 8 with grad accumulation) |
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| **Learning rate** | 2e-4 (cosine scheduler) |
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| **Optimizer** | AdamW 8-bit |
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| **LoRA rank (r)** | 16 |
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| **LoRA alpha** | 16 |
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| **LoRA dropout** | 0.05 |
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| **LoRA target modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
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| **Max sequence length** | 1024 |
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| **Final training loss** | 2.18 |
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| **Peak VRAM used** | 3.69 GB |
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| **Training hardware** | Google Colab Free T4 (15GB VRAM) |
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| **Training time** | ~2 hours 20 minutes |
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| **Framework** | Unsloth + HuggingFace TRL |
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---
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## 🚀 How to Use
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### Quick Start (Transformers)
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "somrajmondal/phi3-mini-finance-lora-fp16"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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question = "What is compound interest and why is it important?"
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prompt = f"""<|user|>
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{question}
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<|end|>
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<|assistant|>
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"""
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=300,
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do_sample=False,
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repetition_penalty=1.3,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.eos_token_id,
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)
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response = tokenizer.decode(
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outputs[0][inputs["input_ids"].shape[1]:],
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skip_special_tokens=True
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)
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print(response)
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```
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### With Unsloth (Faster Inference)
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```python
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from unsloth import FastLanguageModel
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import torch
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = "somrajmondal/phi3-mini-finance-lora-fp16",
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max_seq_length = 1024,
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dtype = None,
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load_in_4bit = True, # set False for full fp16
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)
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FastLanguageModel.for_inference(model)
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question = "What is the difference between a stock and a bond?"
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prompt = f"""<|user|>
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{question}
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<|end|>
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<|assistant|>
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"""
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(
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**inputs,
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max_new_tokens=300,
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do_sample=False,
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repetition_penalty=1.3,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.eos_token_id,
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)
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response = tokenizer.decode(
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outputs[0][inputs["input_ids"].shape[1]:],
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skip_special_tokens=True
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)
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print(response)
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```
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---
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## 💬 Prompt Format
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This model uses the **Phi-3 chat template**. Always wrap your input like this:
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```
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<|user|>
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Your finance question here
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<|end|>
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<|assistant|>
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```
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---
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## 📊 Example Outputs
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**Q: What is a P/E ratio?**
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> 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.
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**Q: What is dollar cost averaging?**
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> 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.
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---
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## ⚠️ Limitations
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- Trained on only 5,000 rows — may lack depth on niche financial topics
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- Not suitable for real financial advice — always consult a professional
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- May occasionally produce incomplete answers on complex multi-part questions
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- Training loss of 2.18 indicates room for improvement with more epochs/data
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---
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## 🔧 Recommended Inference Settings
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```python
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# For factual / accurate answers (recommended)
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do_sample = False
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repetition_penalty = 1.3
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max_new_tokens = 300
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# For more creative / detailed answers
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do_sample = True
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temperature = 0.3
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top_p = 0.9
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```
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---
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## 📦 Training Framework
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- [Unsloth](https://github.com/unslothai/unsloth) — 2x faster training, 60% less VRAM
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- [HuggingFace TRL](https://github.com/huggingface/trl) — SFTTrainer
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- [PEFT](https://github.com/huggingface/peft) — LoRA adapters
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- [BitsAndBytes](https://github.com/TimDettmers/bitsandbytes) — 4-bit NF4 quantization
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---
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## 📄 Citation
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If you use this model, please cite the base model and dataset:
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```bibtex
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@misc{phi3-mini-finance-lora,
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author = {somrajmondal},
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title = {Phi-3-mini Finance LoRA},
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year = {2025},
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publisher = {HuggingFace},
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url = {https://huggingface.co/somrajmondal/phi3-mini-finance-lora-fp16}
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}
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```
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