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Model: somrajmondal/phi3-mini-finance-lora-fp16
Source: Original Platform

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README.md

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+---
+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}
+}
+```

chat_template.jinja

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+{% for message in messages %}{% if message['role'] == 'system' %}{{'<|system|>
+' + message['content'] + '<|end|>
+'}}{% elif message['role'] == 'user' %}{{'<|user|>
+' + message['content'] + '<|end|>
+'}}{% elif message['role'] == 'assistant' %}{{'<|assistant|>
+' + message['content'] + '<|end|>
+'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>
+' }}{% else %}{{ eos_token }}{% endif %}

config.json

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+    "architectures": [
+        "MistralForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bos_token_id": 1,
+    "torch_dtype": "float16",
+    "eos_token_id": 32000,
+    "head_dim": 96,
+    "hidden_act": "silu",
+    "hidden_size": 3072,
+    "initializer_range": 0.02,
+    "intermediate_size": 8192,
+    "max_position_embeddings": 4096,
+    "model_type": "mistral",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 32,
+    "num_key_value_heads": 32,
+    "pad_token_id": 32009,
+    "rms_norm_eps": 1e-05,
+    "rope_parameters": {
+        "rope_theta": 10000.0,
+        "rope_type": "default"
+    },
+    "sliding_window": 2048,
+    "tie_word_embeddings": false,
+    "unsloth_version": "2026.5.2",
+    "use_cache": false,
+    "vocab_size": 32064
+}

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tokenizer_config.json

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+}