bhaiyahnsingh45/bhaiya-loan-assistant-lora

Bhaiya & Company — Loan Assistant (LoRA Adapter)

Fine-tuned Qwen/Qwen2.5-3B-Instruct using LoRA for banking loan assistance.

Model Details

Property	Value
Base Model	Qwen/Qwen2.5-3B-Instruct
Method	LoRA (Low-Rank Adaptation)
Rank	16
Alpha	32
Target Modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Training Precision	bf16
Quantization	4-bit (BitsAndBytes NF4)
Trainable Params	~29M / 3.1B (0.96%)

Training Details

Property	Value
Instance	AWS SageMaker ml.g5.2xlarge (NVIDIA A10G 22 GB)
Epochs	5
Batch Size	2 (gradient accumulation 8, effective 16)
Learning Rate	2e-4 (cosine schedule)
Optimizer	AdamW 8-bit
Best Epoch	3 (val loss 0.1838)

Training Loss

Epoch	Training Loss	Validation Loss
1	0.3478	0.2551
2	0.1432	0.1891
3	0.1020	0.1838
4	0.0835	0.1930

Usage

Load with PEFT (recommended)

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

base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-3B-Instruct",
    torch_dtype=torch.float16,
    device_map="auto",
)
model = PeftModel.from_pretrained(base_model, "bhaiyahnsingh45/bhaiya-loan-assistant-lora")
model.eval()

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-3B-Instruct")

messages = [
    {"role": "system", "content": "You are a compliant and helpful loan assistant for Bhaiya & Company — Banking & Finance Division."},
    {"role": "user", "content": "My salary is 55,000, CIBIL 740, age 30. Am I eligible for a personal loan?"},
]

inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        input_ids=inputs["input_ids"],
        max_new_tokens=512,
        temperature=0.3,
        do_sample=True
    )

generated_tokens = outputs[0][inputs["input_ids"].shape[1]:]

print(tokenizer.decode(generated_tokens, skip_special_tokens=True))

Intended Use

This model is a loan assistant for Bhaiya & Company — Banking & Finance Division. It handles:

• Loan eligibility checks (Personal, Business, Home loans)
• Document requirement guidance
• EMI calculations
• Application process guidance

Limitations

• Never guarantees loan approval
• Refuses investment advice, stock tips, financial planning
• Final approval is always subject to verification

Dataset

Trained on bhaiyahnsingh45/bhaiya-loan-assistant-dataset — a custom instruction-tuning dataset in chat format (system/user/assistant messages).