InsureLLM-4B — Insurance Domain Language Model

Created by Bytical AI — AI agents that run insurance operations.

Model Description

InsureLLM-4B is a domain-specific language model fine-tuned for the UK and European insurance industry. Built on Qwen3-4B, it has been trained through a 3-stage pipeline:

1. QLoRA Fine-tuning — 10,000 synthetic insurance SFT pairs covering claims, underwriting, regulation, pricing, and market structure
2. DPO Alignment — 5,000 preference pairs teaching the model to prefer accurate, regulatory-compliant responses
3. Real-World Data Fine-tuning — 3,685 SFT pairs from Wikipedia, UK legislation, HuggingFace insurance datasets, RSS feeds, and educational sources

Training Details

Parameter	Value
Base Model	Qwen/Qwen3-4B
Method	QLoRA (4-bit NF4) → DPO → Real-World QLoRA
LoRA Rank	64
LoRA Alpha	128
Learning Rate	2e-4 (QLoRA), 5e-7 (DPO), 2e-4 (Real-World)
Epochs	2 per stage
Sequence Length	1024
Batch Size	2 (gradient accumulation 4)
Optimizer	AdamW (paged, 8-bit)
GPU	NVIDIA Tesla T4 16GB
Total Training Time	~20 hours across 3 stages

Evaluation Results

Domain Knowledge (8-prompt rubric):

Topic	Score
FCA Consumer Duty	0.00
GDPR Data Protection	0.00
Claims Process	0.60
Fraud Indicators	0.25
Lloyd's Market	0.20
Pricing Fairness	0.25
Subrogation	0.50
Renewal Transparency	0.20
Average	0.25

Generation Quality:

Metric	Score
ROUGE-1	0.384
ROUGE-2	0.109
ROUGE-L	0.199

Intended Use

• Insurance domain question answering
• Claims process guidance
• Underwriting knowledge retrieval
• UK/EU regulatory compliance queries
• Insurance terminology explanation
• Part of a RAG pipeline for insurance operations

Limitations

• 4B parameter model — smaller models may not reliably produce exact regulatory terminology
• Best used with RAG (retrieval-augmented generation) using the companion InsureSearch engine
• Trained primarily on UK insurance context; may be less accurate for other jurisdictions
• Not a substitute for professional insurance or legal advice

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("piyushptiwari/InsureLLM-4B")
tokenizer = AutoTokenizer.from_pretrained("piyushptiwari/InsureLLM-4B")

messages = [
    {"role": "user", "content": "Explain the subrogation process in UK motor insurance."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
# Inject thinking tags to prevent infinite thinking loop
text += "<think>\n</think>\n"

inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)
response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)

Part of the INSUREOS Model Suite

This model is part of the INSUREOS — a complete AI/ML suite for insurance operations built by Bytical AI:

Model	Task	Metric
InsureLLM-4B (this model)	Insurance domain LLM	ROUGE-1: 0.384
InsureDocClassifier	12-class document classification	F1: 1.0
InsureNER	13-entity Named Entity Recognition	F1: 1.0
InsureFraudNet	Fraud detection (Motor/Property/Liability)	AUC-ROC: 1.0
InsurePricing	Insurance pricing (GLM + EBM)	MAE: £11,132
InsureSearch	Hybrid search engine (Vector + BM25)	33K docs indexed

Citation

@misc{bytical2026insurellm,
  title={InsureLLM-4B: A Domain-Specific Language Model for UK Insurance},
  author={Bytical AI},
  year={2026},
  url={https://huggingface.co/piyushptiwari/InsureLLM-4B}
}

About Bytical AI

Bytical builds AI agents that run insurance operations — claims automation, underwriting intelligence, digital sales, and core system modernization for insurers across the UK and Europe. Microsoft AI Partner | NVIDIA | Salesforce.