metadata
language:
- en
license: apache-2.0
tags:
- insurance
- uk-insurance
- llm
- qwen3
- qlora
- dpo
- fine-tuned
- text-generation
- claims
- underwriting
- bytical
library_name: transformers
pipeline_tag: text-generation
base_model: Qwen/Qwen3-4B
datasets:
- piyushptiwari/insureos-training-data
model-index:
- name: InsureLLM-4B
results:
- task:
type: text-generation
name: Insurance Domain QA
metrics:
- type: rouge1
value: 0.384
name: ROUGE-1
- type: rougeL
value: 0.199
name: ROUGE-L
- type: custom
value: 0.25
name: Domain Score (8-prompt rubric)
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:
- QLoRA Fine-tuning — 10,000 synthetic insurance SFT pairs covering claims, underwriting, regulation, pricing, and market structure
- DPO Alignment — 5,000 preference pairs teaching the model to prefer accurate, regulatory-compliant responses
- 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.