InsureLLM-4B

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

Evaluation Results

Domain Knowledge (8-prompt rubric):

Generation Quality:

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)

Learn How This Model Works

New to ML or insurance AI? We wrote a plain-English guide that explains the concepts, the data, and the actual training code behind every INSUREOS model — from tabular models to this LLM:

• Learning guide: LEARN.md
• Training code: piyushptiwari/insureos-models — this model is trained by training/qlora_finetune.py and training/dpo_train.py

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:

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.