h3ir/morbi-v022-lora

Morbi v0.2.2

Professional Domain Expert for Insurance, Actuarial Science, Accounting & Legal

A specialized AI assistant trained on 19,000+ examples across insurance, actuarial science, accounting (CPA/CFA), and legal (Bar Exam) domains.

HEIR Platform | Documentation | API Access

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Model Description

Morbi is a domain-specialized large language model built for professional financial services applications. Named after the actuarial term for mortality/morbidity analysis, Morbi provides expert-level assistance across interconnected professional domains.

Attribute	Value
Developer	HEIR
Model Type	Causal Language Model (LoRA Adapter)
Base Model	Mistral-Small-Instruct-2409 (22B parameters)
Language	English
License	Apache 2.0
Version	0.2.2 (January 2026)

What's New in v0.2.2

• Accounting Domain: Added CPA exam topics (FAR, AUD, REG, BEC), GAAP/IFRS standards, and financial calculations
• Legal Domain: Added California Bar Exam subjects, UCC Article 2, and case analysis capabilities
• Improved Training: Lower learning rate with cosine schedule for better convergence

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Intended Uses

Primary Use Cases

Domain	Applications
Insurance Advisory	Product recommendations, policy analysis, claims guidance, underwriting support
Actuarial Analysis	Mortality calculations, reserve estimates, premium projections, risk assessment
Accounting Support	CPA exam prep, GAAP/IFRS guidance, financial statement analysis, audit procedures
Legal Research	Bar exam preparation, contract analysis, legal concept explanation, case briefing
Wealth Management	Estate planning, tax implications, beneficiary strategies, asset protection

Out-of-Scope Uses

• Not for: Medical diagnosis, investment advice requiring fiduciary duty, legal representation
• Not a substitute for: Licensed professionals (CPAs, attorneys, actuaries, financial advisors)
• Should not be used for: High-stakes decisions without professional verification

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Domain Expertise

Insurance & Actuarial Science

Life Insurance          Health Insurance         Actuarial Methods
├── Term Life           ├── Medical              ├── Mortality Tables (CSO, VBT)
├── Whole Life          ├── Dental/Vision        ├── Interest Theory
├── Universal Life      ├── Disability (STD/LTD) ├── Life Contingencies
├── Variable Life       ├── Long-Term Care       ├── Reserve Calculations
├── Indexed Products    └── Medicare/Medicaid    ├── Premium Development
└── Annuities                                    └── Experience Studies

Sample Topics: Non-forfeiture options, policy loans, 1035 exchanges, HIPAA compliance, NAIC regulations, SOA exam concepts, risk classification, reinsurance structures.

Accounting & Finance (CPA/CFA)

CPA Exam Section	Key Topics
FAR	Financial statements, leases, bonds, consolidations, governmental accounting
AUD	Audit procedures, internal controls, sampling, ethics, reporting
REG	Individual/corporate taxation, business law, ethics
BEC	Economics, IT, operations, financial management, cost accounting

CFA Topics	Coverage
Ethics & Standards	Code of Ethics, GIPS
Quantitative Methods	Time value, statistics, probability
Financial Analysis	Ratios, cash flow, earnings quality
Corporate Finance	NPV, IRR, WACC, capital structure

Legal (Bar Exam)

Subject	Key Concepts
Contracts	Formation, consideration, breach, remedies, UCC Article 2
Evidence	Hearsay exceptions, privileges, relevance, authentication
Torts	Negligence, strict liability, intentional torts, damages
Constitutional Law	Due process, equal protection, commerce clause, amendments
Civil Procedure	Jurisdiction, Erie doctrine, pleading, discovery, res judicata
Criminal Law	Actus reus, mens rea, defenses, 4th/5th/6th Amendment
Real Property	Estates, easements, covenants, recording acts, landlord-tenant

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Training Details

Training Data

Category	Examples	Percentage
Insurance & Actuarial	19,080	98.5%
Accounting (CPA/CFA)	~150	0.8%
Legal (Bar Exam)	~145	0.7%
Total Training	19,374	100%
Validation Set	1,068	-

Data Sources:

• Proprietary insurance training materials
• Actuarial exam preparation content
• CPA/CFA study guides and practice questions
• California Bar Exam past papers and study materials
• UCC Article 2 and Black's Law Dictionary excerpts

Training Procedure

Hyperparameter	Value
Learning Rate	5e-5
LR Schedule	Cosine with warmup
Warmup Steps	50
Batch Size	1 (gradient accumulation: 8)
Effective Batch Size	8
Max Steps	2,000
Max Sequence Length	2,048
Weight Decay	0.01
Gradient Clipping	0.5

LoRA Configuration

Parameter	Value
Rank (r)	32
Alpha	64
Dropout	0.05
Target Modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Trainable Parameters	~763M

Quantization

Setting	Value
Method	QLoRA (4-bit)
Compute Dtype	bfloat16
Quant Type	nf4
Double Quant	True

Hardware & Compute

Resource	Specification
GPU	NVIDIA A100-SXM4-80GB
Training Time	8.2 hours (29,394 seconds)
Platform	RunPod

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Evaluation

Quantitative Results

Metric	Value
Final Training Loss	1.269
Evaluation Loss	0.117
Training Samples/Second	0.544

Qualitative Examples

Insurance Example

Prompt: What are the key differences between term and whole life insurance?

Response: Term life insurance provides pure death benefit protection for a specified period (10, 20, or 30 years) with no cash value accumulation. Premiums are lower initially but increase upon renewal. Whole life insurance provides lifetime coverage with guaranteed level premiums and builds cash value on a tax-deferred basis. The cash value can be accessed through policy loans or withdrawals. Key considerations include: (1) affordability vs. permanence, (2) need for cash value accumulation, (3) estate planning objectives, and (4) the insured's age and health status...

Actuarial Example

Prompt: Calculate the present value of a life annuity-due for a person age 65.

Response: To calculate the present value of a life annuity-due (ä_65), we use the formula: ä_x = Σ(v^k × k_p_x) for k=0 to ω-x, where v = 1/(1+i) is the discount factor, k_p_x is the probability of surviving k years, and ω is the limiting age. Using the 2017 CSO mortality table at 4% interest...

Accounting Example

Prompt: When does UCC Article 2 apply instead of common law for contracts?

Response: UCC Article 2 applies to contracts for the sale of goods, while common law governs contracts for services, real estate, and intangibles. Key distinctions: (1) Goods are defined as movable, tangible personal property at the time of identification to the contract; (2) Mixed contracts (goods + services) use the predominant purpose test; (3) UCC provides different rules for offer/acceptance (battle of forms §2-207), statute of frauds ($500 threshold), and warranties...

Legal Example

Prompt: Explain the hearsay rule and its major exceptions.

Response: Hearsay is an out-of-court statement offered to prove the truth of the matter asserted (FRE 801). It is generally inadmissible because the declarant was not under oath and cannot be cross-examined. Major exceptions include: (1) Present sense impression (FRE 803(1)); (2) Excited utterance (FRE 803(2)); (3) State of mind (FRE 803(3)); (4) Medical diagnosis (FRE 803(4)); (5) Business records (FRE 803(6)); (6) Former testimony (FRE 804(b)(1)); (7) Dying declaration (FRE 804(b)(2))...

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Usage

Quick Start

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

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "mistralai/Mistral-Small-Instruct-2409",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "h3ir/morbi-v022-lora")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-Small-Instruct-2409")

# Generate response
prompt = """<s>[INST] You are Morbi, an expert AI assistant specializing in insurance, actuarial science, accounting, and legal matters.

What is the difference between GAAP and IFRS for revenue recognition? [/INST]"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7,
    do_sample=True,
    top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Prompt Format

Morbi uses the Mistral instruction format with a specialized system prompt:

<s>[INST] You are Morbi, an expert AI assistant specializing in insurance, actuarial science, accounting, and legal matters.

{your question here} [/INST]

With vLLM

from vllm import LLM, SamplingParams
from vllm.lora.request import LoRARequest

llm = LLM(
    model="mistralai/Mistral-Small-Instruct-2409",
    enable_lora=True,
    max_lora_rank=32
)

sampling_params = SamplingParams(temperature=0.7, max_tokens=512)

outputs = llm.generate(
    prompts,
    sampling_params,
    lora_request=LoRARequest("morbi", 1, "h3ir/morbi-v022-lora")
)

With Text Generation Inference (TGI)

# Download and merge adapter first, or use TGI's LoRA support
docker run --gpus all -p 8080:80 \
  -v $PWD/data:/data \
  ghcr.io/huggingface/text-generation-inference:latest \
  --model-id mistralai/Mistral-Small-Instruct-2409 \
  --lora-adapters h3ir/morbi-v022-lora

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Technical Specifications

Memory Requirements

Precision	VRAM Required
bfloat16 (full)	~44 GB
8-bit quantized	~24 GB
4-bit quantized	~14 GB
4-bit + LoRA adapter	~16 GB

Supported Backends

• Transformers + PEFT
• vLLM (with LoRA support)
• Text Generation Inference
• llama.cpp (after conversion)
• Ollama (after conversion)

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Limitations & Risks

Known Limitations

1. Domain Imbalance: Insurance/actuarial data (98.5%) significantly outweighs accounting (0.8%) and legal (0.7%) data
2. Jurisdiction: Legal training focuses on U.S. law (California Bar Exam); may not apply to other jurisdictions
3. Temporal: Training data has a knowledge cutoff; regulations and laws change
4. Calculations: While trained on calculation examples, complex numerical computations should be verified

Potential Biases

• May reflect biases present in insurance industry practices
• Legal analysis may favor common law interpretations over civil law systems
• Financial calculations assume U.S. regulatory frameworks

Mitigation Recommendations

• Always verify critical calculations with qualified professionals
• Cross-reference legal advice with licensed attorneys
• Use for educational and research purposes; not as sole decision-making tool

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Environmental Impact

Metric	Value
Hardware	1x NVIDIA A100-80GB
Training Duration	8.2 hours
Estimated Energy	~3.3 kWh
Estimated CO2	~1.3 kg CO2eq*

*Estimate based on U.S. average grid intensity

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Version History

Version	Date	Changes
v0.2.2	Jan 2026	Added accounting (CPA/CFA) and legal (Bar Exam) domains
v0.2.1	Jan 2026	Improved training with cosine LR schedule, lower learning rate
v0.2.0	Jan 2026	Initial release with insurance and actuarial focus

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Citation

@misc{morbi-v022,
  author = {HEIR},
  title = {Morbi v0.2.2: Professional Domain Expert for Insurance, Actuarial Science, Accounting \& Legal},
  year = {2026},
  month = {January},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/h3ir/morbi-v022-lora}},
  note = {LoRA adapter for Mistral-Small-Instruct-2409}
}

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Contact & Support

• Website: https://heir.es
• Documentation: https://heir.es/docs
• Issues: GitHub Issues
• Email: support@heir.es

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