Instructions to use MorbidCorp/Morbid-22B-Insurance-v020 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MorbidCorp/Morbid-22B-Insurance-v020 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MorbidCorp/Morbid-22B-Insurance-v020")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("MorbidCorp/Morbid-22B-Insurance-v020")
model = AutoModelForMultimodalLM.from_pretrained("MorbidCorp/Morbid-22B-Insurance-v020")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MorbidCorp/Morbid-22B-Insurance-v020 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MorbidCorp/Morbid-22B-Insurance-v020"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MorbidCorp/Morbid-22B-Insurance-v020",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MorbidCorp/Morbid-22B-Insurance-v020

SGLang

How to use MorbidCorp/Morbid-22B-Insurance-v020 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "MorbidCorp/Morbid-22B-Insurance-v020" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MorbidCorp/Morbid-22B-Insurance-v020",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "MorbidCorp/Morbid-22B-Insurance-v020" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MorbidCorp/Morbid-22B-Insurance-v020",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MorbidCorp/Morbid-22B-Insurance-v020 with Docker Model Runner:
```
docker model run hf.co/MorbidCorp/Morbid-22B-Insurance-v020
```

Morbid v0.2.0 - Enterprise Insurance AI

A 22B parameter LLM fine-tuned for health and life insurance applications, built on Mistral Small Instruct.

Model Details

Base Model: mistralai/Mistral-Small-Instruct-2409
Parameters: 22B
Training: Supervised Fine-Tuning (SFT) with LoRA on insurance/actuarial dataset
License: Apache 2.0
Developed by: MorbidCorp

Capabilities

Insurance & Actuarial

Life insurance products (term, whole, universal, variable)
Health insurance (medical, dental, disability, LTC)
Premium calculations and rate setting
Underwriting and risk classification
Claims analysis and management
Regulatory compliance (NAIC, state/federal)

Actuarial Mathematics

Mortality tables and life expectancy calculations
Present value and annuity calculations
Reserve valuation
Risk assessment and modeling

Medical Classification

ICD-10 code lookup and explanation
Cause-of-death classification
Medical terminology

Usage

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "MorbidCorp/Morbid-22B-Insurance-v020"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Mistral Instruct format
system = """You are Morbi, an expert AI assistant specializing in health and life insurance, actuarial science, and risk analysis."""

user_msg = "What is the life expectancy for a 50-year-old male in the US?"

prompt = f"<s>[INST] {system}\n\n{user_msg} [/INST]"

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

With Transformers Pipeline

from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="MorbidCorp/Morbid-22B-Insurance-v020",
    torch_dtype="bfloat16",
    device_map="auto"
)

messages = [
    {"role": "user", "content": "Explain the difference between term and whole life insurance."}
]

output = pipe(messages, max_new_tokens=512)
print(output[0]["generated_text"][-1]["content"])

Training Data

The model was fine-tuned on a curated dataset including:

Insurance product documentation and explanations
Actuarial exam questions and solutions (SOA P, FM, IFM)
Life expectancy and mortality data
ICD-10 WHO 2019 medical classification codes
Underwriting guidelines and risk assessment scenarios
Regulatory compliance documentation

Limitations

This model is for informational purposes only
Not a substitute for licensed professional advice
Should not be used for final underwriting decisions
May not reflect the most current regulatory requirements
Life expectancy estimates are population averages, not individual predictions

Hardware Requirements

Minimum: 40GB VRAM (A100 40GB, A6000)
Recommended: 80GB VRAM (A100 80GB, H100)
Quantized (AWQ/GPTQ 4-bit): 24GB VRAM (RTX 4090, A10G)

Citation

@misc{morbid2026,
  title={Morbid v0.2.0: Enterprise Insurance AI},
  author={MorbidCorp},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/MorbidCorp/Morbid-22B-Insurance-v020}
}

Contact

For enterprise support and customization: MORBID.AI

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Safetensors

Model size

22B params

Tensor type

BF16

Model tree for MorbidCorp/Morbid-22B-Insurance-v020

Base model

mistralai/Mistral-Small-Instruct-2409

Finetuned

(28)

this model