Add proper model card for Morbid v0.2.0

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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-## Uses
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-### Recommendations
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
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-## Environmental Impact
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-## Technical Specifications [optional]
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 ---
+license: apache-2.0
+base_model: mistralai/Mistral-Small-Instruct-2409
+tags:
+- insurance
+- actuarial
+- healthcare
+- life-insurance
+- health-insurance
+- mortality
+- icd-10
+- enterprise
+- morbid
+language:
+- en
+pipeline_tag: text-generation
 library_name: transformers
 ---
+# 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](https://huggingface.co/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
+```python
+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
+```python
+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
+```bibtex
+@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](https://morbid.ai)