README.md

---
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- Text Generation
- Transformers
- llama
- llama-3
- 8B
- nvidia
- facebook
- meta
- LLM
- insurance
- research
- pytorch
- instruct
- chatqa-1.5
- chatqa
- finetune
- gpt4
- conversational
- text-generation-inference
datasets:
- InsuranceQA

base_model: "nvidia/Llama3-ChatQA-1.5-8B"
finetuned: "Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B"
quantized: "Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B-GGUF"
license: llama3
---

# Open-Insurance-LLM-Llama3-8B

This model is a domain-specific language model based on Nvidia Llama 3 ChatQA, fine-tuned for insurance-related queries and conversations. It leverages the architecture of Llama 3 and is specifically trained to handle insurance domain tasks.

## Model Details

- **Model Type:** Instruction-tuned Language Model
- **Base Model:** nvidia/Llama3-ChatQA-1.5-8B
- **Finetuned Model:** Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B
- **Quantized Model:** Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B-GGUF
- **Model Architecture:** Llama
- **Parameters:** 8.05 billion
- **Developer:** Raj Maharajwala
- **License:** llama3
- **Language:** English

### Quantized Model 

Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B-GGUF: https://huggingface.co/Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B-GGUF

## Training Data

The model has been fine-tuned on the InsuranceQA dataset using LoRA (8 bit), which contains insurance-specific question-answer pairs and domain knowledge.
trainable params: 20.97M || all params: 8.05B || trainable %: 0.26%
```bash
LoraConfig(
  r=8,
  lora_alpha=32,
  lora_dropout=0.05,
  bias="none",
  task_type="CAUSAL_LM",
  target_modules=['up_proj', 'down_proj', 'gate_proj', 'k_proj', 'q_proj', 'v_proj', 'o_proj']
)
```

## Model Architecture

The model uses the Llama 3 architecture with the following key components:
- 8B parameter configuration
- Enhanced attention mechanisms from Llama 3
- ChatQA 1.5 instruction-tuning framework
- Insurance domain-specific adaptations

## Files in Repository

- **Model Files:**
  - `model-00001-of-00004.safetensors` (4.98 GB)
  - `model-00002-of-00004.safetensors` (5 GB)
  - `model-00003-of-00004.safetensors` (4.92 GB)
  - `model-00004-of-00004.safetensors` (1.17 GB)
  - `model.safetensors.index.json` (24 kB)

- **Tokenizer Files:**
  - `tokenizer.json` (17.2 MB)
  - `tokenizer_config.json` (51.3 kB)
  - `special_tokens_map.json` (335 Bytes)

- **Configuration Files:**
  - `config.json` (738 Bytes)
  - `generation_config.json` (143 Bytes)

## Use Cases

This model is specifically designed for:
- Insurance policy understanding and explanation
- Claims processing assistance
- Coverage analysis
- Insurance terminology clarification
- Policy comparison and recommendations
- Risk assessment queries
- Insurance compliance questions

## Limitations

- The model's knowledge is limited to its training data cutoff
- Should not be used as a replacement for professional insurance advice
- May occasionally generate plausible-sounding but incorrect information

## Bias and Ethics

This model should be used with awareness that:
- It may reflect biases present in insurance industry training data
- Output should be verified by insurance professionals for critical decisions
- It should not be used as the sole basis for insurance decisions
- The model's responses should be treated as informational, not as legal or professional advice

## Citation and Attribution

If you use this model in your research or applications, please cite:
```
@misc{maharajwala2024openinsurance,
  author = {Raj Maharajwala},
  title = {Open-Insurance-LLM-Llama3-8B},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B}
}
```