README.md

metadata

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%

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}
}