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Raj-Maharajwala_-_Open-Insurance-LLM-Llama3-8B-gguf

Name: Raj-Maharajwala_-_Open-Insurance-LLM-Llama3-8B-gguf
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Open-Insurance-LLM-Llama3-8B - GGUF

Model creator: https://huggingface.co/Raj-Maharajwala/
Original model: https://huggingface.co/Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B/

Name	Quant method	Size
Open-Insurance-LLM-Llama3-8B.Q2_K.gguf	Q2_K	2.96GB
Open-Insurance-LLM-Llama3-8B.IQ3_XS.gguf	IQ3_XS	3.28GB
Open-Insurance-LLM-Llama3-8B.IQ3_S.gguf	IQ3_S	3.43GB
Open-Insurance-LLM-Llama3-8B.Q3_K_S.gguf	Q3_K_S	3.41GB
Open-Insurance-LLM-Llama3-8B.IQ3_M.gguf	IQ3_M	3.52GB
Open-Insurance-LLM-Llama3-8B.Q3_K.gguf	Q3_K	3.74GB
Open-Insurance-LLM-Llama3-8B.Q3_K_M.gguf	Q3_K_M	3.74GB
Open-Insurance-LLM-Llama3-8B.Q3_K_L.gguf	Q3_K_L	4.03GB
Open-Insurance-LLM-Llama3-8B.IQ4_XS.gguf	IQ4_XS	4.18GB
Open-Insurance-LLM-Llama3-8B.Q4_0.gguf	Q4_0	4.34GB
Open-Insurance-LLM-Llama3-8B.IQ4_NL.gguf	IQ4_NL	4.38GB
Open-Insurance-LLM-Llama3-8B.Q4_K_S.gguf	Q4_K_S	4.37GB
Open-Insurance-LLM-Llama3-8B.Q4_K.gguf	Q4_K	4.58GB
Open-Insurance-LLM-Llama3-8B.Q4_K_M.gguf	Q4_K_M	4.58GB
Open-Insurance-LLM-Llama3-8B.Q4_1.gguf	Q4_1	4.78GB
Open-Insurance-LLM-Llama3-8B.Q5_0.gguf	Q5_0	5.21GB
Open-Insurance-LLM-Llama3-8B.Q5_K_S.gguf	Q5_K_S	5.21GB
Open-Insurance-LLM-Llama3-8B.Q5_K.gguf	Q5_K	5.34GB
Open-Insurance-LLM-Llama3-8B.Q5_K_M.gguf	Q5_K_M	5.34GB
Open-Insurance-LLM-Llama3-8B.Q5_1.gguf	Q5_1	5.65GB
Open-Insurance-LLM-Llama3-8B.Q6_K.gguf	Q6_K	6.14GB
Open-Insurance-LLM-Llama3-8B.Q8_0.gguf	Q8_0	7.95GB

Original model description:

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}
}

RichardErkhov/Raj-Maharajwala_-_Open-Insurance-LLM-Llama3-8B-gguf