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.
Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B-GGUF: https://huggingface.co/Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B-GGUF
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']
)
The model uses the Llama 3 architecture with the following key components:
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)This model is specifically designed for:
This model should be used with awareness that:
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}
}
9 commits
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.
Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B-GGUF: https://huggingface.co/Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B-GGUF
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']
)
The model uses the Llama 3 architecture with the following key components:
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)This model is specifically designed for:
This model should be used with awareness that:
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}
}
9 commits