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Model: Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B-GGUF
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---
license: llama3
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- Text Generation
- Transformers
- llama
- llama-3
- 8B
- nvidia
- facebook
- meta
- LLM
- fine-tuned
- insurance
- research
- pytorch
- instruct
- chatqa-1.5
- chatqa
- finetune
- gpt4
- conversational
- text-generation-inference
- Inference Endpoints
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"
---
# Open-Insurance-LLM-Llama3-8B-GGUF
This model is a GGUF-quantized version of an insurance domain-specific language model based on Nvidia Llama 3-ChatQA
Fine-tuned for insurance-related queries and conversations.
# Other Quantized Variants
[bartowski/Open-Insurance-LLM-Llama3-8B-GGUF](https://huggingface.co/bartowski/Open-Insurance-LLM-Llama3-8B-GGUF)
## Model Details
- **Model Type:** Quantized Language Model (GGUF format)
- **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
- **Quantization:** 8-bit (Q8_0), 5-bit (Q5_K_M), 4-bit (Q4_K_M), 16-bit
- **Finetuned Dataset**: InsuranceQA (https://github.com/shuzi/insuranceQA)
- **Developer:** Raj Maharajwala
- **License:** llama3
- **Language:** English
## Setup Instructions
### Environment Setup
#### For Windows
```bash
python3 -m venv .venv_open_insurance_llm
.\.venv_open_insurance_llm\Scripts\activate
```
#### For Mac/Linux
```bash
python3 -m venv .venv_open_insurance_llm
source .venv_open_insurance_llm/bin/activate
```
### Installation
#### For Mac Users (Metal Support)
```bash
export FORCE_CMAKE=1
CMAKE_ARGS="-DGGML_METAL=on" pip install --upgrade --force-reinstall llama-cpp-python==0.3.2 --no-cache-dir
```
#### For Windows Users (CPU Support)
```bash
pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu
```
### Dependencies
Then install dependencies (inference_requirements.txt) attached under `Files and Versions`:
```bash
pip install -r inference_requirements.txt
```
## Inference Loop
```python
# Attached under `Files and Versions` (inference_open-insurance-llm-gguf.py)
import os
import time
from pathlib import Path
from llama_cpp import Llama
from rich.console import Console
from huggingface_hub import hf_hub_download
from dataclasses import dataclass
from typing import List, Dict, Any, Tuple
@dataclass
class ModelConfig:
# Optimized parameters for coherent responses and efficient performance on devices like MacBook Air M2
model_name: str = "Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B-GGUF"
model_file: str = "open-insurance-llm-q4_k_m.gguf"
# model_file: str = "open-insurance-llm-q8_0.gguf" # 8-bit quantization; higher precision, better quality, increased resource usage
# model_file: str = "open-insurance-llm-q5_k_m.gguf" # 5-bit quantization; balance between performance and resource efficiency
max_tokens: int = 1000 # Maximum number of tokens to generate in a single output
temperature: float = 0.1 # Controls randomness in output; lower values produce more coherent responses (performs scaling distribution)
top_k: int = 15 # After temperature scaling, Consider the top 15 most probable tokens during sampling
top_p: float = 0.2 # After reducing the set to 15 tokens, Uses nucleus sampling to select tokens with a cumulative probability of 20%
repeat_penalty: float = 1.2 # Penalize repeated tokens to reduce redundancy
num_beams: int = 4 # Number of beams for beam search; higher values improve quality at the cost of speed
n_gpu_layers: int = -2 # Number of layers to offload to GPU; -1 for full GPU utilization, -2 for automatic configuration
n_ctx: int = 2048 # Context window size; Llama 3 models support up to 8192 tokens context length
n_batch: int = 256 # Number of tokens to process simultaneously; adjust based on available hardware (suggested 512)
verbose: bool = False # True for enabling verbose logging for debugging purposes
use_mmap: bool = False # Memory-map model to reduce RAM usage; set to True if running on limited memory systems
use_mlock: bool = True # Lock model into RAM to prevent swapping; improves performance on systems with sufficient RAM
offload_kqv: bool = True # Offload key, query, value matrices to GPU to accelerate inference
class InsuranceLLM:
def __init__(self, config: ModelConfig):
self.config = config
self.llm_ctx = None
self.console = Console()
self.conversation_history: List[Dict[str, str]] = []
self.system_message = (
"This is a chat between a user and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. "
"The assistant should also indicate when the answer cannot be found in the context. "
"You are an expert from the Insurance domain with extensive insurance knowledge and "
"professional writer skills, especially about insurance policies. "
"Your name is OpenInsuranceLLM, and you were developed by Raj Maharajwala. "
"You are willing to help answer the user's query with a detailed explanation. "
"In your explanation, leverage your deep insurance expertise, such as relevant insurance policies, "
"complex coverage plans, or other pertinent insurance concepts. Use precise insurance terminology while "
"still aiming to make the explanation clear and accessible to a general audience."
)
def download_model(self) -> str:
try:
with self.console.status("[bold green]Downloading model..."):
model_path = hf_hub_download(
self.config.model_name,
filename=self.config.model_file,
local_dir=os.path.join(os.getcwd(), 'gguf_dir')
)
return model_path
except Exception as e:
self.console.print(f"[red]Error downloading model: {str(e)}[/red]")
raise
def load_model(self) -> None:
try:
quantized_path = os.path.join(os.getcwd(), "gguf_dir")
directory = Path(quantized_path)
try:
model_path = str(list(directory.glob(self.config.model_file))[0])
except IndexError:
model_path = self.download_model()
with self.console.status("[bold green]Loading model..."):
self.llm_ctx = Llama(
model_path=model_path,
n_gpu_layers=self.config.n_gpu_layers,
n_ctx=self.config.n_ctx,
n_batch=self.config.n_batch,
num_beams=self.config.num_beams,
verbose=self.config.verbose,
use_mlock=self.config.use_mlock,
use_mmap=self.config.use_mmap,
offload_kqv=self.config.offload_kqv
)
except Exception as e:
self.console.print(f"[red]Error loading model: {str(e)}[/red]")
raise
def build_conversation_prompt(self, new_question: str, context: str = "") -> str:
prompt = f"System: {self.system_message}\n\n"
# Add conversation history
for exchange in self.conversation_history:
prompt += f"User: {exchange['user']}\n\n"
prompt += f"Assistant: {exchange['assistant']}\n\n"
# Add the new question
if context:
prompt += f"User: Context: {context}\nQuestion: {new_question}\n\n"
else:
prompt += f"User: {new_question}\n\n"
prompt += "Assistant:"
return prompt
def generate_response(self, prompt: str) -> Tuple[str, int, float]:
if not self.llm_ctx:
raise RuntimeError("Model not loaded. Call load_model() first.")
self.console.print("[bold cyan]Assistant: [/bold cyan]", end="")
complete_response = ""
token_count = 0
start_time = time.time()
try:
for chunk in self.llm_ctx.create_completion(
prompt,
max_tokens=self.config.max_tokens,
top_k=self.config.top_k,
top_p=self.config.top_p,
temperature=self.config.temperature,
repeat_penalty=self.config.repeat_penalty,
stream=True
):
text_chunk = chunk["choices"][0]["text"]
complete_response += text_chunk
token_count += 1
print(text_chunk, end="", flush=True)
elapsed_time = time.time() - start_time
print()
return complete_response, token_count, elapsed_time
except Exception as e:
self.console.print(f"\n[red]Error generating response: {str(e)}[/red]")
return f"I encountered an error while generating a response. Please try again or ask a different question.", 0, 0
def run_chat(self):
try:
self.load_model()
self.console.print("\n[bold green]Welcome to Open-Insurance-LLM![/bold green]")
self.console.print("Enter your questions (type '/bye', 'exit', or 'quit' to end the session)\n")
self.console.print("Optional: You can provide context by typing 'context:' followed by your context, then 'question:' followed by your question\n")
self.console.print("Your conversation history will be maintained for context-aware responses.\n")
total_tokens = 0
while True:
try:
user_input = self.console.input("[bold cyan]User:[/bold cyan] ").strip()
if user_input.lower() in ["exit", "/bye", "quit"]:
self.console.print(f"\n[dim]Total tokens: {total_tokens}[/dim]")
self.console.print("\n[bold green]Thank you for using OpenInsuranceLLM![/bold green]")
break
# Reset conversation with command
if user_input.lower() == "/reset":
self.conversation_history = []
self.console.print("[yellow]Conversation history has been reset.[/yellow]")
continue
context = ""
question = user_input
if "context:" in user_input.lower() and "question:" in user_input.lower():
parts = user_input.split("question:", 1)
context = parts[0].replace("context:", "").strip()
question = parts[1].strip()
prompt = self.build_conversation_prompt(question, context)
response, tokens, elapsed_time = self.generate_response(prompt)
# Add to conversation history
self.conversation_history.append({
"user": question,
"assistant": response
})
# Update total tokens
total_tokens += tokens
# Print metrics
tokens_per_sec = tokens / elapsed_time if elapsed_time > 0 else 0
self.console.print(
f"[dim]Tokens: {tokens} || " +
f"Time: {elapsed_time:.2f}s || " +
f"Speed: {tokens_per_sec:.2f} tokens/sec[/dim]"
)
print() # Add a blank line after each response
except KeyboardInterrupt:
self.console.print("\n[yellow]Input interrupted. Type '/bye', 'exit', or 'quit' to quit.[/yellow]")
continue
except Exception as e:
self.console.print(f"\n[red]Error processing input: {str(e)}[/red]")
continue
except Exception as e:
self.console.print(f"\n[red]Fatal error: {str(e)}[/red]")
finally:
if self.llm_ctx:
del self.llm_ctx
def main():
try:
config = ModelConfig()
llm = InsuranceLLM(config)
llm.run_chat()
except KeyboardInterrupt:
print("\nProgram interrupted by user")
except Exception as e:
print(f"\nApplication error: {str(e)}")
if __name__ == "__main__":
main()
```
```bash
python3 inference_open-insurance-llm-gguf.py
```
### Nvidia Llama 3 - ChatQA Paper:
Arxiv : [https://arxiv.org/pdf/2401.10225](https://arxiv.org/pdf/2401.10225)
## 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 base model or quantized model in your research or applications, please cite:
```
@misc{maharajwala2024openinsurance,
author = {Raj Maharajwala},
title = {Open-Insurance-LLM-Llama3-8B-GGUF},
year = {2024},
publisher = {HuggingFace},
linkedin = {https://www.linkedin.com/in/raj6800/},
url = {https://huggingface.co/Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B-GGUF}
}
```

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import os
import time
from pathlib import Path
from llama_cpp import Llama
from rich.console import Console
from huggingface_hub import hf_hub_download
from dataclasses import dataclass
from typing import List, Dict, Any, Tuple
@dataclass
class ModelConfig:
# Optimized parameters for coherent responses and efficient performance on devices like MacBook Air M2
model_name: str = "Raj-Maharajwala/Open-Insurance-LLM-Llama3-8B-GGUF"
model_file: str = "open-insurance-llm-q4_k_m.gguf"
# model_file: str = "open-insurance-llm-q8_0.gguf" # 8-bit quantization; higher precision, better quality, increased resource usage
# model_file: str = "open-insurance-llm-q5_k_m.gguf" # 5-bit quantization; balance between performance and resource efficiency
max_tokens: int = 1000 # Maximum number of tokens to generate in a single output
temperature: float = 0.1 # Controls randomness in output; lower values produce more coherent responses (performs scaling distribution)
top_k: int = 15 # After temperature scaling, Consider the top 15 most probable tokens during sampling
top_p: float = 0.2 # After reducing the set to 15 tokens, Uses nucleus sampling to select tokens with a cumulative probability of 20%
repeat_penalty: float = 1.2 # Penalize repeated tokens to reduce redundancy
num_beams: int = 4 # Number of beams for beam search; higher values improve quality at the cost of speed
n_gpu_layers: int = -2 # Number of layers to offload to GPU; -1 for full GPU utilization, -2 for automatic configuration
n_ctx: int = 2048 # Context window size; Llama 3 models support up to 8192 tokens context length
n_batch: int = 256 # Number of tokens to process simultaneously; adjust based on available hardware (suggested 512)
verbose: bool = False # True for enabling verbose logging for debugging purposes
use_mmap: bool = False # Memory-map model to reduce RAM usage; set to True if running on limited memory systems
use_mlock: bool = True # Lock model into RAM to prevent swapping; improves performance on systems with sufficient RAM
offload_kqv: bool = True # Offload key, query, value matrices to GPU to accelerate inference
class InsuranceLLM:
def __init__(self, config: ModelConfig):
self.config = config
self.llm_ctx = None
self.console = Console()
self.conversation_history: List[Dict[str, str]] = []
self.system_message = (
"This is a chat between a user and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. "
"The assistant should also indicate when the answer cannot be found in the context. "
"You are an expert from the Insurance domain with extensive insurance knowledge and "
"professional writer skills, especially about insurance policies. "
"Your name is OpenInsuranceLLM, and you were developed by Raj Maharajwala. "
"You are willing to help answer the user's query with a detailed explanation. "
"In your explanation, leverage your deep insurance expertise, such as relevant insurance policies, "
"complex coverage plans, or other pertinent insurance concepts. Use precise insurance terminology while "
"still aiming to make the explanation clear and accessible to a general audience."
)
def download_model(self) -> str:
try:
with self.console.status("[bold green]Downloading model..."):
model_path = hf_hub_download(
self.config.model_name,
filename=self.config.model_file,
local_dir=os.path.join(os.getcwd(), 'gguf_dir')
)
return model_path
except Exception as e:
self.console.print(f"[red]Error downloading model: {str(e)}[/red]")
raise
def load_model(self) -> None:
try:
quantized_path = os.path.join(os.getcwd(), "gguf_dir")
directory = Path(quantized_path)
try:
model_path = str(list(directory.glob(self.config.model_file))[0])
except IndexError:
model_path = self.download_model()
with self.console.status("[bold green]Loading model..."):
self.llm_ctx = Llama(
model_path=model_path,
n_gpu_layers=self.config.n_gpu_layers,
n_ctx=self.config.n_ctx,
n_batch=self.config.n_batch,
num_beams=self.config.num_beams,
verbose=self.config.verbose,
use_mlock=self.config.use_mlock,
use_mmap=self.config.use_mmap,
offload_kqv=self.config.offload_kqv
)
except Exception as e:
self.console.print(f"[red]Error loading model: {str(e)}[/red]")
raise
def build_conversation_prompt(self, new_question: str, context: str = "") -> str:
prompt = f"System: {self.system_message}\n\n"
# Add conversation history
for exchange in self.conversation_history:
prompt += f"User: {exchange['user']}\n\n"
prompt += f"Assistant: {exchange['assistant']}\n\n"
# Add the new question
if context:
prompt += f"User: Context: {context}\nQuestion: {new_question}\n\n"
else:
prompt += f"User: {new_question}\n\n"
prompt += "Assistant:"
return prompt
def generate_response(self, prompt: str) -> Tuple[str, int, float]:
if not self.llm_ctx:
raise RuntimeError("Model not loaded. Call load_model() first.")
self.console.print("[bold cyan]Assistant: [/bold cyan]", end="")
complete_response = ""
token_count = 0
start_time = time.time()
try:
for chunk in self.llm_ctx.create_completion(
prompt,
max_tokens=self.config.max_tokens,
top_k=self.config.top_k,
top_p=self.config.top_p,
temperature=self.config.temperature,
repeat_penalty=self.config.repeat_penalty,
stream=True
):
text_chunk = chunk["choices"][0]["text"]
complete_response += text_chunk
token_count += 1
print(text_chunk, end="", flush=True)
elapsed_time = time.time() - start_time
print()
return complete_response, token_count, elapsed_time
except Exception as e:
self.console.print(f"\n[red]Error generating response: {str(e)}[/red]")
return f"I encountered an error while generating a response. Please try again or ask a different question.", 0, 0
def run_chat(self):
try:
self.load_model()
self.console.print("\n[bold green]Welcome to Open-Insurance-LLM![/bold green]")
self.console.print("Enter your questions (type '/bye', 'exit', or 'quit' to end the session)\n")
self.console.print("Optional: You can provide context by typing 'context:' followed by your context, then 'question:' followed by your question\n")
self.console.print("Your conversation history will be maintained for context-aware responses.\n")
total_tokens = 0
while True:
try:
user_input = self.console.input("[bold cyan]User:[/bold cyan] ").strip()
if user_input.lower() in ["exit", "/bye", "quit"]:
self.console.print(f"\n[dim]Total tokens: {total_tokens}[/dim]")
self.console.print("\n[bold green]Thank you for using Open-Insurance-LLM![/bold green]")
break
# Reset conversation with command
if user_input.lower() == "/reset":
self.conversation_history = []
self.console.print("[yellow]Conversation history has been reset.[/yellow]")
continue
context = ""
question = user_input
if "context:" in user_input.lower() and "question:" in user_input.lower():
parts = user_input.split("question:", 1)
context = parts[0].replace("context:", "").strip()
question = parts[1].strip()
prompt = self.build_conversation_prompt(question, context)
response, tokens, elapsed_time = self.generate_response(prompt)
# Add to conversation history
self.conversation_history.append({
"user": question,
"assistant": response
})
# Update total tokens
total_tokens += tokens
# Print metrics
tokens_per_sec = tokens / elapsed_time if elapsed_time > 0 else 0
self.console.print(
f"[dim]Tokens: {tokens} || " +
f"Time: {elapsed_time:.2f}s || " +
f"Speed: {tokens_per_sec:.2f} tokens/sec[/dim]"
)
print() # Add a blank line after each response
except KeyboardInterrupt:
self.console.print("\n[yellow]Input interrupted. Type '/bye', 'exit', or 'quit' to quit.[/yellow]")
continue
except Exception as e:
self.console.print(f"\n[red]Error processing input: {str(e)}[/red]")
continue
except Exception as e:
self.console.print(f"\n[red]Fatal error: {str(e)}[/red]")
finally:
if self.llm_ctx:
del self.llm_ctx
def main():
try:
config = ModelConfig()
llm = InsuranceLLM(config)
llm.run_chat()
except KeyboardInterrupt:
print("\nProgram interrupted by user")
except Exception as e:
print(f"\nApplication error: {str(e)}")
if __name__ == "__main__":
main()

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inference_requirements.txt Normal file
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black==24.10.0
certifi==2024.8.30
charset-normalizer==3.4.0
click==8.1.7
diskcache==5.6.3
filelock==3.16.1
fsspec==2024.10.0
huggingface-hub==0.26.2
idna==3.10
iniconfig==2.0.0
isort==5.13.2
Jinja2==3.1.4
llama_cpp_python==0.3.2
markdown-it-py==3.0.0
MarkupSafe==3.0.2
mdurl==0.1.2
mypy-extensions==1.0.0
numpy==2.1.3
packaging==24.2
pathspec==0.12.1
platformdirs==4.3.6
pluggy==1.5.0
psutil==6.1.0
Pygments==2.18.0
pytest==8.3.3
PyYAML==6.0.2
requests==2.32.3
rich==13.9.4
tqdm==4.67.0
typing_extensions==4.12.2
urllib3==2.2.3

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