Instructions to use bartowski/Open-Insurance-LLM-Llama3-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use bartowski/Open-Insurance-LLM-Llama3-8B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bartowski/Open-Insurance-LLM-Llama3-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/Open-Insurance-LLM-Llama3-8B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bartowski/Open-Insurance-LLM-Llama3-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/Open-Insurance-LLM-Llama3-8B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf bartowski/Open-Insurance-LLM-Llama3-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bartowski/Open-Insurance-LLM-Llama3-8B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf bartowski/Open-Insurance-LLM-Llama3-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bartowski/Open-Insurance-LLM-Llama3-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bartowski/Open-Insurance-LLM-Llama3-8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bartowski/Open-Insurance-LLM-Llama3-8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/Open-Insurance-LLM-Llama3-8B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bartowski/Open-Insurance-LLM-Llama3-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bartowski/Open-Insurance-LLM-Llama3-8B-GGUF:Q4_K_M

Ollama
How to use bartowski/Open-Insurance-LLM-Llama3-8B-GGUF with Ollama:
```
ollama run hf.co/bartowski/Open-Insurance-LLM-Llama3-8B-GGUF:Q4_K_M
```

Unsloth Studio

How to use bartowski/Open-Insurance-LLM-Llama3-8B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for bartowski/Open-Insurance-LLM-Llama3-8B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for bartowski/Open-Insurance-LLM-Llama3-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bartowski/Open-Insurance-LLM-Llama3-8B-GGUF to start chatting

Docker Model Runner
How to use bartowski/Open-Insurance-LLM-Llama3-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/bartowski/Open-Insurance-LLM-Llama3-8B-GGUF:Q4_K_M
```

Lemonade

How to use bartowski/Open-Insurance-LLM-Llama3-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bartowski/Open-Insurance-LLM-Llama3-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Open-Insurance-LLM-Llama3-8B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Request: DOI

by billrilea - opened Mar 23, 2025

Discussion

billrilea

Mar 23, 2025

I am a Florida Licensed public adjuster. I am looking for a safe way for a model with Florida Laws and ethical codes in place with a bit of reasoning on Current Residential Construction Code. To be able to review insurance policy - extract my clients data coverages policy information exclusions options dispute resolution paths appraisal managed repair clauses. Extract summary.
Then review from insurance adjuster or desk adjuster emails coverage letters Xactimate estimates engineer reports weather reports our policy holder estimates contractors estimates apply some reasoning what we have differences in price and scope. Would cool if could point it to an Insurds folder on my LAN that has all this data in it and it could learn and forget as needed to move on to the next claim but not risk my clients data.

billrilea

Mar 23, 2025

So far your llm is pretty sharp.

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