Instructions to use hjogidasani/medical-triage-llama-3.1-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hjogidasani/medical-triage-llama-3.1-8b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="hjogidasani/medical-triage-llama-3.1-8b",
	filename="medical_triage_q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use hjogidasani/medical-triage-llama-3.1-8b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf hjogidasani/medical-triage-llama-3.1-8b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf hjogidasani/medical-triage-llama-3.1-8b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf hjogidasani/medical-triage-llama-3.1-8b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf hjogidasani/medical-triage-llama-3.1-8b: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 hjogidasani/medical-triage-llama-3.1-8b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf hjogidasani/medical-triage-llama-3.1-8b: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 hjogidasani/medical-triage-llama-3.1-8b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hjogidasani/medical-triage-llama-3.1-8b:Q4_K_M

Use Docker

docker model run hf.co/hjogidasani/medical-triage-llama-3.1-8b:Q4_K_M

LM Studio
Jan

vLLM

How to use hjogidasani/medical-triage-llama-3.1-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hjogidasani/medical-triage-llama-3.1-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hjogidasani/medical-triage-llama-3.1-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hjogidasani/medical-triage-llama-3.1-8b:Q4_K_M

Ollama
How to use hjogidasani/medical-triage-llama-3.1-8b with Ollama:
```
ollama run hf.co/hjogidasani/medical-triage-llama-3.1-8b:Q4_K_M
```

Unsloth Studio

How to use hjogidasani/medical-triage-llama-3.1-8b 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 hjogidasani/medical-triage-llama-3.1-8b 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 hjogidasani/medical-triage-llama-3.1-8b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for hjogidasani/medical-triage-llama-3.1-8b to start chatting

How to use hjogidasani/medical-triage-llama-3.1-8b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf hjogidasani/medical-triage-llama-3.1-8b:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "hjogidasani/medical-triage-llama-3.1-8b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use hjogidasani/medical-triage-llama-3.1-8b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf hjogidasani/medical-triage-llama-3.1-8b:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default hjogidasani/medical-triage-llama-3.1-8b:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use hjogidasani/medical-triage-llama-3.1-8b with Docker Model Runner:
```
docker model run hf.co/hjogidasani/medical-triage-llama-3.1-8b:Q4_K_M
```

Lemonade

How to use hjogidasani/medical-triage-llama-3.1-8b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hjogidasani/medical-triage-llama-3.1-8b:Q4_K_M

Run and chat with the model

lemonade run user.medical-triage-llama-3.1-8b-Q4_K_M

List all available models

lemonade list

🩺 ZeroTime-Bot: Medical Triage Alignment

Problem: Standard AI models often "over-triage" (e.g., calling a stubbed toe an emergency) due to safety-bias in training data. Solution: Used GRPO (Reinforcement Learning) to align a Llama-3.1 8B model to recognize clinical nuances between Level 1 (Emergency) and Level 3 (Non-Urgent).

🚀 Quick Start (Local Run)

Install Ollama.
Download the medical_triage.gguf from my [Hugging Face Link].
Run: ollama create medicalbot -f Modelfile
Run: ollama run medicalbot

📊 Results: Before vs. After

Scenario	Base Llama-3.1	My Aligned Model	Result
Stubbed Toe	Level 1 (Emergency)	Level 3 (Non-Urgent)	✅ Fixed Bias
Chest Pain	Level 1 (Emergency)	Level 1 (Emergency)	✅ Kept Safety

🛠️ Technical Approach

Instead of standard fine-tuning (SFT), we utilized Group Relative Policy Optimization (GRPO). We created a reward function that penalized the model for assigning "Emergency" status to cases with stable clinical indicators, forcing it to develop deeper medical reasoning.