Instructions to use Navaneeth-14/rag-hackathon-app with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Navaneeth-14/rag-hackathon-app 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 Navaneeth-14/rag-hackathon-app:Q4_K_M # Run inference directly in the terminal: llama cli -hf Navaneeth-14/rag-hackathon-app:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Navaneeth-14/rag-hackathon-app:Q4_K_M # Run inference directly in the terminal: llama cli -hf Navaneeth-14/rag-hackathon-app: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 Navaneeth-14/rag-hackathon-app:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Navaneeth-14/rag-hackathon-app: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 Navaneeth-14/rag-hackathon-app:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Navaneeth-14/rag-hackathon-app:Q4_K_M
Use Docker
docker model run hf.co/Navaneeth-14/rag-hackathon-app:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Navaneeth-14/rag-hackathon-app with Ollama:
ollama run hf.co/Navaneeth-14/rag-hackathon-app:Q4_K_M
- Unsloth Studio
How to use Navaneeth-14/rag-hackathon-app 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 Navaneeth-14/rag-hackathon-app 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 Navaneeth-14/rag-hackathon-app to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Navaneeth-14/rag-hackathon-app to start chatting
- Docker Model Runner
How to use Navaneeth-14/rag-hackathon-app with Docker Model Runner:
docker model run hf.co/Navaneeth-14/rag-hackathon-app:Q4_K_M
- Lemonade
How to use Navaneeth-14/rag-hackathon-app with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Navaneeth-14/rag-hackathon-app:Q4_K_M
Run and chat with the model
lemonade run user.rag-hackathon-app-Q4_K_M
List all available models
lemonade list
- Atomic Chat
| #!/usr/bin/env python3 | |
| """ | |
| Test script for hackrx/run endpoint | |
| """ | |
| import requests | |
| import json | |
| # Your ngrok URL | |
| BASE_URL = "https://c1c8ea4c476e.ngrok-free.app" | |
| def test_hackrx_run(): | |
| """Test the hackrx/run endpoint""" | |
| print("π§ͺ Testing hackrx/run endpoint...") | |
| url = f"{BASE_URL}/hackrx/run" | |
| payload = { | |
| "questions": [ | |
| "What is covered under this policy?", | |
| "What is the maximum coverage amount?" | |
| ] | |
| } | |
| try: | |
| print(f"URL: {url}") | |
| print(f"Payload: {json.dumps(payload, indent=2)}") | |
| response = requests.post( | |
| url, | |
| json=payload, | |
| headers={"Content-Type": "application/json"}, | |
| timeout=30 | |
| ) | |
| print(f"Status Code: {response.status_code}") | |
| print(f"Response: {json.dumps(response.json(), indent=2)}") | |
| if response.status_code == 200: | |
| print("β hackrx/run endpoint is working!") | |
| return True | |
| else: | |
| print("β hackrx/run endpoint failed") | |
| return False | |
| except requests.exceptions.ConnectionError: | |
| print("β Connection error - server might not be running") | |
| return False | |
| except Exception as e: | |
| print(f"β Error: {e}") | |
| return False | |
| def test_health(): | |
| """Test health endpoint""" | |
| print("\nπ Testing health endpoint...") | |
| try: | |
| response = requests.get(f"{BASE_URL}/api/health") | |
| print(f"Health Status: {response.status_code}") | |
| print(f"Health Response: {json.dumps(response.json(), indent=2)}") | |
| return response.status_code == 200 | |
| except Exception as e: | |
| print(f"β Health check failed: {e}") | |
| return False | |
| def test_root(): | |
| """Test root endpoint""" | |
| print("\nπ Testing root endpoint...") | |
| try: | |
| response = requests.get(f"{BASE_URL}/") | |
| print(f"Root Status: {response.status_code}") | |
| print(f"Root Response: {json.dumps(response.json(), indent=2)}") | |
| return response.status_code == 200 | |
| except Exception as e: | |
| print(f"β Root check failed: {e}") | |
| return False | |
| def main(): | |
| """Run all tests""" | |
| print("π Testing HackRX Endpoints") | |
| print("=" * 50) | |
| # Test basic endpoints first | |
| health_ok = test_health() | |
| root_ok = test_root() | |
| if not health_ok: | |
| print("β Server is not responding. Please restart the Flask server.") | |
| return | |
| # Test the main hackrx/run endpoint | |
| hackrx_ok = test_hackrx_run() | |
| print("\n" + "=" * 50) | |
| print("π TEST RESULTS") | |
| print("=" * 50) | |
| print(f"Health Check: {'β PASS' if health_ok else 'β FAIL'}") | |
| print(f"Root Endpoint: {'β PASS' if root_ok else 'β FAIL'}") | |
| print(f"HackRX Run: {'β PASS' if hackrx_ok else 'β FAIL'}") | |
| if hackrx_ok: | |
| print("\nπ Your endpoint is ready for hackathon submission!") | |
| print(f"URL: {BASE_URL}/hackrx/run") | |
| else: | |
| print("\nβ οΈ Please restart your Flask server and try again.") | |
| if __name__ == "__main__": | |
| main() |