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
File size: 3,139 Bytes
09281fe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | #!/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() |