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 the Flask API server | |
| Demonstrates how to use the various endpoints | |
| """ | |
| import requests | |
| import json | |
| import os | |
| # API Configuration | |
| BASE_URL = "http://127.0.0.1:5000" | |
| HEADERS = { | |
| "Content-Type": "application/json" | |
| } | |
| def test_health_check(): | |
| """Test the health check endpoint""" | |
| print("π Testing health check...") | |
| try: | |
| response = requests.get(f"{BASE_URL}/health") | |
| print(f"Status: {response.status_code}") | |
| print(f"Response: {response.json()}") | |
| return response.status_code == 200 | |
| except Exception as e: | |
| print(f"β Health check failed: {e}") | |
| return False | |
| def test_system_status(): | |
| """Test the system status endpoint""" | |
| print("\nπ Testing system status...") | |
| try: | |
| response = requests.get(f"{BASE_URL}/hackrx/status") | |
| print(f"Status: {response.status_code}") | |
| print(f"Response: {json.dumps(response.json(), indent=2)}") | |
| return response.status_code == 200 | |
| except Exception as e: | |
| print(f"β System status failed: {e}") | |
| return False | |
| def test_query_processing(questions): | |
| """Test query processing""" | |
| print(f"\nπ€ Testing query processing...") | |
| payload = { | |
| "questions": questions | |
| } | |
| try: | |
| response = requests.post(f"{BASE_URL}/hackrx/run", | |
| json=payload, | |
| headers=HEADERS) | |
| print(f"Status: {response.status_code}") | |
| print(f"Response: {json.dumps(response.json(), indent=2)}") | |
| return response.status_code == 200 | |
| except Exception as e: | |
| print(f"β Query processing failed: {e}") | |
| return False | |
| def test_upload_document(file_path): | |
| """Test document upload""" | |
| print(f"\nπ Testing document upload: {file_path}") | |
| if not os.path.exists(file_path): | |
| print(f"β File not found: {file_path}") | |
| return False | |
| try: | |
| with open(file_path, 'rb') as f: | |
| files = {'file': f} | |
| response = requests.post(f"{BASE_URL}/hackrx/upload", | |
| files=files) | |
| print(f"Status: {response.status_code}") | |
| print(f"Response: {json.dumps(response.json(), indent=2)}") | |
| return response.status_code == 200 | |
| except Exception as e: | |
| print(f"β Upload failed: {e}") | |
| return False | |
| def test_query_processing(questions): | |
| """Test query processing""" | |
| print(f"\nπ€ Testing query processing...") | |
| payload = { | |
| "questions": questions | |
| } | |
| try: | |
| response = requests.post(f"{BASE_URL}/hackrx/run", | |
| json=payload, | |
| headers=HEADERS) | |
| print(f"Status: {response.status_code}") | |
| print(f"Response: {json.dumps(response.json(), indent=2)}") | |
| return response.status_code == 200 | |
| except Exception as e: | |
| print(f"β Query processing failed: {e}") | |
| return False | |
| def main(): | |
| """Main test function""" | |
| print("π Starting API Tests") | |
| print("=" * 50) | |
| # Test 1: Health check | |
| if not test_health_check(): | |
| print("β Health check failed. Make sure the server is running.") | |
| return | |
| # Test 2: System status | |
| test_system_status() | |
| # Test 4: Document upload (if file exists) | |
| test_files = ["doc2.pdf", "test_document.txt"] | |
| for test_file in test_files: | |
| if os.path.exists(test_file): | |
| test_upload_document(test_file) | |
| break | |
| # Test 3: Query processing | |
| test_questions = [ | |
| "What is covered under this policy?", | |
| "What is the maximum coverage amount?", | |
| "What documents are required for claims?" | |
| ] | |
| test_query_processing(test_questions) | |
| print("\nβ API tests completed!") | |
| if __name__ == "__main__": | |
| main() |