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 | |
| """ | |
| Quick Test for app2.py Server | |
| """ | |
| import requests | |
| import time | |
| BASE_URL = "https://0468c638cef7.ngrok-free.app" | |
| def test_server(): | |
| print("π§ͺ Quick Server Test") | |
| print("=" * 40) | |
| # Test 1: Health Check | |
| print("1. Testing health endpoint...") | |
| try: | |
| response = requests.get(f"{BASE_URL}/api/health", timeout=10) | |
| print(f" Status: {response.status_code}") | |
| if response.status_code == 200: | |
| print(" β Server is running!") | |
| else: | |
| print(f" β Server error: {response.text}") | |
| except Exception as e: | |
| print(f" β Connection failed: {e}") | |
| print(" π‘ Make sure to run: python app2.py") | |
| return False | |
| # Test 2: Simple Query | |
| print("\n2. Testing simple query...") | |
| try: | |
| headers = { | |
| "Content-Type": "application/json", | |
| "Authorization": "Bearer test_key_123" | |
| } | |
| payload = { | |
| "questions": ["What is the grace period for premium payment?"] | |
| } | |
| response = requests.post(f"{BASE_URL}/hackrx/run", | |
| json=payload, headers=headers, timeout=30) | |
| print(f" Status: {response.status_code}") | |
| if response.status_code == 200: | |
| print(" β Query processed successfully!") | |
| result = response.json() | |
| print(f" Answer: {result.get('answers', [''])[0][:100]}...") | |
| else: | |
| print(f" β Query failed: {response.text}") | |
| except Exception as e: | |
| print(f" β Query error: {e}") | |
| print("\n" + "=" * 40) | |
| print("π NEXT STEPS:") | |
| print("1. Make sure app2.py is running: python app2.py") | |
| print("2. Keep the server running in a separate terminal") | |
| print("3. Test your Postman requests") | |
| print("=" * 40) | |
| if __name__ == "__main__": | |
| test_server() |