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 Upload Endpoint | |
| This script tests the file upload functionality | |
| """ | |
| import requests | |
| import os | |
| # Your ngrok URL | |
| BASE_URL = "https://0468c638cef7.ngrok-free.app" | |
| def test_upload(): | |
| """Test file upload""" | |
| print("π§ͺ Testing File Upload...") | |
| # Check if we have a test file | |
| test_files = ["doc2.pdf", "main.py", "app.py"] | |
| test_file = None | |
| for file in test_files: | |
| if os.path.exists(file): | |
| test_file = file | |
| break | |
| if not test_file: | |
| print("β No test file found. Please ensure you have a PDF file in the directory.") | |
| return False | |
| print(f"π Using test file: {test_file}") | |
| url = f"{BASE_URL}/hackrx/upload" | |
| try: | |
| with open(test_file, 'rb') as f: | |
| files = {'file': (test_file, f, 'application/pdf')} | |
| print(f"β³ Uploading {test_file}...") | |
| response = requests.post(url, files=files, timeout=60) | |
| print(f"Status: {response.status_code}") | |
| if response.status_code == 200: | |
| print("β Upload successful!") | |
| result = response.json() | |
| print(f"Response: {result}") | |
| return True | |
| else: | |
| print(f"β Upload failed: {response.text}") | |
| return False | |
| except Exception as e: | |
| print(f"β Error: {e}") | |
| return False | |
| def test_health_first(): | |
| """Test health endpoint first""" | |
| print("π§ͺ Testing Health Endpoint...") | |
| try: | |
| response = requests.get(f"{BASE_URL}/api/health", timeout=10) | |
| if response.status_code == 200: | |
| print("β Health check passed!") | |
| return True | |
| else: | |
| print(f"β Health check failed: {response.text}") | |
| return False | |
| except Exception as e: | |
| print(f"β Error: {e}") | |
| return False | |
| def main(): | |
| """Run upload test""" | |
| print("π Upload Test") | |
| print("=" * 40) | |
| # Test health first | |
| if not test_health_first(): | |
| print("\nβ Server not responding. Make sure Flask server is running!") | |
| print("Run: python app.py") | |
| return | |
| # Test upload | |
| test_upload() | |
| print("\n" + "=" * 40) | |
| print("π POSTMAN UPLOAD GUIDE") | |
| print("=" * 40) | |
| print("1. Method: POST") | |
| print(f"2. URL: {BASE_URL}/hackrx/upload") | |
| print("3. Body: form-data") | |
| print("4. Key: file (Type: File)") | |
| print("5. Value: Select your PDF file") | |
| print("=" * 40) | |
| if __name__ == "__main__": | |
| main() |