| """Run one request through Sarvam-Prism INT4 (any CUDA GPU with ~10-16 GB, >= 64 GB host RAM). |
| python example.py # from the Hugging Face Hub |
| python example.py /path/to/local/copy # from a local copy |
| python example.py . 12 # cap GPU memory use at 12 GiB |
| """ |
| import json, sys |
| from prism_int4 import Prism |
|
|
| model = Prism.from_pretrained(sys.argv[1] if len(sys.argv) > 1 else "jc-914/prism-quantized", |
| gpu_memory_gb=float(sys.argv[2]) if len(sys.argv) > 2 else None) |
| answers = model.decide( |
| state={"ticket": "My card was charged twice for the same order and I still haven't got a refund."}, |
| questions={ |
| "team": {"type": "choice", "instructions": "Which team should handle this ticket?", |
| "criteria": {"billing": "payments, charges and refunds", "tech": "app or website problems", |
| "delivery": "shipping and order status"}}, |
| "urgent": {"type": "yes_no", "instructions": "Does this need a reply within the hour?"}, |
| "anger": {"type": "score", "instructions": "How upset is the customer?", |
| "criteria": ["calm", "annoyed", "angry", "furious"]}, |
| }) |
| print(json.dumps(answers, indent=2)) |
|
|
