| import spaces |
| def extract_top_level_json(s: str) -> str: |
| start = s.find("{") |
| if start == -1: |
| return "" |
| depth = 0 |
| for i in range(start, len(s)): |
| ch = s[i] |
| if ch == "{": |
| depth += 1 |
| elif ch == "}": |
| depth -= 1 |
| if depth == 0: |
| candidate = s[start:i + 1] |
| try: |
| json.loads(candidate) |
| return candidate |
| except Exception: |
| return "" |
| return "" |
| @spaces.GPU(duration=25) |
| def get_local_model_gpu(model_id: str): |
| import torch |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
| device = "cuda" if torch.cuda.is_available() else "cpu" |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16 if device == "cuda" else torch.float32) |
| model.to(device) |
| model.eval() |
| return model |
