| # /// script | |
| # dependencies = ["torch", "transformers", "accelerate"] | |
| # /// | |
| import torch | |
| import json | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| model_id = "AbdelrehmanFouad/t5-efficient-base-usdjpy-forecaster" | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.float32) | |
| input_data = { | |
| "context_window": [150.0] * 168, | |
| "macro_events": [] | |
| } | |
| prompt = "forecast usdjpy: " + json.dumps(input_data) | |
| inputs = tokenizer(prompt, return_tensors="pt", max_length=1024, truncation=True).to(model.device) | |
| with torch.no_grad(): | |
| outputs = model.generate(**inputs, max_new_tokens=512) | |
| prediction = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| print("\n--- RAW PREDICTION ---") | |
| print(prediction) | |
| print("--- END ---") | |