| #!/usr/bin/env python3 | |
| """ | |
| Example: generate text from QED-75M on Hugging Face. | |
| Run: | |
| python generate_gravity_example.py | |
| """ | |
| from __future__ import annotations | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| def main() -> None: | |
| repo_id = "levossadtchi/QED-75M" | |
| prompt = "Explain gravity in one sentence. \n<|assistant|>" | |
| # trust_remote_code=True is required because QED is a custom architecture. | |
| tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| repo_id, | |
| trust_remote_code=True, | |
| torch_dtype=torch.float32, | |
| ) | |
| model.eval() | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model.to(device) | |
| inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to(device) | |
| with torch.no_grad(): | |
| out_ids = model.generate( | |
| **inputs, | |
| max_new_tokens=64, | |
| do_sample=True, | |
| temperature=0.8, | |
| top_k=50, | |
| eos_token_id=tokenizer.eos_token_id, | |
| pad_token_id=tokenizer.pad_token_id, | |
| ) | |
| text = tokenizer.decode(out_ids[0], skip_special_tokens=True) | |
| print(text) | |
| if __name__ == "__main__": | |
| main() | |