| |
| """ |
| 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|>" |
|
|
| |
| 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() |
|
|
|
|