| import torch | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| model_path = "./Path-to-llm-folder" | |
| tokenizer = AutoTokenizer.from_pretrained(model_path) | |
| model = AutoModelForCausalLM.from_pretrained(model_path) | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model.to(device) | |
| def generate_text(prompt, max_length=2000): | |
| inputs = tokenizer(prompt, return_tensors="pt").to(device) | |
| output = model.generate( | |
| **inputs, | |
| do_sample=True, | |
| temperature=0.7 | |
| ) | |
| return tokenizer.decode(output[0], skip_special_tokens=True) | |
| prompt = "Write a code in react for calling api to server at https://example.com/test" | |
| generated_text = generate_text(prompt) | |
| print(generated_text) | |