| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| # Load tokenizer and model | |
| tokenizer = AutoTokenizer.from_pretrained("/content//TSLAM-4B", use_auth_token=None) | |
| model = AutoModelForCausalLM.from_pretrained("/content//TSLAM-4B", use_auth_token=None) | |
| # Set device | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| # Example text input | |
| text_input = "How QOS is applied on routers?" | |
| p=""" | |
| <|system|> | |
| You are a helpful assistant.<|end|> | |
| <|user|>""" + text_input + """<|end|> | |
| <|assistant|> | |
| """ | |
| # Tokenize and move input to device | |
| inputs = tokenizer(p, return_tensors="pt") | |
| inputs = inputs.to(device) | |
| print("User Query: " + text_input) | |
| # Generate text on the device | |
| outputs = model.generate(**inputs, max_length=2000, num_return_sequences=1) | |
| print("Model Response: ") | |
| # Decode generated text | |
| for output in outputs: | |
| generated_text = tokenizer.decode(output, skip_special_tokens=True) | |
| print(generated_text) |