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| import torch | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import gradio as gr | |
| MODEL = "meta-llama/Llama-3.2-1B-Instruct" | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| MODEL, | |
| torch_dtype=torch.float16, | |
| low_cpu_mem_usage=True, | |
| device_map="auto" | |
| ) | |
| model.config.use_cache = True | |
| def gerar(prompt): | |
| prompt = "Responda direto e curto:\n" + prompt | |
| inputs = tokenizer(prompt, return_tensors="pt") | |
| with torch.no_grad(): | |
| output = model.generate( | |
| **inputs, | |
| max_new_tokens=30, | |
| do_sample=False, | |
| temperature=0.7, | |
| top_p=0.9, | |
| repetition_penalty=1.1 | |
| ) | |
| return tokenizer.decode(output[0], skip_special_tokens=True) | |
| demo = gr.Interface( | |
| fn=gerar, | |
| inputs=gr.Textbox(lines=2), | |
| outputs="text", | |
| title="⚡ Llama 3.2 1B (Modo Bruto)" | |
| ) | |
| demo.launch() |