| ### Model Description | |
| <!-- Provide a longer summary of what this model is. --> | |
| ```python | |
| gen_kwargs = { | |
| "max_new_tokens": 100, | |
| "top_k": 70, | |
| "top_p": 0.8, | |
| "do_sample": True, | |
| "no_repeat_ngram_size": 2, | |
| "bos_token_id": tokenizer.bos_token_id, | |
| "eos_token_id": tokenizer.eos_token_id, | |
| "pad_token_id": tokenizer.pad_token_id, | |
| "temperature": 0.8, | |
| "use_cache": True, | |
| "repetition_penalty": 1.2, | |
| "num_return_sequences": 1 | |
| } | |
| device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") | |
| ft = 'gpt-j-onlyk_v2' | |
| tokenizer = AutoTokenizer.from_pretrained(ft) | |
| model = AutoModelForCausalLM.from_pretrained(ft, torch_dtype=torch.float16, low_cpu_mem_usage=True) | |
| model.to(device) | |
| inp = '''Sophia, 29, a student, meets a male programmer Alex from India, who is 45 <|endoftext|> | |
| Alex: How was your vacation? <|endoftext|> sofie: It was amazing! I went to the beach and it felt like paradise. What about you? | |
| <|endoftext|> Alex: i'm good. Tell me a joke <|endoftext|> Sofie:''' | |
| prepared = tokenizer.encode(inp, return_tensors='pt').to(model.device) | |
| out = model.generate(input_ids=prepared, **gen_kwargs) | |
| generated = tokenizer.decode(out[0]) | |
| ``` |