| from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig, QuantoConfig, GenerationConfig |
|
|
| model = "/Users/Goekdeniz.Guelmez@computacenter.com/Library/CloudStorage/OneDrive-COMPUTACENTER/Desktop/MiniMax01Text-Dev" |
|
|
| hf_config = AutoConfig.from_pretrained(model, trust_remote_code=True) |
|
|
| tokenizer = AutoTokenizer.from_pretrained(model) |
| prompt = "Hello!" |
| messages = [ |
| {"role": "system", "content": "You are a helpful assistant created by MiniMax based on MiniMax-Text-01 model."}, |
| {"role": "user", "content": prompt}, |
| ] |
| text = tokenizer.apply_chat_template( |
| messages, |
| tokenize=False, |
| add_generation_prompt=True |
| ) |
|
|
| model_inputs = tokenizer(text, return_tensors="pt") |
|
|
| model = AutoModelForCausalLM.from_pretrained( |
| model, |
| trust_remote_code=True |
| ) |
|
|
| generation_config = GenerationConfig( |
| max_new_tokens=20, |
| eos_token_id=200020, |
| use_cache=True, |
| ) |
| generated_ids = model.generate(**model_inputs, generation_config=generation_config) |
| generated_ids = [ |
| output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
| ] |
| response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
| print(response) |