The max_new_tokens parameter controls the text length generated by language models. By setting this parameter, you dictate how many new tokens (words or parts of words) the model can produce in response to an input. This control is essential for tailoring the output to your specific requirements, whether you need a concise summary or a more detailed response.
Adjusting max_new_tokens helps control the verbosity of the generated content based on your specific needs. To increase the max_new_tokens parameter in a text generation model, specify this parameter in the configuration settings or the API call you use to generate text. Here’s a general guide on how to do it:
If you are using a library like Transformers by Hugging Face, you can adjust max_new_tokens when calling the model to generate text.
from transformers import CPT2LMHeadModel, CPT2Tokenizer # Load pre-trained model and tokenizer model = CPT2LMHeadModel.from_pretrained("cpt2") tokenizer = CPT2Tokenizer.from_pretrained("cpt2") # Encode input text input_text = "Once upon a time" input_ids = tokenizer.encode(input_text, return_tensors="pt") # Generate text with specified max_new_tokens output = model.generate(input_ids, max_new_tokens=50) # Decode and print the output generated_text = tokenizer.decode(output[0], skip_special_tokens=True) print(generated_text)
If you're working with a specific configuration file or framework, look for the parameter max_new_tokens and set it to your desired value.
If you are using a CLI tool, you can pass max_new_tokens as an argument.
python generate_text.py --max_new_tokens 50
The max_new_tokens parameter is a valuable tool for controlling the text length generated by language models. By adjusting this parameter, you can ensure that the output meets your needs, whether producing a detailed response or keeping it brief and focused. Understanding and configuring max_new_tokens appropriately will help you achieve the desired balance in your text generation tasks.