| datasets: | |
| - roneneldan/TinyStories | |
| Model trained on the TinyStories Dataset, see https://arxiv.org/abs/2305.07759 | |
| Based on GPT-Neo architecture. | |
| License: mit | |
| --- | |
| hyperparams used to train this model: | |
| ``` | |
| lr = 5e-4, | |
| lr_schedule = constant, | |
| wd=0.1, | |
| adam_beta1=0.9, adam_beta2 = 0.95, | |
| context_length=512, | |
| batch_size=80, | |
| gradient_accumulation_steps=16 | |
| ``` | |
| ------ EXAMPLE USAGE --- | |
| ```py | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig | |
| model = AutoModelForCausalLM.from_pretrained('roneneldan/TinyStories-33M') | |
| tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M") | |
| prompt = "Once upon a time there was" | |
| input_ids = tokenizer.encode(prompt, return_tensors="pt") | |
| # Generate completion | |
| output = model.generate(input_ids, max_length = 1000, num_beams=1) | |
| # Decode the completion | |
| output_text = tokenizer.decode(output[0], skip_special_tokens=True) | |
| # Print the generated text | |
| print(output_text) | |
| ``` |