metadata
datasets:
- roneneldan/TinyStories
Model trained on the TinyStories Dataset, replicating https://arxiv.org/abs/2305.07759, based on LLaMA architecture.
Hyperparams used to train this model:
"batch_size": 32, "block_size": 256, "lr": 5e-4, "num_hidden_layers": 8, "num_attention_heads": 8, "hidden_size": 160, "dropout": 0.1, "weight_decay": 0.01, "epochs": 1, "eval_interval": 200, "eval_steps": 50, "vocab_size": 50257, "warmup_tokens": 10000, "gradient_accumulation_steps": 16,
EXAMPLE USAGE
!pip install --quiet transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from huggingface_hub import notebook_login, login
import os
#login to hf to check for llama access
hf_token = os.getenv('HF_TOKEN')
login(token=hf_token)
model = AutoModelForCausalLM.from_pretrained('AnirudhRajagopalan1201/tinyllama-20M')
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
prompt = "Lily likes cats and dogs. She asked her mom for a dog and her mom said no, so instead she asked"
input_ids = tokenizer.encode(prompt, return_tensors="pt")
output = model.generate(input_ids, temperature=0.1, max_length = 100, do_sample=True)
output_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(output_text)