AnirudhRajagopalan1201/tinyllama-37M

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Model trained on the TinyStories Dataset, replicating https://arxiv.org/abs/2305.07759, based on LLaMA architecture.

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Hyperparams used to train this model: "batch_size": 32, "block_size": 256, "lr": 5e-4, "num_hidden_layers": 6, "num_attention_heads": 6, "hidden_size": 288, "dropout": 0.1, "weight_decay": 0.01, "epochs": 1, "eval_interval": 200, "eval_steps": 50, "vocab_size": 50257, "warmup_tokens": 10000, "gradient_accumulation_steps": 8,

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-37M')
  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)