Update README.md

README.md

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----
-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": 64,
-        "block_size": 128,
-        "lr": 6e-4,
-        "num_hidden_layers": 8,
-        "num_attention_heads": 8,
-        "hidden_size": 160,
-        "dropout": 0.1,
-        "weight_decay": 0.01,
-        "epochs": 5,
-        "eval_interval": 200,
-        "eval_steps": 50,
-        "vocab_size": 50257, 
-        "warmup_tokens": 10000,
-        "gradient_accumulation_steps": 16,
-```
----
-EXAMPLE USAGE 
-```py
-  !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)
-  
-```