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