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datasets: |
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- roneneldan/TinyStories |
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Model trained on the TinyStories Dataset, see https://arxiv.org/abs/2305.07759 |
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Based on GPT-Neo architecture. |
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License: mit |
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--- |
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hyperparams used to train this model: |
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lr = 5e-4, |
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lr_schedule = constant, |
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wd=0.1, |
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adam_beta1=0.9, adam_beta2 = 0.95, |
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context_length=512, |
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batch_size=80, |
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gradient_accumulation_steps=16 |
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------ EXAMPLE USAGE --- |
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig |
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model = AutoModelForCausalLM.from_pretrained('roneneldan/TinyStories-33M') |
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tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M") |
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prompt = "Once upon a time there was" |
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input_ids = tokenizer.encode(prompt, return_tensors="pt") |
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# Generate completion |
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output = model.generate(input_ids, max_length = 1000, num_beams=1) |
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# Decode the completion |
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output_text = tokenizer.decode(output[0], skip_special_tokens=True) |
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# Print the generated text |
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print(output_text) |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_roneneldan__TinyStories-33M) |
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| Metric | Value | |
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|-----------------------|---------------------------| |
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| Avg. | 24.38 | |
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| ARC (25-shot) | 24.23 | |
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| HellaSwag (10-shot) | 25.69 | |
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| MMLU (5-shot) | 23.82 | |
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| TruthfulQA (0-shot) | 47.64 | |
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| Winogrande (5-shot) | 49.09 | |
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| GSM8K (5-shot) | 0.0 | |
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| DROP (3-shot) | 0.19 | |
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