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--- |
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datasets: |
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- roneneldan/TinyStories |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-generation |
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--- |
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We tried to use the huggingface transformers library to recreate the TinyStories models on Consumer GPU. Output model has 9 million parameters. |
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Tweaked code of springtangent (https://github.com/springtangent/tinystoriestrainer/blob/main/tinystories_train.py) |
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Code credit - springtangent |
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------ EXAMPLE USAGE --- |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("segestic/Tinystories-0.1-9m") |
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model = AutoModelForCausalLM.from_pretrained("segestic/Tinystories-0.1-9m") |
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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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