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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 using GPT2 Architecture instead of GPT-Neo Architecture orignally used in the paper (https://arxiv.org/abs/2305.07759). Output model is 15mb and has 3 million parameters. |
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# ------ EXAMPLE USAGE 1 --- |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("segestic/Tinystories-gpt-0.1-3m") |
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model = AutoModelForCausalLM.from_pretrained("segestic/Tinystories-gpt-0.1-3m") |
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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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# ------ EXAMPLE USAGE 2 ------ |
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## Use a pipeline as a high-level helper |
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from transformers import pipeline |
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#### pipeline |
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pipe = pipeline("text-generation", model="segestic/Tinystories-gpt-0.1-3m") |
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#### prompt |
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prompt = "where is the little girl" |
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#### generate completion |
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output = pipe(prompt, max_length=1000, num_beams=1) |
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#### decode the completion |
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generated_text = output[0]['generated_text'] |
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#### Print the generated text |
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print(generated_text) |
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