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--- |
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library_name: peft |
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base_model: mistralai/Mistral-7B-v0.1 |
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pipeline_tag: text-generation |
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--- |
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Description: Multiple-choice sentence completion\ |
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Original dataset: https://huggingface.co/datasets/Rowan/hellaswag \ |
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---\ |
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Try querying this adapter for free in Lora Land at https://predibase.com/lora-land! \ |
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The adapter_category is Other and the name is Multiple Choice Sentence Completion (hellaswag)\ |
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---\ |
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Sample input: You are provided with an incomplete passage below as well as 4 endings in quotes and separated by commas, with only one of them being the correct ending. Treat the endings as being labelled 0, 1, 2, 3 in order. Please respond with the number corresponding to the correct ending for the passage.\n\n### Passage: The mother instructs them on how to brush their teeth while laughing. The boy helps his younger sister brush his teeth. she\n\n### Endings: ['shows how to hit the mom and then kiss his dad as well.' |
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'brushes past the camera, looking better soon after.' |
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'glows from the center of the camera as a reaction.' |
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'gets them some water to gargle in their mouths.']\n\n### Correct Ending Number: \ |
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---\ |
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Sample output: 3.0\ |
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---\ |
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Try using this adapter yourself! |
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``` |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_id = "mistralai/Mistral-7B-v0.1" |
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peft_model_id = "predibase/hellaswag" |
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model = AutoModelForCausalLM.from_pretrained(model_id) |
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model.load_adapter(peft_model_id) |
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``` |
