Infernaught
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README.md
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "mistralai/Mistral-7B-v0.1"
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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: Topic identification from a news article, with synthetic explanations.\
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Original dataset: https://huggingface.co/datasets/ag_news\
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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 Explanation and the name is News Topic Identification Explained (ag_news)\
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---\
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Sample input: Below is a news article. Please classify it under one of the following classes (World, Business, Sports, Sci/Tech) and provide a reasonable coherent explanation for why the article is classified as such. Please format your response as a JSON payload.\n\n### Article: US poverty rate climbs, along with number lacking health coverage (AFP) AFP - The number of Americans living in poverty or without health insurance grew last year, a government survey showed, adding potential dynamite in the battle for the White House.\n\n### JSON Response\
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---\
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Sample output: {"text_label": "World", "explanation": "The article is classified as 'World' because it discusses the poverty rate and lack of health coverage in the United States. Although it is a domestic issue, it has potential implications for the upcoming presidential election, making it relevant to a global audience."}\
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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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