---\library_name: peft\Base model: mistralai/Mistral-7B-v0.1\pipeline_tag: text-generation--\Description: Topic identification from a news article, with synthetic explanations.\Original dataset: https://huggingface.co/datasets/ag_news\---\Try querying this adapter for free in Lora Land at https://predibase.com/lora-land!\The adapter_category is Explanation and the name is News Topic Identification Explained (ag_news)---\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---\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."}---\Try using this adapter yourself! \ ``` from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mistral-7B-v0.1" peft_model_id = "predibase/agnews_explained"
model = AutoModelForCausalLM.from_pretrained(model_id) model.load_adapter(peft_model_id) ```