Llm api 购买

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无广放心食用

https://www.youtube.com/watch?v=ddCYORu41Xs Talking about ways to use an off-the-shelf language model to solve a multiple-choice task. Covering: - Intro to the Kaggle competition - Benchmarking with GPT3.5 - Using the OpenAI function calling API to enforce structure on answers - Using Llama2 as a classifier by examining the logits (next token predictions) - Using perplexity to evaluate question-answer pairs Notebook using the OpenAI API to test GPT3.5: https://www.kaggle.com/johnowhitaker/. Llama2 demo notebook: https://colab.research.google.com/dri. (quickly made for this video, don't trust the calculations, rather start with the below notebook) Notebook testing different open models with the perplexity approach: https://www.kaggle.com/code/takamichi. (a good template to start experimenting since it shows how to run as a submission.

搜狐视频

汇总表格：https://docs.qq.com/smartsheet/DSFlBSWJpUElsbWxt 时间线： 开头: 00:00 谷歌的 Gemini API: 00:45 OpenAI 的数据共享计划: 01:49 Mistral AI 的 La Plateforme: 03:14 Cohere: 03:46 阿里的魔搭 ModelScope: 04:15 字节的火山方舟协作奖励计划: 05:01 智谱的 BigModel: 06:45 上海人工智能实验室的书生系列模型（InternLM 和