本文从 OpenAI 的 AGI 愿景出发，首先分析了在该愿景的驱动下 OpenAI 是如何一步步依据 Scale、Generative Model 两个重要技术判断形成了我们所能观察到的 LLM 发展路线，并对此技术路线的底层逻辑进行了分析；在对愿景和技术选型分析的基础上，报告将 OpenAI 的历史行为与此技术路线进行了拟合，尝试解释了许多让人困...
参考资料: https://www.theinformation.com/articles/meet-mai-1-microsoft-readies-new-ai-model-to-compete-with-google-openai?rc=epv9gi https://www.businessinsider.com/microsoft-training-ai-model-rivals-openais-gpt-4-2024-5 https://www.linkedin.com/feed/update/urn:li:activity:7193273937273712643/...
AI代码解释 client=OpenAI(api_key=api_key)defrecognize_multiple_images():response=client.chat.completions.create(model="gpt-4-vision-preview",messages=[{"role":"user","content":[{"type":"image_url","image_url":"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madiso...
As part of developing these new models, we have come up with a new safety training approach that harnesses their reasoning capabilities to make them adhere to safety and alignment guidelines. By being able to reason about our safety rules in context, it can apply them more effectively. 作为开...
training_loss:训练批次上的损失 training_sequence_accuracy:训练批次中的 补全 中,模型预测的 token 与真实补全 token 完全匹配的百分比。例如,batch_size 为3,如果你的数据包含补全 [[1, 2], [0, 5], [4, 2]],并且模型预测了 [[1, 1], [0, 5], [4, 2]],则此准确率为 2/3 = 0.67。 trai...
Today, we're so excited to be introducing this new way of model customization for our O1 series of models, Reinforcement Fine Tuning, or RFT for short. 今天, 我们非常高兴地向大家介绍这种针对 O1 系列模型的全新模型定制方式,即强化微调(简称 RFT)。 For the first time, developers, researchers, ...
在 GPT 中，良好且通用的数据表示，是 tokenizer 带来的 embedding。良好且通用的数据标注是文本清理和去重的一套方法（因为自然语言训练是 unsupervised training，数据本身就是标注）。良好且通用的算法就是大家熟知的 transformers + autoregressive loss。在 Sora 中，良好且通用的数据表示，是 video compress network ...
3. 提供了一个按钮“Create new”，允许用户开始一个新的微调作业。4. Create a fine-tuned model：...
2018年6月,在谷歌的 Transformer 模型诞生一周年时,OpenAI公司发表了论文“Improving Language Understanding by Generative Pre-training”《用生成式预训练提高模型的语言理解力》,推出了具有1.17亿个参数的GPT-1(Generative Pre-training Transformers, 生成式预训练变...
The new text-embedding-ada-002 model is not outperforming text-similarity-davinci-001 on the SentEval linear probing classification benchmark. For tasks that require training a light-weighted linear layer on top of embedding vectors for classification prediction, we suggest comparing the new model to...