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TL;DR TensorFlow has gone through several major evolutions of the user programming interface since its release in 2015, and this article will provide a brief introduction to these evolutions and discuss the motivations behind them, so as to facilitate the reader’s understanding of TensorFlow’s design thinking at different stages. Notes: By user programming interface, we mean the programming interface that TensorFlow provides to users, not the internal programming interface of TensorFlow....

TL;DR OpenAI’s ChatGPT provides the range and meaning of various parameters in its official documentation (https://platform.openai.com/docs/api-reference/chat/create). We will discuss ChatGPT’s generation process and how these parameters implement its generation effects. ChatGPT’s Decoding Process We assume minGPT (equivalent to GPT-2) and ChatGPT have the same decoding process: https://github.com/karpathy/minGPT/blob/master/mingpt/model.py#LL283C12-L283C12. The overall process can be summarized as the following steps: Expand the user’s request from 1 to a batch size of num_samples Perform model inference to obtain logits Perform temperature mapping: logits = logits / temperature [Optional] Perform topk processing: logits = topk_func(logits, top_k) Map logits to probabilities: probs = softmax(logits) Whether to sample: Sample: idx_next = multinomial_sample(probs, num_samples=1) Don’t sample: idx_next = topk_func(probs, k=1) Repeat the above process max_new_tokens times Decoding Parameters of ChatGPT temperature The official definition of the temperature parameter is:...

TL;DR using relative path as metadata_path to projector will cause TensorBoard cannot find metadata. The correct way is use FQPN (fully-qualified path name, aka absolute path) ...

TL;DR This article show how Whisper work and some Linux programming tricks it used. ...