language_tool_python VS transformers

If you are mainly concerned with grammatical errors, maybe the Language Tool could be helpful. There’s a python wrapper for it (https://github.com/jxmorris12/language_tool_python).

Language Tool Python looks like it might be the start to what you're looking for - this article I found shows a pretty similar project in looking for a "grammar quality" sort of score.

Sure, you just need to import a library that codifies the entirety of the English language, such as language tool.

You could use language_tool_python.

Update an keyerror on _save_check_point prevent confusion of missing … #33832

Play around with OpenAI’s GPT models and Hugging Face's Transformers library.

[(https://dev-to-uploads.s3.amazonaws.com/uploads/articles/mwbeic3x9gtowahgunjl.png)](https://github.com/huggingface/transformers)

Don’t repeat yourself is a fundamental principle in computer science. As such, software engineers often use readily available code or pre-built solutions to commonly occurring problems. Consequently, it is common for large machine learning projects to rely on numerous other open source projects. For instance, transformers - a library commonly used to build transformer-based models - rely on over 1000s other open source projects.

A few days ago a PR was merged into the Hugging Face Transformers library implementing this trick.

Thanks to transformers.js, we can run the tokenizer and model locally in the browser. Transformers.js is designed to be functionally equivalent to Hugging Face's transformers python library, meaning you can run the same pretrained models using a very similar API.

Not sure what you are implying. Thomas Wolf has the second highest number of commits on HuggingFace/transformers. He is clearly competent & deeply technical

https://github.com/huggingface/transformers/

Sure, knowing the basics of LLM math is necessary. But it's also _enough_ to know this math to fully grasp the code. There are only 4 concepts - attention, feed-forward net, RMS-normalization and rotary embeddings - organized into a clear structure.

Now compare it to the Hugginface implementation [1]. In addition to the aforementioned concepts, you need to understand the hierarchy of `PreTrainedModel`s, 3 types of attention, 3 types of rotary embeddings, HF's definition of attention mask (which is not the same as mask you read about in transformer tutorials), several types of cache class, dozens of flags to control things like output format or serialization, etc.

It's not that Meta's implementation is good and HF's implementation is bad - they pursue different goals in their own optimal way. But if you just want to learn how the model works, Meta's code base is great.

[1]: https://github.com/huggingface/transformers/blob/main/src/tr...