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.

The new transformers release describes the model: https://github.com/huggingface/transformers/releases/tag/v4....

> GPT OSS is a hugely anticipated open-weights release by OpenAI, designed for powerful reasoning, agentic tasks, and versatile developer use cases. It comprises two models: a big one with 117B parameters (gpt-oss-120b), and a smaller one with 21B parameters (gpt-oss-20b). Both are mixture-of-experts (MoEs) and use a 4-bit quantization scheme (MXFP4), enabling fast inference (thanks to fewer active parameters, see details below) while keeping resource usage low. The large model fits on a single H100 GPU, while the small one runs within 16GB of memory and is perfect for consumer hardware and on-device applications.

pip install torch pip install git+https://github.com/huggingface/transformers pip install git+https://github.com/huggingface/accelerate pip install huggingface_hub pip install --upgrade vllm pip install --upgrade mistral_common chal

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 pip install git+https://github.com/huggingface/transformers pip install git+https://github.com/huggingface/accelerate pip install huggingface_hub

Substitute your own embeddings database to change the knowledge base. txtai supports running local LLMs via transformers or llama.cpp. It also supports a wide variety of LLMs via LiteLLM. For example, setting the 2nd RAG pipeline parameter below to gpt-4o along with the appropriate environment variables with access keys switches to a hosted LLM. See this documentation page for more on this.

Reference implementations are unmaintained and buggy.

For example https://github.com/huggingface/transformers/issues/27961 OpenAI's tokenizer for CLIP is buggy, it's a reference implementation, it isn't the one they used for training, and the problems with it go unsolved and get copied endlessly by other projects.

What about Flux? They don't say it was used for training, it wasn't, there are bugs with it that break cudagraphs or similar that aren't that impactful. On the other hand, it uses CLIP, and CLIP is buggy, so this is buggy...

We see an addition to the AI community with AutoGPT. Along with Tensorflow they represent the AI community in the software category, which is getting relevant (2 out of 8). We can expect in the future to have new AI projects in the top 25 such as Transformers or Ollama (currently top 34 and 36, respectively).

pip install torch pip install git+https://github.com/huggingface/transformers pip install git+https://github.com/huggingface/accelerate pip install huggingface_hub