XTuner is an efficient, flexible and full-featured toolkit for fine-tuning large models (InternLM, Llama, Baichuan, Qwen, ChatGLM) and released under the Apache 2.0 license. The advantage of this framework is that it is not tied down to a specific LLM architecture, but supports multiple ones out of the box. With the just released version v0.2.0 of our llm-dataset-converter Python library, you can read and write the XTuner JSON format (and apply the usual filtering, of course).

Here are the newly added image tags:

• In-house registry:

  • public.aml-repo.cms.waikato.ac.nz:443/pytorch/pytorch-xtuner:2024-02-19_cuda11.7

• Docker hub:

  • waikatodatamining/pytorch-xtuner:2024-02-19_cuda11.7

Of course, you can use these Docker images in conjunction with our gifr Python library for gradio interfaces as well (gifr-textgen). Just now we released version 0.0.4 of the library, which is more flexible in regards to text generation: it can now support send and receive the conversation history and also parse JSON responses.

Large language models (LLMs) for chatbots are all the rage at the moment, but there is plenty of scope of simpler tasks like text classification. Requiring less resources and being a lot faster is nice as well.

We turned the HuggingFace example for sequence classification into a docker image to make it easy for building such classification models.

• In-house registry:

  • public.aml-repo.cms.waikato.ac.nz:443/pytorch/pytorch-huggingface-transformers:4.36.0_cuda11.7_classification

• Docker hub:

  • waikatodatamining/pytorch-huggingface-transformers:4.36.0_cuda11.7_classification

Our gifr Python library for gradio received an interface for text classification (gifr-textclass) in version 0.0.3.

The llm-dataset-converter library obtained native support for text classification formats with version 0.1.1.