kiri VS haystack

I'm one of the authors of Backprop, a library built for transfer learning.

I'd like to share Backprop, an open source library I've been co-authoring for the last few months.

We've also got an open-source library that makes using + finetuning these models possible in a few lines of code.

I'd like to share Backprop, a Python library I've been co-authoring for the last few months. Our goal is to make finetuning and using models as easy as possible, even without extensive ML experience.

You might get some really promising results with finetuning.

If anything, you could build writing assistance that almost automates responses.

I've been co-authoring a library that lets you finetune such models in a single line of code.

https://github.com/backprop-ai/backprop

In specific the text generation finetuning example should be what you are looking for: https://github.com/backprop-ai/backprop/blob/main/examples/F...

Hope this helps, happy to chat more about it. Pretty curious about the results.

Here's an example Jupyter Notebook for finetuning T5. Full disclosure, I work on this library myself -- but it could be helpful.

I'm working on a library that hopefully makes working with some of these a bit easier -- here's an example notebook for running text classification with the BART checkpoint, if you're interested. If you need more task-specific finetuning for text classification, that's going to be rolled out in the near future.

I'm working on a library that includes a few different ML tasks, including summarisation. It uses a pretrained version of Google's T5 transformer model, which we host on Hugging Face with some details on how it was trained.

I was confused for a bit but there is no relation to https://haystack.deepset.ai/

View on GitHub

4. Haystack by Deepset | Github | tutorial

Haystack can be classified as an end-to-end framework for building applications powered by various NLP technologies, including but not limited to generative AI. While it doesn't directly focus on building generative models from scratch, it provides a robust platform for:

But if you want an API that you can use to develop your own flow, Haystack from Deepset could be worth a look.

Haystack for production. We cannot afford breaking changes in our production apps. Its stable, documentation is excellent and did I mention its' STABLE!??

I recently conducted some experiments with Llama2 and Haystack (https://github.com/deepset-ai/haystack), the NLP/LLM framework.

The notebook can be helpful for those trying to load Llama2 on Colab.

1) Installed Transformers from the main branch (and other libraries)