rsaha7

5 ms·

1. 1.▲
   by rsaha7 2y ago

   Thanks for the feedback! Glad you got the default setting working quickly! Right now, we are focussed mostly on offering support for open-source models but we can definitely extend support for OpenAI formats. May I ask what history means?
2. 2.▲
   by rsaha7 2y ago

   Thanks for the feedback! The goal is to extend the training optimization techniques to beyond LoRA / QLoRA :) Happy to have you join our team!
3. 3.▲
   by rsaha7 2y ago

   Thanks for the feedback! 1. The largest model that we have tested is Llama2 13B. For the first phase, we focussed on fine-tuning LLMs in the 1B-13B range. For our next phase, we will focus on 13B-45B'ish -- for this we will have to inc
4. 4.▲
   by rsaha7 2y ago

   Thanks for the feedback! The goal is to offer new techniques via our toolkit as soon as they become available on HuggingFace. To that end, we are aiming to move fast and bring those techniques to the toolkit at the earliest post release.
5. 5.▲
   by rsaha7 2y ago

   Thanks for the feedback! Yes, it is similar to ludwig but we do think that our toolkit is a more lightweight solution to fine-tuning and ablation studies. In most cases, finding the right LLM with the right config on your dataset requires m
6. 6.▲
   by rsaha7 2y ago

   You can fine-tune on your own dataset! As long as your dataset is in one of json, csv or huggingface formats, our toolkit can ingest your data!
7. 7.▲
   by rsaha7 2y ago

   The toolkit does not support UI at this time. We focussed on simplifying the experimentation experience that a data scientist / engineer typically go through. For instance, if you want to find the best LLM with the best configuration f
8. 8.▲
   by rsaha7 2y ago

   The toolkit supports open-source LLMs that are available on HuggingFace. So, that would include Llama2, Falcon, Mistral and the likes.
9. 9.▲
   by rsaha7 2y ago

   Also worth noting that the toolkit comes with 3 settings: 1. Basic - set up your first simple fine-tuning experiment 2. Intermediate - Create custom config files for specialized fine-tuning experiments 3. Advanced - Run ablation studies thr
10. 10.▲
    by rsaha7 2y ago

    Great question. Right now, the roadmap includes extending the training optimizer sections to include techniques beyond LoRA. Furthermore, the testing suite will be extended to add more unit-tests that are task dependent. I know that other r
11. 11.▲
    Show HN: Toolkit for LLM Fine-Tuning, Ablating and Testing(github.com)

    14 points by rsaha7 2y ago|21 comments
12. 12.▲
    Show HN: Leverage Falcon 7B blog post(medium.com)

    1 points by rsaha7 3y ago|0 comments
13. 13.▲
    by rsaha7 3y ago

    I have received a lot of great feedback. We are moving fast to add instructions of how to load your custom dataset, and how to choose prompts to give researchers a finer-level of control. On a separate note, I have received a few questions
14. 14.▲
    by rsaha7 3y ago

    Thanks a ton! And that’s a great question! Before starting this project, I realised that while there are a ton of resources that talk about using these models for chat inference and QnA over documents — no one did a good job of stress-testi
15. 15.▲
    by rsaha7 3y ago

    This is a very common use-case, and other users have mentioned this as well. We have taken this feedback, and will move fast to add instructions on how to leverage these models on custom-datasets and custom prompts. Stay tuned!
16. 16.▲
    by rsaha7 3y ago

    I don’t think you fully understand the scope of this project. Your thinking and arguments are limited by your understanding of what all is possible with these models. This repository argues that LLMs can be used for more applications beyond
17. 17.▲
    by rsaha7 3y ago

    Great feedback! We are working on adding instructions on loading custom datasets for your own needs. What the format of the prompt should be, etc. Next release will have these features.
18. 18.▲
    by rsaha7 3y ago

    Looked at the project. Great initiative.
19. 19.▲
    by rsaha7 3y ago

    Feedback taken. We are working on making it more explicit for users to mention the task and dataset they want to train models on. Additionally, we will introduce a flag to let people mention the prompt they want to use for finetuning these
20. 20.▲
    by rsaha7 3y ago

    Great observation. We are working on making this part very explicit. The goal was to let researchers get up to speed with the codebase to begin with, and then they would understand what needs to change to make these models work on custom da
21. 21.▲
    by rsaha7 3y ago

    You are right in that the loading is right now on huggingface’s dataset. The feedback about it being opaque has merit, and we are working on giving users more control and visibility into the dataset loading. To your point, adding instructio
22. 22.▲
    Show HN: finetune LLMs via the Finetuning Hub(github.com)

    80 points by rsaha7 3y ago|17 comments