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I have been meaning to explore fine-tuning llms on my own dataset. Which formats does this toolkit support?
This is a great project, little bit similar to https://github.com/ludwig-ai/ludwig , but it includes testing capabilities and ablation. questions regarding the LLM testing aspect: How extensive is the test coverage for LLM use cases, and what is the current state of this project area? Do you offer any guarantees, or is it considered an open-ended problem? Would love to see more progress toward this direction!
As for the test coverage, right now, the toolkit includes property-based unit tests. For instance, for an LLM fine-tuned on summarization, a property-test will evaluate if the summarized text is smaller in length compared to the actual input text.
Similar to the above test, we have a handful of property-based tests. Of course, the list is not exhaustive at this time. As more progress is being made on the testing side, we aim to distill the most relevant tests depending on use-cases.
Hope this helps.
1. What's the largest model (number of parameters) that you've tested the library with?
2. Will MoE models work as well? They're known to have more unstable training and need some custom techniques to stabilize
This is a great project! But I was wondering how frequently will the library be updated with the new different optimization techniques which keep coming out?
Sounds like a great library to use for automatically testing all the new models being released everyday and finding out if a new open source model significantly performs better on your custom dataset. 1. What's the largest model (number of parameters) that you've tested the library with? 2. Will MoE models work as well? They're known to have more unstable training and need some custom techniques to stabilize
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 incorporate distributed techniques.
2. Following incorporation of distributed training techniques, we will be able to run MoE based models, such as Mixtral.
Cool project. I hope it expands to other forms of tuning LLMs
The goal is to extend the training optimization techniques to beyond LoRA / QLoRA :)
Happy to have you join our team!
Just tried this out and got the default working in a few minutes! Would love to see more support to use finetuning dataset format used for OpenAI and handling for history.
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?