https://huggingface.co/kalomaze/MiniSymposium-Demo

MiniSymposium is an experimental model that I created based on Mistral 7b. I created it attempting to test these goals:

  1. Demonstrate the untapped potential of using a small, focused dataset of handwritten examples instead of training on a large amount of synthetic GPT outputs, by lowering the learning rate and doing many passes over the small dataset
  2. Create a dataset that allows the model to explore different possible answers from multiple perspectives before reaching a final conclusion (‘Socratic prompting’?)
  3. Develop a model that performs well across various pseudo-markdown prompt formats, rather than overfitting to a specific kind of format such as ChatML, which should naturally benefit other general purpose use cases

The current trend in QLora/Lora-based finetuning (and finetuning in general for local LLMs) is to use large synthetic datasets. These are typically GPT-generated datasets trained with higher learning rates.

However, I believe there is a lot of potential in using small, hand-written datasets with low learning rates, even if it’s for general-purpose instruction following, as long as you train it for many epochs on a learning rate low enough to avoid overfitting.

This approach, I hypothesize, helps the model to learn the deeper patterns of instruction following , including the small details. This should help to avoid shallow data biases (like “As an AI made by OpenAI” and other GPT-isms) that are irrelevant to deeper instruction following patterns, especially in long context and multiturn scenarios.

My initial configuration for this QLora model used a constant learning rate of 1e-6 (0.000001), which resulted in obvious, massive overfitting after about 100 epochs. The model started reproducing the original dataset almost verbatim, and exhibited poor generalization across different prompt formats, including obvious hallucinations & also Chinese language outputs for some reason.

However, turning down the learning rate to 1/10th of (1e-7, which is 0.0000001) significantly improved the model with the same exact small dataset. I trained for about ~10 hours on my RTX 3060 to 600 epochs; I think it’s still a little undertrained, but I encourage people to try the demo model out in the meantime.

https://preview.redd.it/54imvd09ee2c1.png?width=1561&format=png&auto=webp&s=a0e603f5f5a960189b0d225ab5581f2a0339d12d

https://preview.redd.it/al6gmpuaee2c1.png?width=1132&format=png&auto=webp&s=5704aa41e87a5555664405d2f0178287bd7bde35

https://preview.redd.it/7fs90ictee2c1.png?width=1140&format=png&auto=webp&s=7f94c1d76493673d83e0d066efe9f43e21205fe7

It’s designed to be very adaptable to different prompt formats and playing roles, and I’ve gotten some fun and sometimes surprisingly good outputs so far.

A few samples of the training data are formatted like this to help avoid blatant overconfidence in its outputs, to serve as a sort of self-correction mechanism:

https://preview.redd.it/vlmyw1smfe2c1.png?width=2448&format=png&auto=webp&s=4c2cfea77188b9529c2c0c1c1fe29af9d152f0bf

Let me know how this model goes. There’s lots of merges of models that are all sort of doing the same thing, so I figured a more experimental approach would be appreciated. I think there is still more optimization for LR/epoch balance, and I’ll probably add some more examples of specific tasks like Summarization in the dataset so that it’s not *too* small (but still lightweight enough to generalize well).

  • vasileer@alien.topBEnglish
    1·
    1 year ago

    Multiple passes at lower learning rates isn’t supposed to produce different results.

    Overfitting is not a technical challenge, its a mathematical property which undeniably exists when ever the training data is smaller than the full problem domain and simultaneously the learning rate (importantly - multiplied by the number of epochs!) would result in a higher specialization ration on the learned to unobserved data than would be expected based on the ration of the learned to unobserved size.

    Basically if you learn 1 digit addition but half your training sets involve the left number being 1 and none of your training sets involve your left number being 5 then likely your model will treat 5 and 1 the same (since it’s so over trained on examples with 1s)

    GPT-4:

    The statement contains several inaccuracies:

    1. Multiple passes at lower learning rates: It’s not entirely true that multiple passes with lower learning rates will produce identical results. Different learning rates can lead to different convergence properties, and multiple passes with lower learning rates can help in fine-tuning the model and potentially avoid overfitting by making smaller, more precise updates to the weights.
    2. Overfitting as a mathematical property: Overfitting is indeed more of an empirical observation than a strict mathematical property. It is a phenomenon where a model learns the training data too well, including its noise and outliers, which harms its performance on unseen data. It’s not strictly due to the size of the training data but rather the model’s capacity to learn from it relative to its complexity.
    3. Learning rate multiplied by the number of epochs: The learning rate and the number of epochs are both factors in a model’s training process, but their product is not a direct measure of specialization. Instead, it’s the learning rate’s influence on weight updates over time (across epochs) that can affect specialization. Moreover, a model’s capacity and the regularization techniques applied also significantly influence overfitting.
    4. Example of learning 1 digit addition: The example given is somewhat simplistic and does not fully capture the complexities of overfitting. Overfitting would mean the model performs well on the training data (numbers with 1) but poorly on unseen data (numbers with 5). However, the example also suggests a sampling bias in the training data, which is a separate issue from overfitting. Sampling bias can lead to a model that doesn’t generalize well because it hasn’t been exposed to a representative range of the problem domain.

    Overall, while the intention of the statement is to describe overfitting and the effects of learning rates, it conflates different concepts and could benefit from clearer differentiation between them.

    • kindacognizant@alien.topOPBEnglish
      1·
      1 year ago

      I am inclined to believe gpt4 since it consistently claims this across both the API and your comment… but I’m not sure

    • ganzzahl@alien.topBEnglish
      1·
      1 year ago

      Much simpler than GPT-4 – the person above seems to be referring to gradient accumulation (since they mentioned minibatches), where you add up gradients until you reach the target batch size, then apply them. This is perfectly equivalent to training on a larger batch.

      Actually training on small batches with a low learning rate, however, and applying the gradients immediately, is definitely not equivalent to a bigger batch with a bigger learning rate, especially if you’re in a particularly unstable part of parameter space, where large learning rates might overshoot. On the other hand, the tiny batches would tend to make the direction your model moves somewhat random, which might be good, might be bad.

      Whether or not this actually does what OP wants it to is really just an empirical question. If they did it, and it worked better than bigger batches with the same data, then I guess it helped (in this case with this model and this data), haha