This repo contains LoRA adapters for Pixtral-12B. The adapters were generated by fine tuning the model on top Reddit comments that received upvotes from the community.

Read details about training here.

Based on the wonderful model named Pixtral-12B from mistral-community. Pixtral is able to process both images and text and it has the ability to output text. This repo contains the adapters that will convert the original model to a language model whose purpose is to generate engaging comments for posts on the subreddit named r/memes.

This subreddit is about memes, obviously. There are strict rules for content posted in there, basically each posts consist of a title which is a short text and an image which is supposed to be an original meme. These images often contain imprinted text on them, so it's pretty interesting to see how well multimodal LLMs are able to process text found in images.

Be advised: the model is likely to generate text that is aimed to maximize comment "Likes", so it might come up with inappropriate stuff like fake personal stories that are relatable or pure engagement baiting. If you find that hard to believe, remember, it was fine tuned on reddit comments.

Example:

For the post above, current model provided the output:

which was the most upvoted comment. If you ask me, I did not expect it to be that way, but predicting these things is not easy.

There are more examples I tried that were "approved" by the community, however it's not always the case and it depends on a lot more things that are out of the commenter's control, such as the popularity of the post and timing. In general, for most posts I've tried the comments generated were reasonable for my expectations.

Finally, at some point I noticed I got this cool badge for the r/memes subreddit, which does not mean much because it depends on both the quality of your comments but also on the quantity, if I'm not mistaken.

Use the code below to get started with the model

I fine tuned the model using a sample of popular comments from posts that I extracted using Reddit's python API. I started with comments with > 10 upvotes and did some basic filtering, removing long and low quality comments based on my personal standards. About 3% of the model's parameters were trainable. I used 1.5k posts and 12k total comments as training data. The prompt was pretty vanilla, without any carefully crafted prompt engineering tricks.

I tried this training a number of times but I tried my best to filter the training data as much as possible, without greatly affecting the goal of getting upvotes. This decision was made after the previous versions ended up producing text that was pure misinformation or inappropriate. I thought it would be best to avoid extreme cases so I did not make the other versions public. Still, the text produced is likely to be weird (for the lack of a better word), given that the language model was exposed to popular Reddit comments. Also, I filtered out posts that one might consider controversial that were quite popular in the last few days.

For fun and for limit testing text imprinted on images, commonly found in memes :)