AlexandrosChariton
/

Reddit-memes-12B

@@ -18,7 +18,7 @@ Based on the wonderful model named Pixtral-12B from mistral-community. Pixtral i
 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.
 Example:
 ```
@@ -28,6 +28,14 @@ Come up with a comment that will get upvoted by the community for a reddit post
 Note: For the model use the image only, I added the username to credit the person posting this and the title to clarify how a meme is structured in this forum
 ### Model Description
 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.
@@ -38,13 +46,8 @@ This decision was made after the previous versions ended up producing text that
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@@ -56,168 +59,11 @@ This decision was made after the previous versions ended up producing text that
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-### Framework versions
 - PEFT 0.14.0

 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:
 ```
 Note: For the model use the image only, I added the username to credit the person posting this and the title to clarify how a meme is structured in this forum
+For the post above, current model provided the output:
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/63da89db9f2687298a0acbfe/1Z6nzIYHRdqaPSaSKXqZ0.png)
+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.
 ### Model Description
 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.
+- **Developed by:** me
+- **Funded by [optional]:** me :(
 ### Model Sources [optional]
 ## Uses
+For fun and for limit testing text imprinted on images, commonly found in memes :)
 ## How to Get Started with the Model
+Use the code below to get started with the model
+Coming soon
 - PEFT 0.14.0