AlexandrosChariton
/

Reddit-memes-12B

@@ -12,7 +12,7 @@ language:
 pipeline_tag: text-generation
 ---
-# Model Card for AlexandrosChariton/Reddit-memes-pixtral-12B-v4
 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.
@@ -59,8 +59,8 @@ model = LlavaForConditionalGeneration.from_pretrained(model_id, torch_dtype=torc
 processor = AutoProcessor.from_pretrained(model_id)
 # Load the LoRA configuration (This parts downloads current repo)
-peft_config = PeftConfig.from_pretrained("AlexandrosChariton/Reddit-memes-pixtral-12B-v4")
-lora_model = PeftModel.from_pretrained(model, "AlexandrosChariton/Reddit-memes-pixtral-12B-v4")
 # You need a meme in image format to run the model
 image_path = "meme_image.png" # or webp, jpg, jpeg
@@ -84,7 +84,7 @@ I fine tuned the model using a sample of popular comments from posts that I extr
 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.
-You may notice that this repo has a v4 in the title, that's because 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.
 - **Developed by:** me

 pipeline_tag: text-generation
 ---
+# Model Card for AlexandrosChariton/Reddit-memes-12B
 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.
 processor = AutoProcessor.from_pretrained(model_id)
 # Load the LoRA configuration (This parts downloads current repo)
+peft_config = PeftConfig.from_pretrained("AlexandrosChariton/Reddit-memes-12B")
+lora_model = PeftModel.from_pretrained(model, "AlexandrosChariton/Reddit-memes-12B")
 # You need a meme in image format to run the model
 image_path = "meme_image.png" # or webp, jpg, jpeg
 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.
 - **Developed by:** me