google/gemma-3n-E2B-it-litert-preview
Image-Text-to-Text
•
Updated
•
369
Adzuki
melt-adzuki
·
AI & ML interests
None yet
Recent Activity
liked
a model
5 days ago
google/gemma-3n-E2B-it-litert-preview
liked
a model
5 days ago
Miwa-Keita/zenz-v2.5-medium
liked
a dataset
5 days ago
Miwa-Keita/zenz-v2.5-dataset
Organizations
melt-adzuki's activity
liked
2
models
5 days ago
liked
a
dataset
5 days ago
updated
a
model
7 days ago
published
a
model
7 days ago
liked
a
dataset
7 days ago
liked
a
dataset
4 months ago
liked
a
Space
4 months ago
reacted to
Elizezen's
post with 🤯
4 months ago
Post
4033
It turned out that the following simple method seems to be actually effective when you want to increase the appearance probability of only one or a very limited number of tokens.
By training LoRA with unsloth based on the .txt file generated by the code above, you can increase the appearance probability of specific tokens while maintaining the model's performance to great extent. However, it's better to stop the training before train loss becomes 0.0, as it will start spamming the token once it appears even once. In general, you can stop training at a very early stage and it will still work.
It is also possible to reduce the appearance probability of specific tokens by creating an over-learned LoRA with the specific tokens you want to reduce, combining it with the model, and then creating a model that extracts only the difference using the chat vector method and subtracting it from an arbitrary model.
In this case, it is better to set the ratio of chat vector to about five times. It has very little effect on the overall performance, apart from the specific tokens.
import os
one_token = "♡" # Token to increase the appearance probability
value = 1000000
token = one_token * value
with open("one-token.txt", "w", encoding="utf-8") as f:
f.write(token)By training LoRA with unsloth based on the .txt file generated by the code above, you can increase the appearance probability of specific tokens while maintaining the model's performance to great extent. However, it's better to stop the training before train loss becomes 0.0, as it will start spamming the token once it appears even once. In general, you can stop training at a very early stage and it will still work.
It is also possible to reduce the appearance probability of specific tokens by creating an over-learned LoRA with the specific tokens you want to reduce, combining it with the model, and then creating a model that extracts only the difference using the chat vector method and subtracting it from an arbitrary model.
In this case, it is better to set the ratio of chat vector to about five times. It has very little effect on the overall performance, apart from the specific tokens.
new_v = v - (5.0 * chat_vector[i].to(v.device))updated
a
model
4 months ago
published
a
model
4 months ago
updated
a
model
4 months ago
published
a
model
4 months ago
liked
a
Space
4 months ago
updated
a
model
4 months ago
published
a
model
4 months ago
upvoted
a
paper
5 months ago
liked
2
models
5 months ago
liked
a
Space
5 months ago