Gemma4-12b-it-styletuned
I'm still fascinated by the topic of style tuning using the Gryphe method. I have now started calling it SSFT (Supervised Style Fine-Tuning). It's not about making a model "better" at all, but rather about the countless possibilities that arise from this kind of fine-tuning. That's why I also did another run with Gemma4-12b-it and my dataset.
With Gemma, I had to be a bit more aggressive during the training.
The style change was also significant, and Gemma4-12b-it shows that I haven't changed much, if anything, about Gemma's intelligence and approach. However, the tone and expression have completely changed, in my opinion. Perhaps I'm just so caught up in a loop that I'm imagining it.
Training Setup
training:
max_seq_length: 4096
num_epochs: 3
learning_rate: 0.0004
batch_size: 2
gradient_accumulation_steps: 4
warmup_steps: 10
max_steps: 0
save_steps: 200
eval_steps: 0
weight_decay: 0.001
random_seed: 3407
packing: false
train_on_completions: true
gradient_checkpointing: unsloth
optim: adamw_8bit
lr_scheduler_type: linear
vision_image_size: null
lora:
lora_r: 128
lora_alpha: 256
lora_dropout: 0
target_modules:
- lm_head
use_rslora: false
use_loftq: false
finetune_vision_layers: false
finetune_language_layers: false
finetune_attention_modules: false
finetune_mlp_modules: false
An example comparison of vanilla and ssft is here: https://huggingface.co/ewald1976/gemma4-12b-it-styletuned/blob/main/examples.md