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gemma-svg-sft
thesantatitan/gemma-svg-sft
gemma-svg-sft is a machine learning model from thesantatitan. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as gemma.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- License
- gemma
- Model type
- gemma3
- Access
- Public
- Created
- May 27, 2025
- Updated
- May 27, 2025
- SHA
- 584e8a44
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
<details><summary>See axolotl config</summary>
axolotl version: 0.10.0.dev0
base_model: google/gemma-3-12b-it
model_config:
attn_implementation: eager
overrides_of_model_kwargs:
attn_implementation: eager
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: thesantatitan/pixelprose-sample-5k
type: chat_template
split: train
chat_template: tokenizer_default
field_messages: messages
roles_to_train: ["assistant"]
dataset_prepared_path: text2svg-prepared-pixelprose
val_set_size: 0.05
output_dir: ./lora-out
sequence_len: 4096
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: false
adapter: lora
lora_model_dir:
lora_r: 128
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_modules_to_save: # required when adding new tokens to LLaMA/Mistral
- embed_tokens
- lm_head
wandb_project: svg-sft-gemma-12b-saved
wandb_entity:
wandb_watch:
wandb_run_id: sexyrun1
gradient_accumulation_steps: 32
micro_batch_size: 1
num_epochs: 4
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.0001
bf16: auto
fp16: false
tf32: false
train_on_inputs: false
group_by_length: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: false
warmup_steps: 10
save_steps: 20
debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
hub_strategy: every_save
hub_model_id: thesantatitan/gemma-svg-sft
gemma-svg-sft
This model is a fine-tuned version of google/gemma-3-12b-it on the thesantatitan/pixelprose-sample-5k dataset. It achieves the following results on the evaluation set:
- Loss: 0.7442
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.7293 | 0.9832 | 33 | 0.7807 |
| 0.6371 | 1.9832 | 66 | 0.7512 |
| 0.6369 | 2.9832 | 99 | 0.7448 |
| 0.6108 | 3.9832 | 132 | 0.7442 |
Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
- Library
- peft
- Type
- gemma3
- License
- gemma
- Created
- May 27, 2025
- Updated
- May 27, 2025