h2o-danube2 with ChatML template
This model was first fine-tuned with BAdam on mlabonne/orpo-dpo-mix-40k, but as SFT and not DPO, using LLama-Factory.
Quants
Much love, mradermacher!
Template
<|im_start|>user
{{instruction}}<|im_end|>
<|im_start|>assistant
{{response}}<|im_end|>
BAdam config
### model
model_name_or_path: danube2-base-chatml
### method
stage: sft
do_train: true
finetuning_type: full
use_badam: true
badam_switch_mode: ascending
badam_switch_interval: 50
badam_verbose: 1
badam_start_block: 12
badam_mask_mode: scatter
seed: 314
### dataset
dataset: orpo_sft_mix_40k
template: hermes_chatml
cutoff_len: 8192
overwrite_cache: false
preprocessing_num_workers: 12
### output
output_dir: orpo-chatml-badam
logging_steps: 5
save_steps: 1
save_strategy: epoch
plot_loss: true
overwrite_output_dir: false
### train
per_device_train_batch_size: 2
gradient_accumulation_steps: 8
learning_rate: 0.00001
num_train_epochs: 2
lr_scheduler_type: cosine
warmup_ratio: 0.01
pure_bf16: true
flash_attn: fa2
### eval
val_size: 0.01
per_device_eval_batch_size: 1
eval_strategy: steps
eval_steps: 1000
BAdam training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.7474 | 0.3653 | 1000 | 0.8887 |
| 0.9106 | 0.7306 | 2000 | 0.8681 |
| 0.8121 | 1.0958 | 3000 | 0.8635 |
| 0.8636 | 1.4611 | 4000 | 0.8562 |
| 0.8 | 1.8264 | 5000 | 0.8565 |