See axolotl config
axolotl version: 0.9.2
base_model: timarni/qwen3_dpo
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
strict: false
chat_template: qwen3
datasets:
- path: timarni/MNLP_intstruction_tuning
type: alpaca
split: train
shuffle_merged_datasets: true
val_set_size: 0.1
output_dir: ./outputs/dpo_full_alpaca
dataset_prepared_path: last_run_prepared
sequence_len: 4096 #2048
sample_packing: true # was true -> need to check if it actually learns on the samples or not (better understand te hyperparam and event. install axolotl to debug)
eval_sample_packing: true
pad_to_sequence_len: true
# train_on_inputs: true # NEW
# group_by_length: false NEW?
# To be sure that no LORA is done
adapter: null
lora: false
merge_lora: false
wandb_project: mnlp_project
wandb_entity: tim-arni
wandb_watch:
wandb_name: dpo_full_alpaca_resume_from_ckpt
wandb_log_model:
gradient_accumulation_steps: 16 # 2
micro_batch_size: 2 # 1
num_epochs: 3
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.00005 # 0.00005
# cosine_min_lr_ratio: 0.1
warmup_ratio: 0.05
weight_decay: 0.01
bf16: auto
tf32: true
gradient_checkpointing: offload
gradient_checkpointing_kwargs:
use_reentrant: false
resume_from_checkpoint: /mloscratch/users/arni/Workspace/mnlp_sft/outputs/dpo_full_alpaca/checkpoint-186
logging_steps: 1
gradient_clipping: 1.0 # or max_grad_norm?
flash_attention: true
evals_per_epoch: 2
saves_per_epoch: 1
save_total_limit: 20
special_tokens:
outputs/dpo_full_alpaca
This model is a fine-tuned version of timarni/qwen3_dpo on the timarni/MNLP_intstruction_tuning dataset. It achieves the following results on the evaluation set:
- Loss: 0.1520
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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 8
- optimizer: Use 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: 13
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.7154 | 0.0107 | 1 | 1.1239 |
| 0.1282 | 0.2567 | 24 | 0.2029 |
| 0.1105 | 0.5134 | 48 | 0.1860 |
| 0.1056 | 0.7701 | 72 | 0.1779 |
| 0.1004 | 1.0214 | 96 | 0.1736 |
| 0.0912 | 1.2781 | 120 | 0.1643 |
| 0.0861 | 1.5348 | 144 | 0.1576 |
| 0.0791 | 1.7914 | 168 | 0.1530 |
| 0.0751 | 2.0642 | 192 | 0.1510 |
| 0.0625 | 2.3209 | 216 | 0.1509 |
| 0.0453 | 2.5775 | 240 | 0.1513 |
| 0.0426 | 2.8342 | 264 | 0.1520 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 3.5.1
- Tokenizers 0.21.1
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