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--- |
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license: apache-2.0 |
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base_model: h2oai/h2o-danube2-1.8b-base |
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datasets: |
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- cgato/SlimOrcaDedupCleaned |
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language: |
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- en |
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library_name: transformers |
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tags: |
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- llama-factory |
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- unsloth |
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--- |
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# h2o-danube2 with ChatML template |
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This model was first fine-tuned with [BAdam](https://arxiv.org/abs/2404.02827 "BAdam: A Memory Efficient Full Parameter Optimization Method for Large Language Models") on [cgato/SlimOrcaDedupCleaned](https://huggingface.co./datasets/cgato/SlimOrcaDedupCleaned) using LLama-Factory. |
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## Template |
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```jinja |
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<|im_start|>system |
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{{system}}<|im_end|> |
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<|im_start|>user |
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{{instruction}}<|im_end|> |
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<|im_start|>assistant |
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{{response}}<|im_end|> |
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``` |
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## BAdam config |
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```yaml |
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### model |
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model_name_or_path: danube2-base-chatml |
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### method |
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stage: sft |
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do_train: true |
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finetuning_type: full |
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use_badam: true |
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badam_switch_mode: ascending |
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badam_switch_interval: 50 |
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badam_verbose: 1 |
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badam_start_block: 13 |
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seed: 314 |
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### dataset |
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dataset: slimorca_dedup_cleaned |
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template: hermes_chatml |
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cutoff_len: 8192 |
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overwrite_cache: false |
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preprocessing_num_workers: 12 |
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### output |
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output_dir: slim-chatml-badam |
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logging_steps: 5 |
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save_steps: 1 |
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save_strategy: epoch |
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plot_loss: true |
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overwrite_output_dir: false |
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### train |
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per_device_train_batch_size: 2 |
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gradient_accumulation_steps: 4 |
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learning_rate: 0.000005 |
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num_train_epochs: 1 |
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lr_scheduler_type: cosine |
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warmup_ratio: 0.01 |
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bf16: true |
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flash_attn: fa2 |
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### eval |
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val_size: 0.01 |
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per_device_eval_batch_size: 1 |
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eval_strategy: steps |
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eval_steps: 2000 |
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``` |
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### BAdam training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:-----:|:---------------:| |
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| 0.8535 | 0.0889 | 2000 | 0.8340 | |
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| 0.8735 | 0.1778 | 4000 | 0.8128 | |
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| 0.8054 | 0.2668 | 6000 | 0.8008 | |
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| 0.7907 | 0.3557 | 8000 | 0.8002 | |
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| 0.8749 | 0.4446 | 10000 | 0.7972 | |
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| 0.7463 | 0.5335 | 12000 | 0.7899 | |
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| 0.7762 | 0.6225 | 14000 | 0.7870 | |
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| 0.8231 | 0.7114 | 16000 | 0.7854 | |
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| 0.8686 | 0.8003 | 18000 | 0.7801 | |
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| 0.9159 | 0.8892 | 20000 | 0.7877 | |
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| 0.8281 | 0.9782 | 22000 | 0.7786 | |
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