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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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- ajibawa-2023/Code-290k-ShareGPT |
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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 [ajibawa-2023/Code-290k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Code-290k-ShareGPT) using LLama-Factory. |
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## Template |
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```jinja |
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<|im_start|>system |
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You are a helpful coding assistant.<|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: 8 |
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seed: 8 |
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### dataset |
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dataset: code_290k |
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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: code-290k-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: 8 |
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learning_rate: 0.00001 |
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num_train_epochs: 1 |
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lr_scheduler_type: constant_with_warmup |
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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: 1000 |
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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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| 0.7404 | 0.0559 | 1000 | 0.7784 | |
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| 0.7858 | 0.1118 | 2000 | 0.7702 | |
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| 0.7274 | 0.1677 | 3000 | 0.7604 | |
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| 0.6956 | 0.2236 | 4000 | 0.7570 | |
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| 0.7711 | 0.2795 | 5000 | 0.7541 | |
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| 0.7643 | 0.3354 | 6000 | 0.7518 | |
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| 0.8255 | 0.3913 | 7000 | 0.7496 | |
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| 0.7456 | 0.4472 | 8000 | 0.7483 | |
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| 0.7718 | 0.5031 | 9000 | 0.7447 | |
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| 0.6693 | 0.5590 | 10000 | 0.7445 | |
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| 0.7409 | 0.6149 | 11000 | 0.7433 | |
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| 0.7319 | 0.6709 | 12000 | 0.7424 | |
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| 0.7636 | 0.7268 | 13000 | 0.7415 | |
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| 0.7504 | 0.7827 | 14000 | 0.7414 | |
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| 0.7735 | 0.8386 | 15000 | 0.7374 | |
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| 0.7438 | 0.8945 | 16000 | 0.7375 | |
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| 0.839 | 0.9504 | 17000 | 0.7373 | |
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