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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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- TIGER-Lab/MathInstruct |
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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 [TIGER-Lab/MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct) using LLama-Factory. |
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## Quants |
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Mad props, [mradermacher](https://huggingface.co/mradermacher)! |
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- [mradermacher/danube2-1.8b-MathInstruct-GGUF](https://huggingface.co/mradermacher/danube2-1.8b-MathInstruct-GGUF) |
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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 assistant specialised in mathematics.<|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: 7 |
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seed: 5772 |
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### dataset |
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dataset: mathinstruct |
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template: ninja_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: mathinstruct-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: 4 |
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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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pure_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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| 0.2748 | 0.0617 | 1000 | 0.2788 | |
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| 0.2786 | 0.1234 | 2000 | 0.2503 | |
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| 0.18 | 0.1850 | 3000 | 0.2144 | |
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| 0.2015 | 0.2467 | 4000 | 0.1926 | |
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| 0.2044 | 0.3084 | 5000 | 0.1777 | |
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| 0.142 | 0.3701 | 6000 | 0.1661 | |
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| 0.1813 | 0.4317 | 7000 | 0.1570 | |
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| 0.1413 | 0.4934 | 8000 | 0.1529 | |
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| 0.1805 | 0.5551 | 9000 | 0.1462 | |
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| 0.1431 | 0.6168 | 10000 | 0.1410 | |
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| 0.1693 | 0.6784 | 11000 | 0.1375 | |
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| 0.1291 | 0.7401 | 12000 | 0.1357 | |
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| 0.1501 | 0.8018 | 13000 | 0.1348 | |
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| 0.1521 | 0.8635 | 14000 | 0.1345 | |
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| 0.1279 | 0.9251 | 15000 | 0.1346 | |
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| 0.1351 | 0.9868 | 16000 | 0.1344 | |
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### GSM8K results |
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|Tasks|Version| Filter |n-shot| Metric |Value | |Stderr| |
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|-----|------:|----------------|-----:|-----------|-----:|---|-----:| |
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|gsm8k| 3|strict-match | 5|exact_match|0.2691|± |0.0122| |
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| | |flexible-extract| 5|exact_match|0.2752|± |0.0123| |
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It matches the chat trained model from h2o. |
