| | --- |
| | license: apache-2.0 |
| | base_model: h2oai/h2o-danube2-1.8b-base |
| | datasets: |
| | - TIGER-Lab/MathInstruct |
| | language: |
| | - en |
| | library_name: transformers |
| | tags: |
| | - llama-factory |
| | - unsloth |
| | --- |
| | # h2o-danube2 with ChatML template |
| |
|
| | 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. |
| |
|
| | ## Quants |
| |
|
| | Mad props, [mradermacher](https://huggingface.co/mradermacher)! |
| |
|
| | - [mradermacher/danube2-1.8b-MathInstruct-GGUF](https://huggingface.co/mradermacher/danube2-1.8b-MathInstruct-GGUF) |
| |
|
| | ## Template |
| |
|
| | ```jinja |
| | <|im_start|>system |
| | You are a helpful assistant specialised in mathematics.<|im_end|> |
| | <|im_start|>user |
| | {{instruction}}<|im_end|> |
| | <|im_start|>assistant |
| | {{response}}<|im_end|> |
| | ``` |
| |
|
| | ## BAdam config |
| |
|
| | ```yaml |
| | ### 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: 7 |
| | seed: 5772 |
| | |
| | ### dataset |
| | dataset: mathinstruct |
| | template: ninja_chatml |
| | cutoff_len: 8192 |
| | overwrite_cache: false |
| | preprocessing_num_workers: 12 |
| | |
| | ### output |
| | output_dir: mathinstruct-chatml-badam |
| | logging_steps: 5 |
| | save_steps: 1 |
| | save_strategy: epoch |
| | plot_loss: true |
| | overwrite_output_dir: false |
| | |
| | ### train |
| | per_device_train_batch_size: 4 |
| | gradient_accumulation_steps: 4 |
| | learning_rate: 0.000005 |
| | num_train_epochs: 1 |
| | 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 | |
| | |:-------------:|:------:|:-----:|:---------------:| |
| | | 0.2748 | 0.0617 | 1000 | 0.2788 | |
| | | 0.2786 | 0.1234 | 2000 | 0.2503 | |
| | | 0.18 | 0.1850 | 3000 | 0.2144 | |
| | | 0.2015 | 0.2467 | 4000 | 0.1926 | |
| | | 0.2044 | 0.3084 | 5000 | 0.1777 | |
| | | 0.142 | 0.3701 | 6000 | 0.1661 | |
| | | 0.1813 | 0.4317 | 7000 | 0.1570 | |
| | | 0.1413 | 0.4934 | 8000 | 0.1529 | |
| | | 0.1805 | 0.5551 | 9000 | 0.1462 | |
| | | 0.1431 | 0.6168 | 10000 | 0.1410 | |
| | | 0.1693 | 0.6784 | 11000 | 0.1375 | |
| | | 0.1291 | 0.7401 | 12000 | 0.1357 | |
| | | 0.1501 | 0.8018 | 13000 | 0.1348 | |
| | | 0.1521 | 0.8635 | 14000 | 0.1345 | |
| | | 0.1279 | 0.9251 | 15000 | 0.1346 | |
| | | 0.1351 | 0.9868 | 16000 | 0.1344 | |
| |
|
| | ### GSM8K results |
| |
|
| | |Tasks|Version| Filter |n-shot| Metric |Value | |Stderr| |
| | |-----|------:|----------------|-----:|-----------|-----:|---|-----:| |
| | |gsm8k| 3|strict-match | 5|exact_match|0.2691|± |0.0122| |
| | | | |flexible-extract| 5|exact_match|0.2752|± |0.0123| |
| |
|
| | It matches the chat trained model from h2o. |
