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---
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. |