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---
license: apache-2.0
base_model: h2oai/h2o-danube2-1.8b-base
datasets:
- migtissera/Tess-v1.5
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 [migtissera/Tess-v1.5](https://huggingface.co./datasets/migtissera/Tess-v1.5) using LLama-Factory.
## Quants
Thanks to [mradermacher](https://huggingface.co./mradermacher) for this!
- [mradermacher/danube2-1.8b-Tess-v1.5-GGUF](https://huggingface.co./mradermacher/danube2-1.8b-Tess-v1.5-GGUF)
## Template
```jinja
<|im_start|>system
{{system}}<|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: 6
seed: 720
### dataset
dataset: tess15
template: hermes_chatml
cutoff_len: 8192
overwrite_cache: false
preprocessing_num_workers: 12
### output
output_dir: tess15-chatml-badam
logging_steps: 5
save_steps: 1
save_strategy: epoch
plot_loss: true
overwrite_output_dir: false
### train
per_device_train_batch_size: 2
gradient_accumulation_steps: 4
learning_rate: 0.00001
num_train_epochs: 1
lr_scheduler_type: constant_with_warmup
warmup_ratio: 0.01
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 |
|:-------------:|:------:|:-----:|:---------------:|
| 0.8017 | 0.0643 | 1000 | 0.6820 |
| 0.6167 | 0.1287 | 2000 | 0.6610 |
| 0.6161 | 0.1930 | 3000 | 0.6496 |
| 0.6322 | 0.2574 | 4000 | 0.6423 |
| 0.5127 | 0.3217 | 5000 | 0.6366 |
| 0.61 | 0.3860 | 6000 | 0.6312 |
| 0.6758 | 0.4504 | 7000 | 0.6266 |
| 0.5901 | 0.5147 | 8000 | 0.6215 |
| 0.5163 | 0.5791 | 9000 | 0.6197 |
| 0.6043 | 0.6434 | 10000 | 0.6175 |
| 0.5056 | 0.7077 | 11000 | 0.6153 |
| 0.5772 | 0.7721 | 12000 | 0.6126 |
| 0.6692 | 0.8364 | 13000 | 0.6107 |
| 0.5262 | 0.9008 | 14000 | 0.6066 |
| 0.6386 | 0.9651 | 15000 | 0.6056 |
|