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---
license: apache-2.0
datasets:
- jondurbin/airoboros-3.2
language:
- en
library_name: transformers
base_model: h2oai/h2o-danube2-1.8b-base
---
# h2o-danube2 with ChatML template
This is a [BAdam fine-tuned](https://arxiv.org/abs/2404.02827 "BAdam: A Memory Efficient Full Parameter Optimization Method for Large Language Models") and [LoRA+](https://arxiv.org/abs/2402.12354 "LoRA+: Efficient Low Rank Adaptation of Large Models") danube2 base model. It uses the ChatML template and was trained on the [Airoboros-3.2](https://huggingface.co/datasets/jondurbin/airoboros-3.2) (CC BY 4.0) dataset from [jondurbin](https://huggingface.co/jondurbin).
## Quants
Thank you [mradermacher](https://huggingface.co/mradermacher)!
- [mradermacher/danube2-1.8b-airoboros-3.2-GGUF](https://huggingface.co/mradermacher/danube2-1.8b-airoboros-3.2-GGUF)
## Template
```jinja
<|im_start|>user
{{instruction}}<|im_end|>
<|im_start|>assistant
{{response}}<|im_end|>
```
## BAdam
**System:** You are a helpful assistant.
```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: 13
badam_mask_mode: scatter
seed: 314
### dataset
dataset: airoboros32
template: hermes_chatml
cutoff_len: 8192
overwrite_cache: false
preprocessing_num_workers: 12
### output
output_dir: airoboros32-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: 8
learning_rate: 0.00001
num_train_epochs: 2
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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.9124 | 0.2753 | 1000 | 0.9466 |
| 0.8072 | 0.5506 | 2000 | 0.9149 |
| 0.9017 | 0.8258 | 3000 | 0.8982 |
| 0.8883 | 1.1011 | 4000 | 0.8844 |
| 0.8405 | 1.3764 | 5000 | 0.8786 |
| 0.864 | 1.6517 | 6000 | 0.8754 |
| 0.7758 | 1.9270 | 7000 | 0.8752 |
## QLoRA+
**System:** None
```yaml
### model
model_name_or_path: airoboros32-chatml-badam
### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: all
loraplus_lr_ratio: 16.0
lora_rank: 8
lora_alpha: 16
use_unsloth: true
quantization_bit: 4
upcast_layernorm: true
seed: 314
### dataset
dataset: airoboros32
template: hermes_chatml
cutoff_len: 8192
overwrite_cache: false
preprocessing_num_workers: 12
### output
output_dir: airoboros32-chatml-badam/loraplus
logging_steps: 1
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: 0.0001
num_train_epochs: 2.0
lr_scheduler_type: cosine
warmup_ratio: 0.01
bf16: true
flash_attn: fa2
### eval
val_size: 0.02
per_device_eval_batch_size: 1
eval_strategy: steps
eval_steps: 1000
```
### QLoRA+ Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9691 | 0.2781 | 1000 | 0.8704 |
| 0.7387 | 0.5562 | 2000 | 0.8443 |
| 0.6769 | 0.8343 | 3000 | 0.8250 |
| 0.5156 | 1.1123 | 4000 | 0.8134 |
| 0.4142 | 1.3904 | 5000 | 0.8029 |
| 0.6328 | 1.6685 | 6000 | 0.7953 |
| 0.872 | 1.9466 | 7000 | 0.7927 |
|