---
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          |