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danube2-1.8b-openhermes

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danube2-1.8b-openhermes
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---
license: apache-2.0
datasets:
- Crystalcareai/openhermes_200k_unfiltered
language:
- en
library_name: transformers
base_model: h2oai/h2o-danube2-1.8b-base
tags:
- llama-factory
- unsloth
---
# h2o-danube2 with ChatML template

This is a [BAdam](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") fine-tuned danube2 base model. It uses the ChatML template and was trained on the [openhermes-unfiltered](https://huggingface.co/datasets/Crystalcareai/openhermes_200k_unfiltered).

## Quants

Thank you [mradermacher](https://huggingface.co/mradermacher)!

- [mradermacher/danube2-1.8b-openhermes-GGUF](https://huggingface.co/mradermacher/danube2-1.8b-openhermes-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: 10
seed: 720

### dataset
dataset: openhermes_unfiltered
template: ninja_chatml
cutoff_len: 8192
overwrite_cache: false
preprocessing_num_workers: 12

### output
output_dir: openhermes-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: 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: 2000
```

### BAdam Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 0.7971        | 0.1748 | 2000  | 0.7418          |
| 0.6815        | 0.3496 | 4000  | 0.7178          |
| 0.6593        | 0.5245 | 6000  | 0.7055          |
| 0.6923        | 0.6993 | 8000  | 0.6960          |
| 0.6942        | 0.8741 | 10000 | 0.6877          |


## QLoRA+
```yaml
### model
model_name_or_path: openhermes-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: 3141

### dataset
dataset: openhermes_unfiltered
template: hermes_chatml
cutoff_len: 8192
overwrite_cache: false
preprocessing_num_workers: 12

### output
output_dir: openhermes-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: 1.0
lr_scheduler_type: cosine
warmup_ratio: 0.01
bf16: true
flash_attn: fa2
#neftune_noise_alpha: 5

### 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 |
|:-------------:|:------:|:-----:|:---------------:|
| 0.6523        | 0.0883 | 1000  | 0.7126          |
| 0.6398        | 0.1766 | 2000  | 0.7086          |
| 0.6865        | 0.2649 | 3000  | 0.7001          |
| 0.6714        | 0.3532 | 4000  | 0.6917          |
| 0.7213        | 0.4415 | 5000  | 0.6819          |
| 0.7764        | 0.5298 | 6000  | 0.6721          |
| 0.6931        | 0.6181 | 7000  | 0.6638          |
| 0.6632        | 0.7064 | 8000  | 0.6560          |
| 0.5966        | 0.7947 | 9000  | 0.6514          |
| 0.6339        | 0.8830 | 10000 | 0.6482          |
| 0.4987        | 0.9713 | 11000 | 0.6472          |