---
license: apache-2.0
datasets:
- cognitivecomputations/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split
language:
- en
library_name: transformers
base_model: h2oai/h2o-danube2-1.8b-base
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 [cognitivecomputations/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split](https://huggingface.co/datasets/cognitivecomputations/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split) using LLama-Factory.

## Quants

Thanks to [mradermacher](https://huggingface.co/mradermacher)!

- [mradermacher/danube2-1.8b-WizardLM-Evol-V2-Unfiltered-GGUF](https://huggingface.co/mradermacher/danube2-1.8b-WizardLM-Evol-V2-Unfiltered-GGUF)

## Template

```jinja
<|im_start|>system
You are a helpful assistant that gives long and detailed answers.<|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: wizardlm_evol_v2_196k_unfiltered
template: ninja_chatml
cutoff_len: 8192
overwrite_cache: false
preprocessing_num_workers: 12

### output
output_dir: wizardlm-evol-v2-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
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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.6195        | 0.1050 | 1000 | 0.7363          |
| 0.6788        | 0.2100 | 2000 | 0.7252          |
| 0.689         | 0.3150 | 3000 | 0.7172          |
| 0.6707        | 0.4200 | 4000 | 0.7133          |
| 0.6674        | 0.5250 | 5000 | 0.7091          |
| 0.7365        | 0.6301 | 6000 | 0.7085          |
| 0.7037        | 0.7351 | 7000 | 0.7066          |
| 0.709         | 0.8401 | 8000 | 0.7041          |
| 0.6652        | 0.9451 | 9000 | 0.7042          |