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danube2-1.8b-Code-290k

Text Generation
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danube2-1.8b-Code-290k
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---
license: apache-2.0
base_model: h2oai/h2o-danube2-1.8b-base
datasets:
- ajibawa-2023/Code-290k-ShareGPT
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 [ajibawa-2023/Code-290k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Code-290k-ShareGPT) using LLama-Factory.

## Template

```jinja
<|im_start|>system
You are a helpful coding assistant.<|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: 8
seed: 8

### dataset
dataset: code_290k
template: hermes_chatml
cutoff_len: 8192
overwrite_cache: false
preprocessing_num_workers: 12

### output
output_dir: code-290k-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: 1000
```

### BAdam training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 0.7404        | 0.0559 | 1000  | 0.7784          |
| 0.7858        | 0.1118 | 2000  | 0.7702          |
| 0.7274        | 0.1677 | 3000  | 0.7604          |
| 0.6956        | 0.2236 | 4000  | 0.7570          |
| 0.7711        | 0.2795 | 5000  | 0.7541          |
| 0.7643        | 0.3354 | 6000  | 0.7518          |
| 0.8255        | 0.3913 | 7000  | 0.7496          |
| 0.7456        | 0.4472 | 8000  | 0.7483          |
| 0.7718        | 0.5031 | 9000  | 0.7447          |
| 0.6693        | 0.5590 | 10000 | 0.7445          |
| 0.7409        | 0.6149 | 11000 | 0.7433          |
| 0.7319        | 0.6709 | 12000 | 0.7424          |
| 0.7636        | 0.7268 | 13000 | 0.7415          |
| 0.7504        | 0.7827 | 14000 | 0.7414          |
| 0.7735        | 0.8386 | 15000 | 0.7374          |
| 0.7438        | 0.8945 | 16000 | 0.7375          |
| 0.839         | 0.9504 | 17000 | 0.7373          |