---
base_model:
- werty1248/Mistral-Nemo-NT-Ko-12B-sft
datasets:
- zake7749/kyara-chinese-preference-rl-dpo-s0-30K
- sionic/ko-dpo-mix-7k-trl-style
- kuotient/orca-math-korean-dpo-pairs
- HuggingFaceH4/ultrafeedback_binarized
language:
- en
- ko
- ja
- zh
license: apache-2.0
---
# Mistral-Nemo-NT-Ko-12B-dpo
## Description
**Mistral-Nemo-NT-Ko-12B-dpo** is a shallowly DPO-trained version of [*werty1248/Mistral-Nemo-NT-Ko-12B-sft*](https://huggingface.co/werty1248/Mistral-Nemo-NT-Ko-12B-sft).
According to the [Hermes 3 Tech Report](https://nousresearch.com/wp-content/uploads/2024/08/Hermes-3-Technical-Report.pdf), DPO made negligible performance improvements in their model. Therefore, I followed the same approach described in the report and applied DPO using LoRA.
- LoRA r = 32
- Lora alpha = 16
- lr = 3e-6
- neftune alpha = 5
The datasets used are as follows:
- (En) [HuggingFaceH4/ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized)
- (Ko, translated from En) [sionic/ko-dpo-mix-7k-translation-exclude](https://huggingface.co/datasets/sionic/ko-dpo-mix-7k-translation-exclude)
- (Ko, translated from En) [kuotient/orca-math-korean-dpo-pairs](https://huggingface.co/datasets/kuotient/orca-math-korean-dpo-pairs)
- (Zh) [zake7749/kyara-chinese-preference-rl-dpo-s0-30K](https://huggingface.co/datasets/zake7749/kyara-chinese-preference-rl-dpo-s0-30K)
I've been looking for native Korean/Japanese DPO datasets, but haven't found anything that I'm personally satisfied with(Quantity/Quality).
From each dataset, I sampled a subset based on the score given by the reward model. In the end, I used about 13K samples for training for each language.
## Features
- The base model supports a context length of 128K, while I fine-tuned this model with an 8K context size.
- This model works well for **multi-turn conversations**, and tends to strongly reflect the previous conversation.
# Evaluation
### LogicKor
*Cot-1-shot*
| 모델 | 방법 | 추론 | 수학 | 글쓰기 | 코딩 | 이해 | 문법 | 싱글턴 | 멀티턴 | 총점 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|Mistral-Nemo-NT-Ko-12B-sft| cot-1-shot |7.36 | 6.57 | 8.71 | 8.57 | 9.57 | 6.43 | 7.81 | 7.93 | **7.87** |
|**Mistral-Nemo-NT-Ko-12B-dpo**| cot-1-shot | 6.79 | 6.43 | 9.43 | 9.79 | 9.43 | 5.29 | 7.71 | 8.00 | **7.86** |
| Mistral Nemo | cot-1-shot | 5.43 | 6.86 | 6.07 | 7.57 | 5.86 | 7.57 | 7.50 | 5.62 |6.56|
*1-shot*
| 모델 | 방법 | 추론 | 수학 | 글쓰기 | 코딩 | 이해 | 문법 | 싱글턴 | 멀티턴 | 총점 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|**Mistral-Nemo-NT-Ko-12B-dpo**| 1-shot | 8.14 | 5.50 | 9.36 | 8.57 | 9.50 | 4.71 | 7.38 | 7.88 | **7.63** |
|Mistral-Nemo-NT-Ko-12B-sft| 1-shot | 9.00 | 5.71 | 7.93 | 8.29 | 7.93 | 5.21 | 7.29 | 7.40 | 7.35 |
| Mistral Nemo | 1-shot | 5.00 | 6.50 | 6.86 | 8.07 | 7.64 | 8.43 | 7.60 | 6.57 |7.08|
*Default*
| 모델 | 방법 | 추론 | 수학 | 글쓰기 | 코딩 | 이해 | 문법 | 싱글턴 | 멀티턴 | 총점 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|**Mistral-Nemo-NT-Ko-12B-dpo**| default | 6.21 | 5.79 | 8.00 | 8.36 | 9.43 | 5.43 | 7.17 | 7.24 | **7.20** |
|Mistral-Nemo-NT-Ko-12B-sft| default | 6.00 | 4.93 | 5.43 | 7.14 | 9.71 | 4.00 | 6.45 | 5.95 | 6.20 |
| Mistral Nemo | default | 0.43 | 7.64 | 6.21 | 7.14 | 6.79 | 7.21 | 6.26 | 5.55 |5.90|
### Language-Confusion
| Model | Language | Monolingual-LPR | Monolingual-WPR | Crosslingual-LPR | Crosslingual-WPR |
| --- | --- | --- | --- | --- | --- |
|Mistral-Nemo-NT-Ko-12B-dpo| ko | 100.00% | 97.96% | **85.63%** | 96.93% |
|Mistral-Nemo-NT-Ko-12B-sft| ko | 100.00% | 99.00% | **87.51%** | 96.96% |
|Mistral-Nemo-Instruct-2407 | ko | 90.72% | 93.18% | 46.75% | 92.84% |
|Meta-Llama-3.1-8B-Instruct | ko | 99.00% | 96.97% | 91.45% | 93.01% |
|gemma-2-9b-it | ko | 100.00% | 98.00% | 87.93% | 95.58% |
| --- | --- | --- | --- | --- | --- |
|Mistral-Nemo-NT-Ko-12B-dpo| zh | 99.00% | 99.50% | **80.52%** | 97.51% |
|Mistral-Nemo-Instruct-2407 | zh | 97.50% | 98.98% | 53.43% | 93.58% |
| --- | --- | --- | --- | --- | --- |
|Mistral-Nemo-NT-Ko-12B-dpo| ja | 100.00% | 100.00% | **86.89%** | 95.41% |
|Mistral-Nemo-Instruct-2407 | ja | 94.00% | 98.94% | 50.27% | 96.05% |
## Template
```
<|im_start|>system
You are a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
*I trained Mistral-Nemo-NT-Ko-12B with various system prompt from dozens of dataset. You can chat with/without your system prompt.*
# Dataset
- zake7749/kyara-chinese-preference-rl-dpo-s0-30K
- sionic/ko-dpo-mix-7k-trl-style
- kuotient/orca-math-korean-dpo-pairs
- HuggingFaceH4/ultrafeedback_binarized
# Training Details
- GPU: 2xA100
- epoch: 1
- total batch size: 32
- learning rate: 3e-6
- neftune_noise_alpha: 5
See axolotl config
axolotl version: `0.4.1`
```yaml
base_model: werty1248/Mistral-Nemo-NT-Ko-12B-sft
model_type: MistralForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
dpo_beta: 0.1
rl: dpo
datasets:
- path: werty1248/NT-dpo
split: train
type: chatml.prompt_pairs
dataset_prepared_path: /workspace/data/prepared_datasets
output_dir: /workspace/data
save_steps: 500
sequence_len: 8192
sample_packing: false
pad_to_sequence_len: true
gradient_accumulation_steps: 16
micro_batch_size: 1
num_epochs: 1
optimizer: rmsprop
weight_decay: 0.0
learning_rate: 0.000003
lr_scheduler: linear
neftune_noise_alpha: 5
train_on_inputs: false
group_by_length: false
#wandb_project:
#wandb_entity:
#wandb_watch:
#wandb_name:
#wandb_log_model:
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
flash_attention: true
warmup_steps: 9
eval_steps:
val_set_size: 0
early_stopping_patience:
logging_steps: 1
special_tokens:
pad_token:
```
- reward margin
