---
language:
- ko
- en
pipeline_tag: text-generation
datasets:
- nlpai-lab/kullm-v2
- MarkrAI/KoCommercial-Dataset
---
### Model Card for Model ID
base_model : [google/gemma-7b](https://huggingface.co/google/gemma-7b)

### Basic usage
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("MDDDDR/gemma-7b-it-v0.2")
model = AutoModelForCausalLM.from_pretrained(
    "MDDDDR/gemma-7b-it-v0.2",
    device_map="auto",
    torch_dtype=torch.bfloat16
)

input_text = "사과가 뭐야?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```

### Training dataset
- dataset_1 : [MarkrAI/KoCommercial-Dataset](https://huggingface.co/datasets/MarkrAI/KoCommercial-Dataset)
- dataset_2 : [nlpai-lab/kullm-v2](https://huggingface.co/datasets/nlpai-lab/kullm-v2)

### lora_config and bnb_config in Training
```python
bnd_config = BitsAndBytesConfig(
  load_in_4bit = True,
  bnb_4bit_use_double_quant = True,
  bnb_4bit_quant_type = 'nf4',
  bnb_4bit_compute_dtype = torch.bfloat16
)

lora_config = LoraConfig(
  r = 8,
  lora_alpha = 8,
  lora_dropout = 0.05,
  target_modules = ['gate_proj', 'up_proj', 'down_proj']
)
```

### Model Evaluation
|      Tasks      |Version|Filter|n-shot|  Metric   |   |Value |   |Stderr|
|-----------------|-------|------|-----:|-----------|---|-----:|---|------|
|kobest_boolq     |      1|none  |     0|acc        |↑  |0.5912|±  |0.0131|
|                 |       |none  |     0|f1         |↑  |0.5183|±  |N/A   |
|kobest_copa      |      1|none  |     0|acc        |↑  |0.6320|±  |0.0153|
|                 |       |none  |     0|f1         |↑  |0.6313|±  |N/A   |
|kobest_hellaswag |      1|none  |     0|acc        |↑  |0.4220|±  |0.0221|
|                 |       |none  |     0|acc_norm   |↑  |0.5280|±  |0.0223|
|                 |       |none  |     0|f1         |↑  |0.4190|±  |N/A   |
|kobest_sentineg  |      1|none  |     0|acc        |↑  |0.4962|±  |0.0251|
|                 |       |none  |     0|f1         |↑  |0.3747|±  |N/A   |

### Hardware
- RTX 3090 Ti 24GB x 1
- Training Time : 80 hours