---
language:
- ko
- en
pipeline_tag: text-generation
datasets:
- DILAB-HYU/KoQuality
---
### Model Card for Model ID
base_model : [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co./meta-llama/Meta-Llama-3.1-8B-Instruct)

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

tokenizer = AutoTokenizer.from_pretrained("MDDDDR/Meta-Llama-3.1-8B-it-v0.1")
model = AutoModelForCausalLM.from_pretrained(
    "MDDDDR/Meta-Llama-3.1-8B-it-v0.1",
    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 : [DILAB-HYU/KoQuality](https://huggingface.co./datasets/DILAB-HYU/KoQuality)

### 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     |↑  |0.5150|±  |0.0133|
|                |       |none  |     0|f1      |↑  |0.3634|±  |   N/A|
|kobest_copa     |      1|none  |     0|acc     |↑  |0.6280|±  |0.0153|
|                |       |none  |     0|f1      |↑  |0.6279|±  |   N/A|
|kobest_hellaswag|      1|none  |     0|acc     |↑  |0.4280|±  |0.0221|
|                |       |none  |     0|acc_norm|↑  |0.5540|±  |0.0223|
|                |       |none  |     0|f1      |↑  |0.4250|±  |   N/A|
|kobest_sentineg |      1|none  |     0|acc     |↑  |0.7406|±  |0.0220|
|                |       |none  |     0|f1      |↑  |0.7317|±  |   N/A|

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