---
library_name: transformers
datasets:
- daekeun-ml/naver-news-summarization-ko
language:
- ko
base_model:
- google/gemma-2-9b-it
tags:
- DoRA
- summary
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** [More Information Needed]
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- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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### Model Sources [optional]

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## Uses

```python
from peft import PeftModel
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer

MODEL_ID = "google/gemma-2-9b-it"
PEFT_MODEL_ID = "drlee1/gemma2-9b-it-qdora-summary"

model = AutoModelForCausalLM.from_pretrained(MODEL_ID, device_map = 'auto', torch_dtype = torch.float16)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

model = PeftModel.from_pretrained(model, PEFT_MODEL_ID, device_map = 'auto', torch_dtype = torch.float16)

pipe = pipeline("text-generation", model = model, tokenizer = tokenizer, max_new_tokens = 512)

doc = "..."

messages = [
    {"role": "user", "content": "다음 글을 요약해주세요:\n\n{}".format(doc)}
]

prompt = tokenizer.apply_chat_template(messages, tokenize = False, add_generation_prompt = True)

outputs = pipe(
    prompt,
    do_sample = True,
    temperature = .2,
    top_k = 50,
    top_p = .95,
    add_special_tokens = True 
)

print(outputs[0]['generated_text'][len(prompt):])
```

### Template

```text
# chat template
<bos><start_of_turn>user\n다음 글을 요약해주세요:\n\n{data}<end_of_turn>\n<start_of_turn>model\n{label}
```


### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

- SFT
- Quantization
- DoRA

#### Preprocessing [optional]

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#### Training Hyperparameters

- per_device_train_batch_size: 2
- gradient_accumulation_steps: 4
- optimization: paged_adamw_8bit
- lr: 2e-4
- bf16: True
- max_steps: 500

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

- Training Loss

| Step | Training Loss |
| --- | --- |
|100 |1.528100 |
|200 |1.409400 |
|300 |1.372800 |
|400 |1.325900 |
|500 |1.341600 |

### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**APA:**

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## Glossary [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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