README.md

---
library_name: transformers
license: apache-2.0
---

# Model Card for Model ID

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## Model Details
This Model fine-tuned by Security dataset.
I will fine-tune continuous...

### 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.

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## Uses
```python
import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel

model_id = 'model_result'

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    #torch_dtype=torch.bfloat16,
    quantization_config=bnb_config, # 4-bit quantization (4비트 양자화)
    device_map="auto",
)

model.eval()

from transformers import TextStreamer

def inference(input: str):
    streamer = TextStreamer(tokenizer=tokenizer, skip_prompt=True, skip_special_tokens=True)
    
    messages = [
        {"role": "system", "content": "You are an information security AI assistant. Information security questions must be answered accurately."},
        {"role": "user", "content": f"Please provide concise, non-repetitive answers to the following questions:\n {input}"}
        # {"role": "user", "content": f"{input}"}
        ]
    
    input_ids = tokenizer.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True,
        return_tensors="pt",
    ).to(model.device)
    
    outputs = model.generate(
        input_ids,
        streamer=streamer,
        max_new_tokens=8192,
        num_beams=1,
        do_sample=True,
        temperature=0.1,
        top_p=0.95,
        top_k=10
    )

inference("해킹 당하지 않으려면 어떻게 해야하는지 알려줘.")

해킹 당하지 않으려면 다음과 같은 것들을 고려해 보세요:

1. **패스워드 관리**: 강력한 패스워드를 사용하고, 패스워드의 복잡성과 변환 주기를 잘 유지하세요.
2. **시스템 업데이트**: 최신 소프트웨어와 보안 패치를 설치하고, 지속적으로 시스템을 업데이트하세요.
3. **스캔 및 검사**: 시스템과 네트워크를 자주 스캔하고, 보안 취약점을 검사해 보세요.
4. **안전한 브라우징**: 안전한 브라우저와 확장 기능을 사용하고, 악성 소프트웨어 설치를 방지하세요.
5. **데이터 백업**: 중요한 데이터를 백업하고, 이를 안전한 저장소에 보관하세요.
6. **네트워크 보안**: 네트워크 보안 장비를 사용하고, 침입자에 대한 통제와 감시를 유지하세요.
7. **사용자 교육**: 사용자들이 안전한 사용 방법을 이해하고, 정보 보안에 대한 중요성을 인지하세요.
8. **계약자 관리**: 계약자와 파트너와의 계약을 잘 확인하고, 정보 보안에 대한 합의를 유지하세요.

```

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### 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

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#### Preprocessing [optional]

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#### Training Hyperparameters

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#### 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

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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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).

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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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## Citation [optional]

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## Glossary [optional]

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