README.md

---
library_name: peft
base_model: KT-AI/midm-bitext-S-7B-inst-v1
datasets:
- nsmc
---

# Model Card for Model ID

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



### Model Description

<!-- 
NSMC 데이터에 대해 KT-AI/midm-bitext-S-7B-inst-v1 미세튜닝

목표

- 영화 리뷰 텍스트를 프롬프트에 포함하여 모델에 입력하면 '긍정' 또는 '부정'이라고 예측 텍스트를 직접 생성


요건

- NSMC의 train 스플릿 앞쪽 2,000개 이상의 샘플을 학습에 사용테스트는 test 스플릿 앞쪽 1,000개의 샘플만 측정할 것

-->

## Training Details

<!-- 
Training data: nsmc 'train' data 중 상위 2000개의 샘플
Evaluation data: nsmc 'test' data 중 상위 1000개의 샘플
-->


- **Developed by:** [More Information Needed]
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### Model Sources [optional]

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## Uses

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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 Procedure 

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#### Preprocessing [optional]

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

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## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### 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).

- **Hardware Type:** [More Information Needed]
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## 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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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: bfloat16

### Framework versions


- PEFT 0.6.2