README.md

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

# Model Card for Model ID

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



## Model Details
### 네이버 영화 리뷰텍스트(NSMC)데이터셋을 프롬포트에 포함하여 모델에 입력하면
-- "긍정" 또는 "부정" 이라고 예측하는 텍스트 생성하는 것이 목표
#### 실험내용: train dataset의 2100개 샘플,valid dataset의 1000개 샘플을 미세튜닝에 사용
- 일반적으로 1900스텝에서는 정확도 accuracy가 80후반대(약 85%)가 도출, 2000스텝이상부터 90%에 근접한 수치를 보였다.
- seq length를 312로 줄인 결과, seq length 384보다 훈련시간trainer.train이 적게 걸리지만 정확도도 감소
- gradient_accumulation steps을 2로 설정하여 미니배치를 통해 구해진 gradient값을 n step동안
global gradient에 누적시킨 후 한번에 업뎃->배치를 여러개 사용한 효과를 주는 등 노력함.
##Accuracy 정확도 분석
###valid_dataset(test dataset 1000개에 대한 정확도)
*********************************
|               | TP    | TN   |
|:-------------:|:-----:|:----:|
| PP            |  438  | 70  | 
| PN            | 29   | 463  |
|Accuracy    | -       |0.901
*********************************
***정확도:0.901
*********************************

### Model Description

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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 Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### 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).

- **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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## 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 0.7.1
- PEFT 0.7.0