README.md

---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# 어려웠던 점: 학습을 시키기위해 System과 리뷰문장, 분류에 예시들을 적어놓았지만 학습이 제대로 되지않았다..
# instruction_prompt_template = """
# ###System;너는 사용자의 리뷰를 긍정,부정 중 하나로만 판단해야 한다.
# ### 리뷰 문장: 진짜 재밌다 ### 분류 결과: 긍정
# ###System;너는 사용자의 리뷰를 긍정,부정 중 하나로만 판단해야 한다.
# ### 리뷰 문장: 나 잘 뻔 했잖아 영화보고 지루해서 ### 분류 결과: 부정
# ###System;너는 사용자의 리뷰를 긍정,부정 중 하나로만 판단해야 한다.
# ### 리뷰 문장: 어떻게 이렇게까지 재미없을 수가 있지 ### 분류 결과: 부정
# ###System;너는 사용자의 리뷰를 긍정,부정 중 하나로만 판단해야 한다.
# ### 리뷰 문장: 열린결말 영화 좋아하는데 이 영화가 열린결말이야 ### 분류 결과: 긍정

# 위에처럼 학습을 시켰고, 실험 문장들은 아래와 같다.

# evaluation_queries = [
#     "이게 재밌다는 사람들이 이해가 안가"
#     "너무 흥미로워요 시즌2도 나왔으면 좋겠어요"
#     "이게 무슨 영화야 지루하기짝이없네"
#     "배울점이 많은 영화네요"
# ]

# 또한 실험 결과는 아래와 같다.

# - 분석 결과 0: 음식명:닭볶음탕,옵션:중,수량:한그릇
# - 분석 결과 1: 음식명:냉치찜,옵션:대,수량:한판

# 어디를 고치고 어디를 학습시켜야할지 잘 모르겠다...



# Model Card for Model ID

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## Model Details

### Model Description

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## Uses

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## How to Get Started with the Model

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## Training Details

### Training Data

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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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## 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 0.7.0