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---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---


# Dataset

[Here](https://huggingface.co/datasets/divakaivan/fake_movie_review_kr)

- review_with_prefix is a combination of 50 reviews gathered from Naver's movie review corpus dataset
- opinion is a result of passing the respective review_with_prefix to gpt-4o-mini along with the below prompt:

```
You are a helpful assistant that helps me evaluate Korean reviews. For each movie you are given 50 reviews. Analyze the reviews, and for the movie itself return a score(1 to 10) and explanation for each of the following criteria: Emotional, Characters, Plot, Visuals, Pacing. Return the review in Korean. + {review_with_prefix[i]}
```

# Usage

```python
from unsloth import FastLanguageModel

max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "divakaivan/llama3-finetuned-reviewer-kr",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)
FastLanguageModel.for_inference(model) # Enable native 2x faster inference

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
You are a helpful assistant that helps me evaluate Korean reviews. For each movie you are given 50 reviews. Analyze the reviews, and for the movie itself return a score(1 to 10) and explanation for each of the following criteria: Emotional, Characters, Plot, Visuals, Pacing. Return the review in Korean.

### Input:
{}

### Response:
{}"""

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # input - place your input here
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens = 1000, use_cache = True)
tokenizer.batch_decode(outputs)
```

Sample input:

```
๋ฆฌ๋ทฐ 1: ์ตœ๊ณ ์˜ 2d์˜ํ™”๊ฐ€ ์•„๋‹Œ๊ฐ€์‹ถ์œผ๋‹ค....!;  ๋ฆฌ๋ทฐ 2: ์นด๋ฆฌ์Šค๋งˆ์ž‘๋ ฌ~์—ญ๋Œ€ํผ๋‹ˆ์…”์‹œ๋ฆฌ์ฆˆ1.2.3ไธญ๊ฐ€์žฅ์ข‹์€์•ก์…˜์ˆ˜์ž‘!!;  ๋ฆฌ๋ทฐ 3: ์Œ..๋ณ„๋กœ๋‹ค..์ •๋ง๋กœ....;  ๋ฆฌ๋ทฐ 4: ํ•˜....์ •๋ง ๋ง์ด ์•ˆ๋‚˜์˜ค๋Š” ์˜ํ™”. ๋‚ด์šฉ, ์—ฐ์ถœ, ์นด๋ฉ”๋ผ, ์—ฐ๊ธฐ๊นŒ์ง€ ๋ญ ํ•˜๋‚˜ ์ œ๋Œ€๋กœ ๋ง˜์—๋“œ๋Š” ๋ถ€๋ถ„์ด ์—†๋‹ค. ์™„์ „ B๊ธ‰ ์˜ํ™” ๋Š๋‚Œ. '๋ณธ'์ด๋ผ๋Š” ํƒ€์ดํ‹€์„ ๋‹ฌ๊ณ  ์—”๋”ฉ์Œ์•…์„ ํ‹€์–ด์ฃผ๊ธฐ์— ๋„ˆ๋ฌด๋‚˜๋„ ๋ถ€์กฑํ•œ ์˜ํ™”. ๋ณธ์‹œ๋ฆฌ์ฆˆ ํŒฌ์œผ๋กœ์„œ ์ •๋ง ๋ง˜์— ์•ˆ๋“œ๋Š” ์˜ํ™”์˜€๋‹ค.;  ๋ฆฌ๋ทฐ 5: ์•ก์…˜๋ณด๋‹ค๋Š” ๋“œ๋ผ๋งˆ....๋ˆ„๋ฏธ๋ผํŒŒ์Šค, ์ฝœ๋ฆฐํŒจ๋Ÿด์˜ ๊ฐ์ •์—ฐ๊ธฐ๊ฐ€ ์ •๋ง ๊ตณ์ธ ์˜ํ™”;  ๋ฆฌ๋ทฐ 6: ์ž”์ž”ํ•œ ๊ฐ๋™์„ ๋ถˆ๋Ÿฌ ์ผ์œผํ‚ค๋Š” ์˜ํ™”... ๊ทธ๋ž˜ ์•„์ง ๋Šฆ์ง€ ์•Š์•˜๋‹ค~!!;  ๋ฆฌ๋ทฐ 7: ์•„ ์ •๋ง ์ด๋Ÿฐ ํž๋ง์˜ํ™”๋ฅผ ์ƒ์˜ํ•˜๋Š” ์˜ํ™”๊ด€์ด ํ•œ ๋‘๊ณณ๋ฐ–์— ์—†๋‹ค๋Š”๊ฒŒ ์ •๋ง ์•ˆํƒ€๊น๊ณ  ๋Œ€ํ•œ๋ฏผ๊ตญ์˜ ์˜ํ™”์‹œ์žฅ์ด ์–ผ๋งˆ๋‚˜ ํ˜‘์†Œํ•˜๊ณ  ๋‹จ์ˆœํ•˜๋ฉฐ ํ’‹๋‚ด๋‚˜๋Š”์ง€ ์•Œ๊ฑฐ๊ฐ™๋‹ค.;  ๋ฆฌ๋ทฐ 8: ๋„˜ ์žผ๋”ฐ;  ๋ฆฌ๋ทฐ 9: ์–ด๋”” ์ง€๋ฐฉ๋Œ€ ์—ฐ๊ทน์˜ํ™”ํ•™๋ถ€ ์กธ์—…์ž‘ํ’ˆ์ด๋ผ๊ณ  ํ•˜๋ฉด ์ˆ˜๊ธ๊ฐˆ๋งŒํ•œ ์ž‘ํ’ˆ ใ…‹ใ…‹;  ๋ฆฌ๋ทฐ 10: ํ„ฑ๋„ ์—†๋Š” ์‹ ๊ฒฉํ™”. ๋ฒ ๋ฅผ๋ฆฐ์˜ํ™”์ œ์„œ ์™„์ „ ๋ฌด์‹œ.;  ๋ฆฌ๋ทฐ 11: ํƒ์š•๊ณผ ๊ท ํ˜•์— ๋Œ€ํ•œ ๋งŽ์€ ์ƒ๊ฐ์ด ๋“ค๊ฒŒ ๋งŒ๋“œ๋Š” ์˜ํ™”.;  ๋ฆฌ๋ทฐ 12: ํž™ํ•ฉ์Œ์•…์„ ์ข‹์•„ํ•˜๊ณ  ๊ทธ๋“ค์„ ๊ฟˆ๊พธ๋Š” ์‚ฌ๋žŒ ์ž…์žฅ์œผ๋กœ์„œ์Šฌํ”„๊ธฐ๋„ํ•˜๊ณ  ์žฌ๋ฏธ๋„ ์žˆ๋‹ค ๊ณต๊ฐ๋Œ€๋„ ์žˆ๊ณ ๋งˆ์ง€๋ง‰์— ์ฐจ์—์„œ ๋žฉํ• ๋•Œ ๋„๋กœ๋ฅผ ๋ณด๋‹ˆUํ„ดํ•˜๋ผ๋Š”๊ฒŒ ๋ณด์ด๋Š”๋ฐ ์ฐธ ์”์“ธํ•˜๋‹ค;  ๋ฆฌ๋ทฐ 13: ์‚ฌ๋žŒ๋งˆ๋‹ค ๋Œ๋ฆฌ๋Š” ์˜ํ™”๊ฐ€ ์žˆ๋‚˜๋ณด๋‹ค.;  ๋ฆฌ๋ทฐ 14: ํ˜„์กดํ•˜๋Š” ์ตœ๊ณ ์˜ ๋กœ๋งจํ‹ฑ์ฝ”๋ฏธ๋”” ๋ผ ์ƒ๊ฐ ํ•ฉ๋‹ˆ๋‹คใ…‹ใ…‹;  ๋ฆฌ๋ทฐ 15: ๊ทธ๋ž˜ํ”ฝ๋˜ฅ์ด๋„ค์šฉ๊ทธ๋ž˜ํ”ฝ์ด์ฐธใ…‹ใ…‹ใ…‹ใ…‹ใ…‹์˜ํ™”๋ฐœ๋กœ๋งŒ๋“œ๋‚˜;  ๋ฆฌ๋ทฐ 16: ๊ตญ์‚ฐ์˜ํ™”๋ด…์‹œ๋‹ค ๋‹ค์„ธํฌ๋ด์š” ๊ตญ์‚ฐ๋ฌผ์‚ฐ์žฅ๋ ค์šด๋™;  ๋ฆฌ๋ทฐ 17: ์ •๋ง ์ด๋ ‡๊ฒŒ๋ฐ–์— ๋ชป๋งŒ๋“œ๋‚˜? ํŽธ์ง‘์ด๋ผ๋„ ์ข€ ์ž˜ํ•˜์ง€.;  ๋ฆฌ๋ทฐ 18: ๋‚ด ์ธ์ƒ์— ์ตœ์•…์˜ ์˜ํ™”๋ฅผ ๊ผฝ์œผ๋ผ๋ฉด ์ด ์˜ํ™”๊ฐ€ ๋‹จ์—ฐ ๋ฒ ์ŠคํŠธ5์œ„ ์•ˆ์— ๊ฑฐ๋œฌํžˆ ๋“œ๋‹ˆ๊น;  ๋ฆฌ๋ทฐ 19: 1๋ฒˆ์งธ ๋ณผ๋•Œ๋Š” ๋ญ ์ด๋Ÿฐ์กฐ์žกํ•œ ์˜ํ™”๊ฐ€. 2๋ฒˆ์งธ ๋ณต์Šด๋•Œ๋Š” ์—ญ์‹œ ํ…Œ์ธ ์•ผ ๊ฐ๋…์ด๊ตฌ๋‚˜ ๊ฐํƒ„์ด;  ๋ฆฌ๋ทฐ 20: ์ ์ ˆํ•˜๋‹ค;  ๋ฆฌ๋ทฐ 21: ์žฅ๋‚œํ•˜๋Š”๊ฒƒ๋„์•„๋‹ˆ๊ณ  ๋ฐ•์‹œํ™˜์ด ๋ญ๊ฐ€ ์ž˜ํ–ˆ๋‹ค๊ณ  ์ธ๊ธฐ๊ฐ€ ์‹ค๋ ฅ์ธ๊ฐ€ใ…กใ…กํŒฌ๋ถ„๋“ค๋„ ๋‹ค๋ฅธ ์‚ฌ๋žŒ์— ์ž…์žฅ์„ ์ƒ๊ฐํ•˜๊ณ ์กด์ค‘ํ•ด์•ผ์ง€ ์™œ์ž๊ธฐ๋“ค์ด์ข‹์•„ํ•˜๋Š”์‚ฌ๋žŒ์ด๋ผ๊ณ  ๋‚จ๋“คํ•œํ…Œ ๊ทธ๋”ด์‹์œผ๋กœ์• ๊ธฐํ•˜๋Š”๊ฑฐ์˜ˆ์š”ใ…‹ใ…‹์‹ค๋ ฅ์œผ๋ฃจ ์Šน๋ถ€ํ•˜๋Š”๊ฑฐ์ง€ ์ง€๋“ค์€ํ•˜์ง€๋„ ๋ชปํ• ๊ฑฐ๋ฉด์„œ ์†กํฌ์ง„๋””์Šค๊นŒ๊ณ ์ž‡์–ดใ…‹ใ…‹;  ๋ฆฌ๋ทฐ 22: ์ถ”์ฒœํ•˜๊ณ  ์‹ถ์€ ์‚ฌ๋ž‘์˜ํ™”, ๋ณต์žกํ•œ ์‚ฌ๋ž‘..๋‘ ๋ฐฐ์šฐ์˜ ์—ฐ๊ธฐ, ์—ฐ์ถœ๋ ฅ์œผ๋กœ ๋” ๋‹๋ณด์˜€์Šต๋‹ˆ๋‹ค.;  ๋ฆฌ๋ทฐ 23: ์‚ฌ์‹ค๊ฐ๋„˜์น˜๊ณ  ์ฒ˜์ ˆํ•˜๊ณ  ๊ฑฐ์นจ์—†๋Š” ๋งจ๋ชธ์Šคํ”ผ๋“œ ์•ก์…˜์˜ ๋ํŒ ์ดํ‘œํ˜„์œผ๋กœ๋„ ๋ถ€์กฑํ•œ๊ฒƒ๊ฐ™๋‹ค;  ๋ฆฌ๋ทฐ 24: ์—ญ์‹œ ๊ฐ€์กฑ์˜ํ™”๋Š” ์ด๋Ÿฐ๊ฒŒ ๋”ฑ์ด์—์š”;  ๋ฆฌ๋ทฐ 25: 1์— ๋น„ํ•ด ์กฐ๊ธˆ ์‹ค๋ง์Šค๋Ÿฌ์› ์Šต๋‹ˆ๋‹ค;  ๋ฆฌ๋ทฐ 26: ์ƒ๊ฐ ์—†์ด ์•ก์…˜์„ ์ข‹์•„ํ•œ๋‹ค๋ฉด ๊ตฟ ใ…‹ใ…‹ใ…‹ใ…‹;  ๋ฆฌ๋ทฐ 27: ์ƒ๋‚œ๋ฆฌ. ์ƒ์‡ผ.;  ๋ฆฌ๋ทฐ 28: ์ž ์ด์ œ ๊ณตํฌ๋ฅผ ๋ณด์—ฌ์ฃผ์„ธ์š” !;  ๋ฆฌ๋ทฐ 29: ์„ฑ์šฐ๋ž€ ์ง์—…์ด ์™œ์žˆ๋ƒ?;  ๋ฆฌ๋ทฐ 30: ๊ฐ•๋ ฌํ•œ ๋‚ด์šฉ, ๊ฐ•๋ ฌํ•œ ์ด๋ฏธ์ง€.;  ๋ฆฌ๋ทฐ 31: ์žฌ๋ฐ‹๋Š”๋ฐ ๋ญ”๊ฐ€ ๋ฒค์ œ๋งˆ๋‹ฎ์Œ;  ๋ฆฌ๋ทฐ 32: ์ง€ ๋งž์ถค๋ฒ•์ด๋‚˜ ๋งž์ถ”๊ณ  ์ด์•ผ๊ธฐํ•˜์‡ผ ใ…‹ใ…‹ ํ‰์  10์  ๋ฏธ๊ฐœ์ˆ˜์ค€ ใ…‹ใ…‹ใ…‹;  ๋ฆฌ๋ทฐ 33: ์žผ์žˆ๋„ค์—ฌ ใ…Žใ…Ž ๊ฝƒ๋†“๊ณ  ๊ฐ€์š”~~;  ๋ฆฌ๋ทฐ 34: ์กธ์ž‘;  ๋ฆฌ๋ทฐ 35: GTA์˜ํ™”๋ฒ„์ „์ธ์ค„ใ„ทใ„ท๋ฏธ์ฟก ๋’ท๊ณจ๋ชฉ ์–‘์•„์น˜๋“ค์˜ ์˜จ๊ฐ–๋ฒ”์ฃ„ ์ด๋ง๋ผ~ํ•œ๊ตญ ์ค‘2๋ณ‘๋“ค์€ ๊ฑ ์ˆœ๋‘ฅ์ด์ž„~ใ…Žใ„ทใ„ท;  ๋ฆฌ๋ทฐ 36: ๋ˆ๋‚ด๊ณ ๋ดฃ๋Š”๋ฐ ๋ณด๋‹ค๋ณด๋‹ค ๋ชป๋ด์ค˜์„œ ๊บผ๋ฒ„๋ ท๋‹ค ์ง„์งœ .................................;  ๋ฆฌ๋ทฐ 37: ์žฅ๋ฅ ์˜ ๋˜ ํ•˜๋‚˜์˜ ๊ฑธ์ž‘!;  ๋ฆฌ๋ทฐ 38: ์•„........;  ๋ฆฌ๋ทฐ 39: ์ž ์‹œ ๋™์‹ฌ์˜ ์„ธ๊ณ„๋กœ ๋‚  ๋ฐ๋ ค๋‹ค ์ค€ ์˜ํ™”..;  ๋ฆฌ๋ทฐ 40: ์ด ๋‹คํ๋ฅผ ๋ณด๋Š”๋‚ด๋‚ด ์†Œ๋ฆ„์ด ๋‹์•˜๋‹ค. ์ •์กฐ๋Œ€์™•์˜ ์œ„๋Œ€ํ•จ์—, ์ •์กฐ๋Œ€์™•์˜ ์•”์‚ด์œ„๊ธฐ์—, ์ˆ˜์›ํ™”์„ฑ ์ œ์ž‘๊ณผ์ •์—, ํ•œ๊ฐ•์— ๋‹ค๋ฆฌ์—†๋˜ ์‹œ์ ˆ ๋ฐฐ๋‹ค๋ฆฌ ๋งŒ๋“œ๋Š” ๊ณผ์ •๋“ฑ์„ ๋ณด๋ฉด์„œ~~~์˜ˆ์ „์— ๋ดค๋˜ '์ •์กฐ์•”์‚ด ๋ฏธ์Šคํ…Œ๋ฆฌ 8์ผ' ์ด ์ƒ๊ฐ๋‚ฌ๋‹ค. ์—„์ฒญ ๊ฐ๋™๋ฐ›์•˜์—ˆ๋Š”๋ฐ...;  ๋ฆฌ๋ทฐ 41: ์ตœ๊ทผ์— ๋ดค๋Š”๋ฐ..์ƒ‰๊ฐ๋„ ๋„ˆ๋ฌด ์ด์˜๊ณ ..๋‹ค ์•„๋Š”๋‚ด์šฉ์ด์ง€๋งŒ ๊ทธ๋ž˜๋„ ์ข‹๋„ค์š”~;  ๋ฆฌ๋ทฐ 42: ๊ณ ์‚ฌ2๋ณด๋‹ค ๋‚˜์œผ๋‚˜์ด ์˜ํ™”์˜ ๋ฌธ์ œ์ ์€ ๋ฌด์„ญ๋‹ค๊ธฐ๋ณด๋‹ค ์ž”์ธํ•œ๊ฒƒ์ด๊ณ  ์‚ฌ์‹ค ๊ทธ๋ฆฌ ์ž”์ธํ•œ๊ฒƒ๋„ ์•„๋‹ˆ๋ผ ์ฐจ๋ผ๋ฆฌ ์ž”์ธํ•˜๊ฒŒ ๋งŒ๋“ค๊ฑฐ๋ฉด 19์„ธ ์ด์šฉ๊ฐ€ ๊ธ‰์œผ๋กœ ๋งŒ๋“ค์—ˆ์–ด์•ผ ํ–ˆ๋‹ค. ์Šคํ† ๋ฆฌ๋„ ์ง„๋ถ€ํ•˜๊ณ  ๋” ๊นŠ๊ฒŒ ํŒŒ๊ณ ๋“ค์—ˆ์–ด์•ผํ–ˆ๋‹ค. ์ด์˜ํ™”์—์„œ ์นญ์ฐฌ๋ฐ›์„๋งŒํ•œ ์œ ์ผํ•œ ์š”์†Œ๋Š” ๋ธŒ๊ธˆ.;  ๋ฆฌ๋ทฐ 43: ์กฐ์„ ์˜ 3๋Œ€ ์•”๊ตฐ ๊ณ ์ข…์ด๋ž‘ ์ง€ ์™ธ์ฒ™๋“ค๋กœ ์„ธ๋„์ •์น˜ ํ•˜๋‹ค๊ฐ€ ๋‚˜๋ผ๋ง์•„๋จน์€ ๋ฏผ๋น„๋…„ ์ถ”์ผœ์„ธ์šฐ๋Š” ์˜ํ™” ใ…‹ใ…‹ ๊ทธ๋Ÿฐ์˜ํ™” ์ข‹๋‹ค๊ณ  ๊ตญ๋ฝ• ๊ฑฐํ•˜๊ฒŒ ํ•œ์‚ฌ๋ฐœ ํ•˜๋Š” ๋†ˆ๋“ค ์ˆ˜์ค€ ใ…‹ใ…‹ใ…‹ ๊ทธ ์™€์ค‘์— ๋ถ€์นด๋‹ˆ์Šคํƒ„ ์˜นํ˜ธ๊นŒ์ง€ ใ…‹ใ…‹;  ๋ฆฌ๋ทฐ 44: ๋„˜์–ด๋‘ก๊ณ ์ง€๋ฃจํ•˜๋‹ค.๊ฐ๋…์ž์‹ ์˜์˜๋„๋ฅผ์ž˜ํ‘œํ˜„ํ•ด๋‚ด์ง€๋ชปํ–ˆ๋‹ค.๊ฐ€์กฑ์˜ํ™”๋กœ๋Š”๋น„์ถ”์ฒœ.;  ๋ฆฌ๋ทฐ 45: ์ƒ๋‹นํžˆ์ง€๋ฃจํ•˜๋‹ค... ์ •์‹ ์—†๋‹ค ํŠนํžˆ ๋งฅ๋ผ์ด์–ธ์ด.. ๋ณด๋‹ค๊ฐ€ ๊ป๋‹ค.;  ๋ฆฌ๋ทฐ 46: ์˜ํ™”ํ•ด์„ค๋ณด๊ณ  ์ด๋ ‡๊ฒŒ ์‹ค๋งํ•œ๊ฑด ์ฒ˜์Œ์ด๋„ค์š” ^^;  ๋ฆฌ๋ทฐ 47: ๊ทธ๋ƒฅ ์‹ฌ๊ฐํ•ฉ๋‹ˆ๋‹ค ๊ธ€๊ณ  ๋ณด์•„ ์™œ ๋‚˜์˜ค๋Š”๊ฑด์ง€...;  ๋ฆฌ๋ทฐ 48: ํ•˜..๊ฒฐ๋ง์ด์•„์‰ฝ๋„ค์š”;  ๋ฆฌ๋ทฐ 49: 1์ ;  ๋ฆฌ๋ทฐ 50: ์ด์ƒ์šฐ ๊ฐ๋…์˜ ์ƒ์ƒ๋ ฅ์˜ ๋์ด ๊ถ๊ตผํ•˜๋‹ค ๊ทธ์˜ ์ฐจ๊ธฐ์ž‘ ์ œ๋ชฉ์€ ๊ณผ์—ฐ?; 
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Sample output:

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์˜ํ™”์— ๋Œ€ํ•œ ๋ฆฌ๋ทฐ๋ฅผ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์Šต๋‹ˆ๋‹ค.

1. **๊ฐ์ • (Emotional): 5/10**  
   - ๋ฆฌ๋ทฐ์—์„œ ๊ฐ์ •์ ์ธ ์š”์†Œ๊ฐ€ ๋ถ€์กฑํ•˜๋‹ค๋Š” ์–ธ๊ธ‰์ด ๋งŽ์•˜์Šต๋‹ˆ๋‹ค. ์ผ๋ถ€๋Š” ๊ฐ๋™์„ ์ฃผ์—ˆ๋‹ค๊ณ  ํ•˜์ง€๋งŒ, ์ „๋ฐ˜์ ์œผ๋กœ ๊ฐ์ •์ ์œผ๋กœ ๊นŠ์ด ์žˆ๋Š” ์š”์†Œ๊ฐ€ ๋ถ€์กฑํ•œ ๊ฒƒ์œผ๋กœ ํ‰๊ฐ€๋ฉ๋‹ˆ๋‹ค.

2. **์บ๋ฆญํ„ฐ (Characters): 4/10**  
   - ์บ๋ฆญํ„ฐ์— ๋Œ€ํ•œ ๋น„ํŒ์ด ๋งŽ์•˜์œผ๋ฉฐ, ํŠนํžˆ ๋ช‡๋ช‡ ๋ฆฌ๋ทฐ์—์„œ๋Š” ์บ๋ฆญํ„ฐ์˜ ๊นŠ์ด๊ฐ€ ๋ถ€์กฑํ•˜๋‹ค๊ณ  ์–ธ๊ธ‰๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ์ด๋กœ ์ธํ•ด ์บ๋ฆญํ„ฐ์— ๋Œ€ํ•œ ํ˜ธ๊ฐ์ด ๋‚ฎ์€ ๊ฒƒ์œผ๋กœ ๋ณด์ž…๋‹ˆ๋‹ค.

3. **ํ”Œ๋กฏ (Plot): 3/10**  
   - ํ”Œ๋กฏ์— ๋Œ€ํ•œ ๋ถˆ๋งŒ์ด ๋งŽ์•˜์Šต๋‹ˆ๋‹ค. ๋งŽ์€ ๋ฆฌ๋ทฐ์—์„œ ์Šคํ† ๋ฆฌ๊ฐ€ ์ง„๋ถ€ํ•˜๊ฑฐ๋‚˜ ์‹ค๋ง์Šค๋Ÿฝ๋‹ค๋Š” ์–ธ๊ธ‰์ด ์žˆ์—ˆ์Šต๋‹ˆ๋‹ค. ํ”Œ๋กฏ์˜ ์ „๊ฐœ๊ฐ€ ์ผ๊ด€์„ฑ์ด ์—†๊ณ , ํฅ๋ฏธ๋กญ์ง€ ์•Š๋‹ค๋Š” ํ‰๊ฐ€๊ฐ€ ์ง€๋ฐฐ์ ์ž…๋‹ˆ๋‹ค.

4. **๋น„์ฃผ์–ผ (Visuals): 6/10**  
   - ๋น„์ฃผ์–ผ์— ๋Œ€ํ•œ ๊ธ์ •์ ์ธ ์˜๊ฒฌ๋„ ์žˆ์ง€๋งŒ, ๊ทธ๋ž˜ํ”ฝ์ด๋‚˜ ์˜์ƒ๋ฏธ์— ๋Œ€ํ•œ ๋ถ€์ •์ ์ธ ์–ธ๊ธ‰๋„ ๋งŽ์Šต๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ „์ฒด์ ์œผ๋กœ ๋น„์ฃผ์–ผ์ด ์ค‘๊ฐ„ ์ •๋„๋กœ ํ‰๊ฐ€๋ฉ๋‹ˆ๋‹ค.

5. **ํŽ˜์ด์‹ฑ (Pacing): 4/10**  
   - ๋ฆฌ๋ทฐ์—์„œ ์˜ํ™”๊ฐ€ ์ง€๋ฃจํ•˜๋‹ค๋Š” ์˜๊ฒฌ์ด ๋งŽ์•˜์œผ๋ฉฐ, ํŠนํžˆ ์ง€๋ฃจํ•œ ๋ถ€๋ถ„์ด ๋งŽ์€ ๊ฒƒ์œผ๋กœ ๋ณด์ž…๋‹ˆ๋‹ค. ํŽ˜์ด์‹ฑ์ด ์ข‹์ง€ ์•Š๋‹ค๋Š” ํ‰์ด ์ง€๋ฐฐ์ ์ž…๋‹ˆ๋‹ค.

์ข…ํ•ฉ์ ์œผ๋กœ, ์ด ์˜ํ™”๋Š” ๊ฐ์ •, ์บ๋ฆญํ„ฐ, ํ”Œ๋กฏ, ํŽ˜์ด์‹ฑ์—์„œ ๋ชจ๋‘ ๋‚ฎ์€ ์ ์ˆ˜๋ฅผ ์–ป์—ˆ์Šต๋‹ˆ๋‹ค. ๋น„์ฃผ์–ผ์€ ์ค‘๊ฐ„ ์ •๋„๋กœ ํ‰๊ฐ€๋˜์ง€๋งŒ, ์ „๋ฐ˜์ ์œผ๋กœ ์‹ค๋ง์Šค๋Ÿฌ์šด ์˜ํ™”๋กœ ํ‰๊ฐ€๋ฉ๋‹ˆ๋‹ค.
```

# Uploaded  model

- **Developed by:** divakaivan
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit

This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)