gemma-2b-it
#1
by
kreimben
- opened
- .gitattributes +0 -1
- README.md +13 -207
- config.json +0 -28
- generation_config.json +0 -7
- model-00001-of-00002.safetensors +0 -3
- model-00002-of-00002.safetensors +0 -3
- model.safetensors.index.json +0 -171
- special_tokens_map.json +0 -28
- tokenizer.json +0 -3
- tokenizer_config.json +0 -1757
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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language:
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- en
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license: mit
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library_name: transformers
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tags:
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- code
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base_model:
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- google/gemma-1.1-2b-it
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datasets:
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- kreimben/leetcode_with_youtube_captions
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- kreimben/leetcode_user_submissions
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widget:
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- text: explain about leetcode 72 edit distance. i don't get even the approach.
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example_title: edit distance example
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- text: explain about leetcode 139 Word Break. please give me the approach.
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example_title: word break example
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inference:
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parameters:
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max_new_tokens:
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pipeline_tag: text-generation
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---
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# CodeMind
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## 소개
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코딩 테스트 문제 해결 및 학습 보조를 지원해 주는 언어 모델입니다. Leetcode 해설 영상 자막 및 유저들의 포스팅 글을 이용해 파인튜닝하여 코딩 테스트에 조금 더 특화된 답안을 제시해 줄 수 있게 하였습니다.
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## 모델 세부 정보
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- **모델 이름**: CodeMind
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- **기본 모델**: google/gemma-1.1-2b-it
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- **훈련 언어**: 영어
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- **모델 크기**: 2.51B 파라미터
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## 팀원 구성
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- NLP 3명
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- SRE 2명
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## 주요 기능
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- 문제 유형 및 접근법 설명
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- 정답 코드 생성
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## 훈련 데이터
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- [**LeetCode 사용자 제출물**](https://huggingface.co/datasets/kreimben/leetcode_user_submissions): 다양한 알고리즘 문제의 파이썬 솔루션
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- [**유튜브 캡션**](https://huggingface.co/datasets/kreimben/leetcode_with_youtube_captions): LeetCode 문제에 대한 설명 및 단계별 가이드
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## 사용된 라이브러리
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- [transformers](https://github.com/huggingface/transformers): 자연어 처리 모델을 위한 라이브러리
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- [datasets](https://github.com/huggingface/datasets): 데이터셋 처리 및 관리 라이브러리
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- [bitsandbytes](https://github.com/TimDettmers/bitsandbytes): 최적화된 연산을 위한 라이브러리
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- [peft](https://github.com/huggingface/peft): 파인 튜닝을 위한 라이브러리
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- [trl](https://github.com/huggingface/trl): 언어 모델 튜닝을 위한 라이브러리
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- [pandas](https://github.com/pandas-dev/pandas): 데이터 조작을 위한 라이브러리
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## 파일 구조
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- **dataset/**: 데이터셋 파일을 포함합니다.
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- **eval/**: 평가 스크립트를 포함합니다.
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- **fine-tuning/**: fine tuning 관련 노트북 및 스크립트를 포함합니다.
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- `gemma-1.1-2b-it peft qlora.ipynb`: fine tuning 과정에 대한 세부 사항이 포함된 노트북입니다.
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- **demo.ipynb**: 데모 노트북으로 모델 사용 예제가 포함되어 있습니다.
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- **requirements.txt**: 프로젝트 의존성 목록이 포함되어 있습니다.
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- **utils.py**: 유틸리티 함수들이 포함되어 있습니다.
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## 사용 방법
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이 모델은 HuggingFace의 모델 허브를 통해 액세스할 수 있으며, API를 사용하여 응용 프로그램에 통합할 수 있습니다. 코딩 문제 또는 프로그래밍 관련 질문을 제공하면 모델이 관련 설명, 코드 스니펫 또는 가이드를 생성합니다.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("kreimben/CodeMind-gemma-2b")
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model = AutoModelForCausalLM.from_pretrained("kreimben/CodeMind-gemma-2b")
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inputs = tokenizer("코딩 문제나 질문을 여기에 입력하세요", return_tensors="pt")
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outputs = model.generate(inputs.input_ids)
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print(tokenizer.decode(outputs[0]))
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```
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## 훈련 과정
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### 모델 및 토크나이저 로드
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```python
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import os
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16
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)
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model_id = 'google/gemma-1.1-2b-it'
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token = os.getenv('HF_READ')
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model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={"": 0}, token=token)
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model.config.use_cache = False
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model.gradient_checkpointing_enable()
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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tokenizer.padding_side = 'right'
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tokenizer.pad_token = tokenizer.eos_token
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```
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### LoRA 구성 및 모델 준비
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```python
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from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
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import bitsandbytes as bnb
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model = prepare_model_for_kbit_training(model)
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def find_all_linear_names(model):
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cls = bnb.nn.Linear4bit
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lora_module_names = set()
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for name, module in model.named_modules():
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if isinstance(module, cls):
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names = name.split('.')
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lora_module_names.add(names[0] if len(names) == 1 else names[-1])
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if 'lm_head' in lora_module_names:
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lora_module_names.remove('lm_head')
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return list(lora_module_names)
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modules = find_all_linear_names(model)
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lora_config = LoraConfig(
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r=64,
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lora_alpha=32,
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target_modules=modules,
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lora_dropout=0.05,
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bias="none",
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task_type="CAUSAL_LM"
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)
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model = get_peft_model(model, lora_config)
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```
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### 데이터 준비
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```python
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import pandas as pd
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from datasets import Dataset
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submission_dataset = datasets.load_dataset('kreimben/leetcode_user_submissions_only_python', split='train').to_pandas()
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submission_dataset = submission_dataset[['title', 'question_hints', 'question_content', 'content']]
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captions_dataset = datasets.load_dataset('kreimben/leetcode_with_youtube_captions', split='train').to_pandas()
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captions_dataset = captions_dataset[['title', 'question_hints', 'question_content', 'cc_content']]
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captions_dataset.rename(columns={'cc_content': 'content'}, inplace=True)
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dataset = pd.concat([submission_dataset, captions_dataset])
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del submission_dataset, captions_dataset
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dataset = Dataset.from_pandas(dataset)
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GEMMA_2B_IT_MODEL_PREFIX_TEXT = "Below is an coding test problem. Solve the question."
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def generate_prompt(data_point):
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return f"<bos><start_of_turn>user {GEMMA_2B_IT_MODEL_PREFIX_TEXT}
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I don't know {data_point['title']} problem. give me the insight or appoach.
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this is problem's hint.
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{data_point['question_hints']}
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here are some content of question.
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{data_point['question_content']}<end_of_turn>
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<start_of_turn>model {data_point['content']}<end_of_turn><eos>"
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text_column = [generate_prompt(data_point) for data_point in dataset]
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dataset = dataset.add_column("prompt", text_column)
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```
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### 훈련
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```python
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from trl import SFTTrainer
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import transformers
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import torch
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tokenizer.pad_token = tokenizer.eos_token
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torch.cuda.empty_cache()
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trainer = SFTTrainer(
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model=model,
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train_dataset=dataset,
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dataset_text_field="prompt",
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peft_config=lora_config,
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data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),
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args=transformers.TrainingArguments(
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output_dir='out',
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bf16=True,
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max_steps=100,
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warmup_steps=50,
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per_device_train_batch_size=1,
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gradient_accumulation_steps=1,
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optim="paged_adamw_8bit",
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logging_steps=20,
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report_to='wandb',
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),
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)
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trainer.train()
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```
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## 평가
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모델의 성능은 다음과 같이 평가되었습니다:
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| Metric | Value |
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| Average | 41.62 |
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| ARC | 41.81 |
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| HellaSwag | 59.03 |
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| MMLU | 37.26 |
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| TruthfulQA | 43.45 |
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| Winogrande | 59.91 |
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| GSM8K | 8.26 |
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## 제한 사항 및 윤리적 고려사항
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- 모델의 출력은 학습 데이터에 기반하므로 항상 정확하지 않을 수 있습니다.
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- 중요한 결정이나 실세계 문제 해결에 모델 출력을 사용하기 전에 반드시 검증이 필요합니다.
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---
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license: mit
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library_name: transformers
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widget:
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- messages:
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- role: user
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content: explain about leetcode 1 add sum problem.
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inference:
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parameters:
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max_new_tokens: 200
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datasets:
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- kreimben/leetcode_with_youtube_captions
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- kreimben/leetcode_user_submissions
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language:
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- en
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pipeline_tag: text-generation
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base_model:
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- google-bert/bert-base-uncased
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- google/gemma-2b-it
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---
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# CodeMind
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Coding Test Explanatory LLM Model.
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config.json
DELETED
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{
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"_name_or_path": "google/gemma-1.1-2b-it",
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"architectures": [
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"GemmaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 2,
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"eos_token_id": 1,
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"head_dim": 256,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_activation": "gelu_pytorch_tanh",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 16384,
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"max_position_embeddings": 8192,
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"model_type": "gemma",
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"num_attention_heads": 8,
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"num_hidden_layers": 18,
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"num_key_value_heads": 1,
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"pad_token_id": 0,
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"rms_norm_eps": 1e-06,
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"rope_theta": 10000.0,
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"torch_dtype": "float16",
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"transformers_version": "4.39.3",
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"use_cache": true,
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special_tokens_map.json
DELETED
@@ -1,28 +0,0 @@
|
|
1 |
-
{
|
2 |
-
"additional_special_tokens": [
|
3 |
-
"<start_of_turn>",
|
4 |
-
"<end_of_turn>"
|
5 |
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|
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|
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|
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|
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|
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|
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|
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|
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tokenizer.json
DELETED
@@ -1,3 +0,0 @@
|
|
1 |
-
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:c15eb04bc5ad609fb26533e8525302c5640a945e5f67f65b7c849900acda7d99
|
3 |
-
size 17518497
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tokenizer_config.json
DELETED
@@ -1,1757 +0,0 @@
|
|
1 |
-
{
|
2 |
-
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|
3 |
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},
|
1653 |
-
"206": {
|
1654 |
-
"content": "<sup>",
|
1655 |
-
"lstrip": false,
|
1656 |
-
"normalized": false,
|
1657 |
-
"rstrip": false,
|
1658 |
-
"single_word": false,
|
1659 |
-
"special": false
|
1660 |
-
},
|
1661 |
-
"207": {
|
1662 |
-
"content": "<code>",
|
1663 |
-
"lstrip": false,
|
1664 |
-
"normalized": false,
|
1665 |
-
"rstrip": false,
|
1666 |
-
"single_word": false,
|
1667 |
-
"special": false
|
1668 |
-
},
|
1669 |
-
"208": {
|
1670 |
-
"content": "</strong>",
|
1671 |
-
"lstrip": false,
|
1672 |
-
"normalized": false,
|
1673 |
-
"rstrip": false,
|
1674 |
-
"single_word": false,
|
1675 |
-
"special": false
|
1676 |
-
},
|
1677 |
-
"209": {
|
1678 |
-
"content": "</em>",
|
1679 |
-
"lstrip": false,
|
1680 |
-
"normalized": false,
|
1681 |
-
"rstrip": false,
|
1682 |
-
"single_word": false,
|
1683 |
-
"special": false
|
1684 |
-
},
|
1685 |
-
"210": {
|
1686 |
-
"content": "</b>",
|
1687 |
-
"lstrip": false,
|
1688 |
-
"normalized": false,
|
1689 |
-
"rstrip": false,
|
1690 |
-
"single_word": false,
|
1691 |
-
"special": false
|
1692 |
-
},
|
1693 |
-
"211": {
|
1694 |
-
"content": "</i>",
|
1695 |
-
"lstrip": false,
|
1696 |
-
"normalized": false,
|
1697 |
-
"rstrip": false,
|
1698 |
-
"single_word": false,
|
1699 |
-
"special": false
|
1700 |
-
},
|
1701 |
-
"212": {
|
1702 |
-
"content": "</u>",
|
1703 |
-
"lstrip": false,
|
1704 |
-
"normalized": false,
|
1705 |
-
"rstrip": false,
|
1706 |
-
"single_word": false,
|
1707 |
-
"special": false
|
1708 |
-
},
|
1709 |
-
"213": {
|
1710 |
-
"content": "</s>",
|
1711 |
-
"lstrip": false,
|
1712 |
-
"normalized": false,
|
1713 |
-
"rstrip": false,
|
1714 |
-
"single_word": false,
|
1715 |
-
"special": false
|
1716 |
-
},
|
1717 |
-
"214": {
|
1718 |
-
"content": "</sub>",
|
1719 |
-
"lstrip": false,
|
1720 |
-
"normalized": false,
|
1721 |
-
"rstrip": false,
|
1722 |
-
"single_word": false,
|
1723 |
-
"special": false
|
1724 |
-
},
|
1725 |
-
"215": {
|
1726 |
-
"content": "</sup>",
|
1727 |
-
"lstrip": false,
|
1728 |
-
"normalized": false,
|
1729 |
-
"rstrip": false,
|
1730 |
-
"single_word": false,
|
1731 |
-
"special": false
|
1732 |
-
},
|
1733 |
-
"216": {
|
1734 |
-
"content": "</code>",
|
1735 |
-
"lstrip": false,
|
1736 |
-
"normalized": false,
|
1737 |
-
"rstrip": false,
|
1738 |
-
"single_word": false,
|
1739 |
-
"special": false
|
1740 |
-
}
|
1741 |
-
},
|
1742 |
-
"additional_special_tokens": [
|
1743 |
-
"<start_of_turn>",
|
1744 |
-
"<end_of_turn>"
|
1745 |
-
],
|
1746 |
-
"bos_token": "<bos>",
|
1747 |
-
"chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}",
|
1748 |
-
"clean_up_tokenization_spaces": false,
|
1749 |
-
"eos_token": "<eos>",
|
1750 |
-
"model_max_length": 1000000000000000019884624838656,
|
1751 |
-
"pad_token": "<eos>",
|
1752 |
-
"sp_model_kwargs": {},
|
1753 |
-
"spaces_between_special_tokens": false,
|
1754 |
-
"tokenizer_class": "GemmaTokenizer",
|
1755 |
-
"unk_token": "<unk>",
|
1756 |
-
"use_default_system_prompt": false
|
1757 |
-
}
|
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