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README.md CHANGED
@@ -1,217 +1,23 @@
1
  ---
2
- language:
3
- - en
4
  license: mit
5
  library_name: transformers
6
- tags:
7
- - code
8
- base_model:
9
- - google/gemma-1.1-2b-it
10
- datasets:
11
- - kreimben/leetcode_with_youtube_captions
12
- - kreimben/leetcode_user_submissions
13
  widget:
14
- - text: explain about two sum problem. from brute force approach to the most advanced
15
- algorithms.
16
- example_title: two sum example
17
- - text: explain about leetcode 72 edit distance. i don't get even the approach.
18
- example_title: edit distance example
19
- - text: explain about leetcode 139 Word Break. please give me the approach.
20
- example_title: word break example
21
  inference:
22
  parameters:
23
- max_new_tokens: 250
24
- temperature: 0.3
 
 
 
 
25
  pipeline_tag: text-generation
 
 
 
26
  ---
27
 
28
  # CodeMind
29
-
30
- ## 소개
31
- 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.
32
-
33
- ## 모델 세부 정보
34
- - **모델 이름**: CodeMind
35
- - **기본 모델**: google/gemma-1.1-2b-it
36
- - **훈련 언어**: 영어
37
- - **모델 크기**: 2.51B 파라미터
38
-
39
- ## 팀원 구성
40
- - NLP 3명
41
- - SRE 2명
42
-
43
- ## 주요 기능
44
- - 문제 유형 및 접근법 설명
45
- - 정답 코드 생성
46
-
47
- ## 훈련 데이터
48
- - [**LeetCode 사용자 제출물**](https://huggingface.co/datasets/kreimben/leetcode_user_submissions): 다양한 알고리즘 문제의 파이썬 솔루션
49
- - [**유튜브 캡션**](https://huggingface.co/datasets/kreimben/leetcode_with_youtube_captions): LeetCode 문제에 대한 설명 및 단계별 가이드
50
-
51
- ## 사용된 라이브러리
52
- - [transformers](https://github.com/huggingface/transformers): 자연어 처리 모델을 위한 라이브러리
53
- - [datasets](https://github.com/huggingface/datasets): 데이터셋 처리 및 관리 라이브러리
54
- - [bitsandbytes](https://github.com/TimDettmers/bitsandbytes): 최적화된 연산을 위한 라이브러리
55
- - [peft](https://github.com/huggingface/peft): 파인 튜닝을 위한 라이브러리
56
- - [trl](https://github.com/huggingface/trl): 언어 모델 튜닝을 위한 라이브러리
57
- - [pandas](https://github.com/pandas-dev/pandas): 데이터 조작을 위한 라이브러리
58
-
59
- ## 파일 구조
60
- - **dataset/**: 데이터셋 파일을 포함합니다.
61
- - **eval/**: 평가 스크립트를 포함합니다.
62
- - **fine-tuning/**: fine tuning 관련 노트북 및 스크립트를 포함합니다.
63
- - `gemma-1.1-2b-it peft qlora.ipynb`: fine tuning 과정에 대한 세부 사항이 포함된 노트북입니다.
64
- - **demo.ipynb**: 데모 노트북으로 모델 사용 예제가 포함되어 있습니다.
65
- - **requirements.txt**: 프로젝트 의존성 목록이 포함되어 있습니다.
66
- - **utils.py**: 유틸리티 함수들이 포함되어 있습니다.
67
-
68
- ## 사용 방법
69
- 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.
70
-
71
- ```python
72
- from transformers import AutoModelForCausalLM, AutoTokenizer
73
-
74
- tokenizer = AutoTokenizer.from_pretrained("kreimben/CodeMind-gemma-2b")
75
- model = AutoModelForCausalLM.from_pretrained("kreimben/CodeMind-gemma-2b")
76
-
77
- inputs = tokenizer("코딩 문제나 질문을 여기에 입력하세요", return_tensors="pt")
78
- outputs = model.generate(inputs.input_ids)
79
- print(tokenizer.decode(outputs[0]))
80
- ```
81
-
82
- ## 훈련 과정
83
-
84
- ### 모델 및 토크나이저 로드
85
- ```python
86
- import os
87
- from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
88
-
89
- bnb_config = BitsAndBytesConfig(
90
- load_in_4bit=True,
91
- bnb_4bit_quant_type="nf4",
92
- bnb_4bit_compute_dtype=torch.bfloat16
93
- )
94
-
95
- model_id = 'google/gemma-1.1-2b-it'
96
- token = os.getenv('HF_READ')
97
-
98
- model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={"": 0}, token=token)
99
- model.config.use_cache = False
100
- model.gradient_checkpointing_enable()
101
-
102
- tokenizer = AutoTokenizer.from_pretrained(model_id)
103
- tokenizer.padding_side = 'right'
104
- tokenizer.pad_token = tokenizer.eos_token
105
- ```
106
-
107
- ### LoRA 구성 및 모델 준비
108
- ```python
109
- from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
110
- import bitsandbytes as bnb
111
-
112
- model = prepare_model_for_kbit_training(model)
113
-
114
- def find_all_linear_names(model):
115
- cls = bnb.nn.Linear4bit
116
- lora_module_names = set()
117
- for name, module in model.named_modules():
118
- if isinstance(module, cls):
119
- names = name.split('.')
120
- lora_module_names.add(names[0] if len(names) == 1 else names[-1])
121
- if 'lm_head' in lora_module_names:
122
- lora_module_names.remove('lm_head')
123
- return list(lora_module_names)
124
-
125
- modules = find_all_linear_names(model)
126
- lora_config = LoraConfig(
127
- r=64,
128
- lora_alpha=32,
129
- target_modules=modules,
130
- lora_dropout=0.05,
131
- bias="none",
132
- task_type="CAUSAL_LM"
133
- )
134
-
135
- model = get_peft_model(model, lora_config)
136
- ```
137
-
138
- ### 데이터 준비
139
- ```python
140
- import pandas as pd
141
- from datasets import Dataset
142
-
143
- submission_dataset = datasets.load_dataset('kreimben/leetcode_user_submissions_only_python', split='train').to_pandas()
144
- submission_dataset = submission_dataset[['title', 'question_hints', 'question_content', 'content']]
145
- captions_dataset = datasets.load_dataset('kreimben/leetcode_with_youtube_captions', split='train').to_pandas()
146
- captions_dataset = captions_dataset[['title', 'question_hints', 'question_content', 'cc_content']]
147
- captions_dataset.rename(columns={'cc_content': 'content'}, inplace=True)
148
-
149
- dataset = pd.concat([submission_dataset, captions_dataset])
150
- del submission_dataset, captions_dataset
151
-
152
- dataset = Dataset.from_pandas(dataset)
153
- GEMMA_2B_IT_MODEL_PREFIX_TEXT = "Below is an coding test problem. Solve the question."
154
-
155
- def generate_prompt(data_point):
156
- return f"<bos><start_of_turn>user {GEMMA_2B_IT_MODEL_PREFIX_TEXT}
157
-
158
- I don't know {data_point['title']} problem. give me the insight or appoach.
159
-
160
- this is problem's hint.
161
- {data_point['question_hints']}
162
-
163
- here are some content of question.
164
- {data_point['question_content']}<end_of_turn>
165
- <start_of_turn>model {data_point['content']}<end_of_turn><eos>"
166
-
167
- text_column = [generate_prompt(data_point) for data_point in dataset]
168
- dataset = dataset.add_column("prompt", text_column)
169
- ```
170
-
171
- ### 훈련
172
- ```python
173
- from trl import SFTTrainer
174
- import transformers
175
- import torch
176
-
177
- tokenizer.pad_token = tokenizer.eos_token
178
- torch.cuda.empty_cache()
179
-
180
- trainer = SFTTrainer(
181
- model=model,
182
- train_dataset=dataset,
183
- dataset_text_field="prompt",
184
- peft_config=lora_config,
185
- data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),
186
- args=transformers.TrainingArguments(
187
- output_dir='out',
188
- bf16=True,
189
- max_steps=100,
190
- warmup_steps=50,
191
- per_device_train_batch_size=1,
192
- gradient_accumulation_steps=1,
193
- optim="paged_adamw_8bit",
194
- logging_steps=20,
195
- report_to='wandb',
196
- ),
197
- )
198
-
199
- trainer.train()
200
- ```
201
-
202
- ## 평가
203
- 모델의 성능은 다음과 같이 평가되었습니다:
204
-
205
- | Metric | Value |
206
- |--------------|--------|
207
- | Average | 41.62 |
208
- | ARC | 41.81 |
209
- | HellaSwag | 59.03 |
210
- | MMLU | 37.26 |
211
- | TruthfulQA | 43.45 |
212
- | Winogrande | 59.91 |
213
- | GSM8K | 8.26 |
214
-
215
- ## 제한 사항 및 윤리적 고려사항
216
- - 모델의 출력은 학습 데이터에 기반하므로 항상 정확하지 않을 수 있습니다.
217
- - 중요한 결정이나 실세계 문제 해결에 모델 출력을 사용하기 전에 반드시 검증이 필요합니다.
 
1
  ---
 
 
2
  license: mit
3
  library_name: transformers
 
 
 
 
 
 
 
4
  widget:
5
+ - messages:
6
+ - role: user
7
+ content: explain about leetcode 1 add sum problem.
 
 
 
 
8
  inference:
9
  parameters:
10
+ max_new_tokens: 200
11
+ datasets:
12
+ - kreimben/leetcode_with_youtube_captions
13
+ - kreimben/leetcode_user_submissions
14
+ language:
15
+ - en
16
  pipeline_tag: text-generation
17
+ base_model:
18
+ - google-bert/bert-base-uncased
19
+ - google/gemma-2b-it
20
  ---
21
 
22
  # CodeMind
23
+ Coding Test Explanatory LLM Model.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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1550
- "content": "</h2>",
1551
- "lstrip": false,
1552
- "normalized": false,
1553
- "rstrip": false,
1554
- "single_word": false,
1555
- "special": false
1556
- },
1557
- "194": {
1558
- "content": "</h3>",
1559
- "lstrip": false,
1560
- "normalized": false,
1561
- "rstrip": false,
1562
- "single_word": false,
1563
- "special": false
1564
- },
1565
- "195": {
1566
- "content": "</h4>",
1567
- "lstrip": false,
1568
- "normalized": false,
1569
- "rstrip": false,
1570
- "single_word": false,
1571
- "special": false
1572
- },
1573
- "196": {
1574
- "content": "</h5>",
1575
- "lstrip": false,
1576
- "normalized": false,
1577
- "rstrip": false,
1578
- "single_word": false,
1579
- "special": false
1580
- },
1581
- "197": {
1582
- "content": "</h6>",
1583
- "lstrip": false,
1584
- "normalized": false,
1585
- "rstrip": false,
1586
- "single_word": false,
1587
- "special": false
1588
- },
1589
- "198": {
1590
- "content": "</blockquote>",
1591
- "lstrip": false,
1592
- "normalized": false,
1593
- "rstrip": false,
1594
- "single_word": false,
1595
- "special": false
1596
- },
1597
- "199": {
1598
- "content": "<strong>",
1599
- "lstrip": false,
1600
- "normalized": false,
1601
- "rstrip": false,
1602
- "single_word": false,
1603
- "special": false
1604
- },
1605
- "200": {
1606
- "content": "<em>",
1607
- "lstrip": false,
1608
- "normalized": false,
1609
- "rstrip": false,
1610
- "single_word": false,
1611
- "special": false
1612
- },
1613
- "201": {
1614
- "content": "<b>",
1615
- "lstrip": false,
1616
- "normalized": false,
1617
- "rstrip": false,
1618
- "single_word": false,
1619
- "special": false
1620
- },
1621
- "202": {
1622
- "content": "<i>",
1623
- "lstrip": false,
1624
- "normalized": false,
1625
- "rstrip": false,
1626
- "single_word": false,
1627
- "special": false
1628
- },
1629
- "203": {
1630
- "content": "<u>",
1631
- "lstrip": false,
1632
- "normalized": false,
1633
- "rstrip": false,
1634
- "single_word": false,
1635
- "special": false
1636
- },
1637
- "204": {
1638
- "content": "<s>",
1639
- "lstrip": false,
1640
- "normalized": false,
1641
- "rstrip": false,
1642
- "single_word": false,
1643
- "special": false
1644
- },
1645
- "205": {
1646
- "content": "<sub>",
1647
- "lstrip": false,
1648
- "normalized": false,
1649
- "rstrip": false,
1650
- "single_word": false,
1651
- "special": false
1652
- },
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
- }