cgus
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Question Answering
qwen2
biology
medical
4-bit precision
exl2
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README.md ADDED
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1
+ ---
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+ license: apache-2.0
3
+ datasets:
4
+ - FreedomIntelligence/ApolloMoEDataset
5
+ language:
6
+ - ar
7
+ - en
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+ - zh
9
+ - ko
10
+ - ja
11
+ - mn
12
+ - th
13
+ - vi
14
+ - lo
15
+ - mg
16
+ - de
17
+ - pt
18
+ - es
19
+ - fr
20
+ - ru
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+ - it
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+ - hr
23
+ - gl
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+ - cs
25
+ - co
26
+ - la
27
+ - uk
28
+ - bs
29
+ - bg
30
+ - eo
31
+ - sq
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+ - da
33
+ - sa
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+ - gn
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+ - sr
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+ - sk
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+ - gd
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+ - lb
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+ - hi
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+ - ku
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+ - mt
42
+ - he
43
+ - ln
44
+ - bm
45
+ - sw
46
+ - ig
47
+ - rw
48
+ - ha
49
+ metrics:
50
+ - accuracy
51
+ base_model:
52
+ - Qwen/Qwen2-7B
53
+ pipeline_tag: question-answering
54
+ tags:
55
+ - biology
56
+ - medical
57
+ ---
58
+ # Democratizing Medical LLMs For Much More Languages
59
+
60
+ Covering 12 Major Languages including English, Chinese, French, Hindi, Spanish, Arabic, Russian, Japanese, Korean, German, Italian, Portuguese and 38 Minor Languages So far.
61
+
62
+
63
+
64
+ <p align="center">
65
+ 📃 <a href="https://arxiv.org/abs/2410.10626" target="_blank">Paper</a> • 🌐 <a href="" target="_blank">Demo</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEDataset" target="_blank">ApolloMoEDataset</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEBench" target="_blank">ApolloMoEBench</a> • 🤗 <a href="https://huggingface.co/collections/FreedomIntelligence/apollomoe-and-apollo2-670ddebe3bb1ba1aebabbf2c" target="_blank">Models</a> •🌐 <a href="https://github.com/FreedomIntelligence/Apollo" target="_blank">Apollo</a> • 🌐 <a href="https://github.com/FreedomIntelligence/ApolloMoE" target="_blank">ApolloMoE</a>
66
+ </p>
67
+
68
+
69
+
70
+ ![Apollo](assets/apollo_medium_final.png)
71
+
72
+
73
+ ## 🌈 Update
74
+
75
+ * **[2024.10.15]** ApolloMoE repo is published!🎉
76
+
77
+
78
+ ## Languages Coverage
79
+ 12 Major Languages and 38 Minor Languages
80
+
81
+ <details>
82
+ <summary>Click to view the Languages Coverage</summary>
83
+
84
+ ![ApolloMoE](assets/languages.png)
85
+
86
+ </details>
87
+
88
+
89
+ ## Architecture
90
+
91
+ <details>
92
+ <summary>Click to view the MoE routing image</summary>
93
+
94
+ ![ApolloMoE](assets/hybrid_routing.png)
95
+
96
+ </details>
97
+
98
+ ## Results
99
+
100
+ #### Dense
101
+ 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-0.5B" target="_blank">Apollo2-0.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-1.5B" target="_blank">Apollo2-1.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-2B" target="_blank">Apollo2-2B</a>
102
+
103
+ 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-3.8B" target="_blank">Apollo2-3.8B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-7B" target="_blank">Apollo2-7B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-9B" target="_blank">Apollo2-9B</a>
104
+
105
+ <details>
106
+ <summary>Click to view the Dense Models Results</summary>
107
+
108
+ ![ApolloMoE](assets/dense_results.png)
109
+
110
+ </details>
111
+
112
+
113
+ #### Post-MoE
114
+ 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MoE-0.5B" target="_blank">Apollo-MoE-0.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MoE-1.5B" target="_blank">Apollo-MoE-1.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MoE-7B" target="_blank">Apollo-MoE-7B</a>
115
+
116
+ <details>
117
+ <summary>Click to view the Post-MoE Models Results</summary>
118
+
119
+ ![ApolloMoE](assets/post_moe_results.png)
120
+
121
+ </details>
122
+
123
+
124
+
125
+
126
+ ## Usage Format
127
+ ##### Apollo2
128
+ - 0.5B, 1.5B, 7B: User:{query}\nAssistant:{response}<|endoftext|>
129
+ - 2B, 9B: User:{query}\nAssistant:{response}\<eos\>
130
+ - 3.8B: <|user|>\n{query}<|end|><|assisitant|>\n{response}<|end|>
131
+
132
+ ##### Apollo-MoE
133
+ - 0.5B, 1.5B, 7B: User:{query}\nAssistant:{response}<|endoftext|>
134
+
135
+ ## Dataset & Evaluation
136
+
137
+ - Dataset
138
+ 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEDataset" target="_blank">ApolloMoEDataset</a>
139
+
140
+ <details><summary>Click to expand</summary>
141
+
142
+ ![ApolloMoE](assets/Dataset.png)
143
+
144
+ - [Data category](https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus/tree/main/train)
145
+
146
+
147
+ </details>
148
+
149
+ - Evaluation
150
+ 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEBench" target="_blank">ApolloMoEBench</a>
151
+
152
+ <details><summary>Click to expand</summary>
153
+
154
+ - EN:
155
+ - [MedQA-USMLE](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options)
156
+ - [MedMCQA](https://huggingface.co/datasets/medmcqa/viewer/default/test)
157
+ - [PubMedQA](https://huggingface.co/datasets/pubmed_qa): Because the results fluctuated too much, they were not used in the paper.
158
+ - [MMLU-Medical](https://huggingface.co/datasets/cais/mmlu)
159
+ - Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
160
+ - ZH:
161
+ - [MedQA-MCMLE](https://huggingface.co/datasets/bigbio/med_qa/viewer/med_qa_zh_4options_bigbio_qa/test)
162
+ - [CMB-single](https://huggingface.co/datasets/FreedomIntelligence/CMB): Not used in the paper
163
+ - Randomly sample 2,000 multiple-choice questions with single answer.
164
+ - [CMMLU-Medical](https://huggingface.co/datasets/haonan-li/cmmlu)
165
+ - Anatomy, Clinical_knowledge, College_medicine, Genetics, Nutrition, Traditional_chinese_medicine, Virology
166
+ - [CExam](https://github.com/williamliujl/CMExam): Not used in the paper
167
+ - Randomly sample 2,000 multiple-choice questions
168
+
169
+
170
+ - ES: [Head_qa](https://huggingface.co/datasets/head_qa)
171
+ - FR:
172
+ - [Frenchmedmcqa](https://github.com/qanastek/FrenchMedMCQA)
173
+ - [MMLU_FR]
174
+ - Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
175
+ - HI: [MMLU_HI](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Hindi)
176
+ - Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
177
+ - AR: [MMLU_AR](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Arabic)
178
+ - Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
179
+ - JA: [IgakuQA](https://github.com/jungokasai/IgakuQA)
180
+ - KO: [KorMedMCQA](https://huggingface.co/datasets/sean0042/KorMedMCQA)
181
+ - IT:
182
+ - [MedExpQA](https://huggingface.co/datasets/HiTZ/MedExpQA)
183
+ - [MMLU_IT]
184
+ - Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
185
+ - DE: [BioInstructQA](https://huggingface.co/datasets/BioMistral/BioInstructQA): German part
186
+ - PT: [BioInstructQA](https://huggingface.co/datasets/BioMistral/BioInstructQA): Portuguese part
187
+ - RU: [RuMedBench](https://github.com/sb-ai-lab/MedBench)
188
+
189
+
190
+
191
+
192
+ </details>
193
+
194
+
195
+ ## Results reproduction
196
+ <details><summary>Click to expand</summary>
197
+
198
+
199
+ We take Apollo2-7B or Apollo-MoE-0.5B as example
200
+ 1. Download Dataset for project:
201
+
202
+ ```
203
+ bash 0.download_data.sh 
204
+ ```
205
+
206
+ 2. Prepare test and dev data for specific model:
207
+
208
+
209
+ - Create test data for with special token
210
+
211
+ ```
212
+ bash 1.data_process_test&dev.sh
213
+ ```
214
+
215
+ 3. Prepare train data for specific model (Create tokenized data in advance):
216
+
217
+
218
+ - You can adjust data Training order and Training Epoch in this step
219
+
220
+ ```
221
+ bash 2.data_process_train.sh
222
+ ```
223
+
224
+ 4. Train the model
225
+
226
+
227
+ - If you want to train in Multi Nodes please refer to ./src/sft/training_config/zero_multi.yaml
228
+
229
+
230
+ ```
231
+ bash 3.single_node_train.sh
232
+ ```
233
+
234
+
235
+ 5. Evaluate your model: Generate score for benchmark
236
+
237
+ ```
238
+ bash 4.eval.sh
239
+ ```
240
+
241
+ </details>
242
+
243
+
244
+
245
+ ## Citation
246
+ Please use the following citation if you intend to use our dataset for training or evaluation:
247
+
248
+ ```
249
+ @misc{zheng2024efficientlydemocratizingmedicalllms,
250
+ title={Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts},
251
+ author={Guorui Zheng and Xidong Wang and Juhao Liang and Nuo Chen and Yuping Zheng and Benyou Wang},
252
+ year={2024},
253
+ eprint={2410.10626},
254
+ archivePrefix={arXiv},
255
+ primaryClass={cs.CL},
256
+ url={https://arxiv.org/abs/2410.10626},
257
+ }
258
+ ```
259
+
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+ {
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+ "architectures": [
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+ "Qwen2ForCausalLM"
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