Hugging Face's logo

davidkim205
/
Hunminai-1.0-12b

Text Generation
Transformers
Safetensors
Korean
gemma3
image-text-to-text
conversational
text-generation-inference
Model card Files Community
3

Update README.md

#2
by Doleeee - opened
base: refs/heads/main
←
from: refs/pr/2
Discussion Files changed
+22
-33
Files changed (1) hide show
  1. README.md +22 -33
README.md CHANGED
@@ -81,39 +81,28 @@ The model was trained on approximately 100k high-quality Korean instruction exam
81
 
82
  The table below contains a description of the Korean LLM evaluation benchmark dataset used for the model evaluation. More information on the benchmarks is available at [Blog](https://davidkim205.github.io/).
83
 
84
- | Benchmark | Description | Abbreviation |
85
- |------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------|
86
- | [ko-bench](https://huggingface.co/datasets/davidkim205/ko-bench) | Korean-translated dataset of [MT-Bench](https://github.com/lm-sys/FastChat/blob/main/fastchat/llm_judge/data/mt_bench/question.jsonl) questions | bench |
87
- | [ko-bench-v2](https://huggingface.co/datasets/davidkim205/ko-bench-v2) | Dataset including new questions and answers following the ko-bench format | bench2 |
88
- | [ko-ged](https://huggingface.co/datasets/davidkim205/ko-ged) | Korean GED (elementary, middle, high school) open-ended question dataset<br/>Subjects: Korean, English, Mathematics, Science, Social Studies | ged |
89
- | [ko-ged2](https://huggingface.co/datasets/davidkim205/ko-ged2) | Korean GED open-ended question dataset for the 2025 1st Korean GED Exam, covering all subjects | ged2 |
90
- | [tiny-eval](https://huggingface.co/datasets/davidkim205/tiny-eval) | High-quality evaluation dataset designed to assess overall model performance with a small amount of data | tiny |
91
- | [ko-ifeval](https://huggingface.co/datasets/davidkim205/ko-ifeval) | Instruction-following evaluation dataset translated from [IFEval](https://github.com/google-research/google-research/tree/master/instruction_following_eval), adapted for Korean language and culture | ifeval |
92
- | [ko-ged-elementary](https://huggingface.co/datasets/davidkim205/ko-ged-elementary) | Korean elementary school GED multiple-choice question dataset | ged\:E |
93
- | [ko-ged-middle](https://huggingface.co/datasets/davidkim205/ko-ged-middle) | Korean middle school GED multiple-choice question dataset | ged\:M |
94
- | [ko-ged-high](https://huggingface.co/datasets/davidkim205/ko-ged-high) | Korean high school GED multiple-choice question dataset | ged\:H |
95
- | [ko-ged2-elementary](https://huggingface.co/datasets/davidkim205/ko-ged2-middle) | Korean elementary school GED multiple-choice dataset, updated for the 2025 GED Exam | ged2\:E |
96
- | [ko-ged2-middle](https://huggingface.co/datasets/davidkim205/ko-ged2-elementary) | Korean middle school GED multiple-choice dataset, updated for the 2025 GED Exam | ged2\:M |
97
- | [ko-ged2-high](https://huggingface.co/datasets/davidkim205/ko-ged2-high) | Korean high school GED multiple-choice dataset, updated for the 2025 GED Exam | ged2\:H |
98
- | [ko-gpqa](https://huggingface.co/datasets/davidkim205/ko-gpqa) | Korean version of GPQA containing challenging physics questions designed to test deep understanding and logical reasoning | gpqa |
99
- | [ko-math-500](https://huggingface.co/datasets/davidkim205/ko-math-500) | Korean-translated subset of 500 high school-level math problems from the MATH dataset, including detailed solutions with LaTeX notation | math500 |
100
 
101
  ### Benchmark Results
102
 
103
- | | **davidkim205<br>ko-gemma<br>-3-12b** | google<br>gemma-3<br>-12b-it | unsloth<br>gemma-3<br>-12b-it | K-intelligence<br>Midm-2.0<br>-Base-Instruct | LGAI-EXAONE<br>EXAONE-3.5<br>-7.8B-Instruct |
104
- |---------|---------------------------------------:|-----------------------------:|-------------------------------:|----------------------------------------------:|---------------------------------------------:|
105
- | Avg. | **8.26** | 8.22 | 8.20 | 8.12 | 7.85 |
106
- | bench | 7.96 | 8.00 | 7.83 | **8.01** | 7.70 |
107
- | bench2 | 8.39 | 8.23 | **8.44** | 8.21 | 8.01 |
108
- | ged | 8.65 | 8.61 | **8.73** | 8.10 | 8.25 |
109
- | ged2 | 8.17 | 8.17 | 8.31 | **8.84** | 8.06 |
110
- | tiny | **8.33** | **8.33** | 7.88 | 8.25 | 8.12 |
111
- | ifeval | **8.37** | 8.30 | 8.33 | 8.24 | 6.76 |
112
- | ged:E | **9.72** | **9.72** | 9.51 | **9.72** | 9.65 |
113
- | ged:M | **9.63** | 9.55 | 9.39 | 9.31 | 9.10 |
114
- | ged:H | 9.32 | 9.36 | 9.24 | **9.48** | 9.00 |
115
- | ged2:E | 9.60 | 9.60 | **9.66** | 9.60 | 9.48 |
116
- | ged2:M | 9.37 | **9.54** | **9.54** | 9.16 | 8.95 |
117
- | ged2:H | **9.32** | 9.24 | 9.24 | 9.28 | 8.84 |
118
- | gpqa | **3.18** | 2.88 | 2.98 | 2.68 | 3.13 |
119
- | math500 | 5.60 | 5.58 | **5.70** | 4.80 | 4.88 |
 
81
 
82
  The table below contains a description of the Korean LLM evaluation benchmark dataset used for the model evaluation. More information on the benchmarks is available at [Blog](https://davidkim205.github.io/).
83
 
84
+ | Benchmark | Description | Abbreviation |
85
+ |------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------|
86
+ | [ko-bench](https://huggingface.co/datasets/davidkim205/ko-bench) | Korean-translated dataset of [MT-Bench](https://github.com/lm-sys/FastChat/blob/main/fastchat/llm_judge/data/mt_bench/question.jsonl) questions | bench |
87
+ | [ko-ged](https://huggingface.co/datasets/davidkim205/ko-ged) | Korean GED (elementary, middle, high school) open-ended question dataset<br/>Subjects: Korean, English, Mathematics, Science, Social Studies | ged |
88
+ | [ko-ifeval](https://huggingface.co/datasets/davidkim205/ko-ifeval) | Instruction-following evaluation dataset translated from [IFEval](https://github.com/google-research/google-research/tree/master/instruction_following_eval), adapted for Korean language and culture | ifeval |
89
+ | [ko-ged-mc-elementary](https://huggingface.co/datasets/davidkim205/ko-ged-mc-elementary) | Korean elementary school GED multiple-choice question dataset | ged\:E |
90
+ | [ko-ged-mc-middle](https://huggingface.co/datasets/davidkim205/ko-ged-mc-middle) | Korean middle school GED multiple-choice question dataset | ged\:M |
91
+ | [ko-ged-mc-high](https://huggingface.co/datasets/davidkim205/ko-ged-mc-high) | Korean high school GED multiple-choice question dataset | ged\:H |
92
+ | [ko-gpqa](https://huggingface.co/datasets/davidkim205/ko-gpqa) | Korean version of GPQA containing challenging physics questions designed to test deep understanding and logical reasoning | gpqa |
93
+ | [ko-math-500](https://huggingface.co/datasets/davidkim205/ko-math-500) | Korean-translated subset of 500 high school-level math problems from the MATH dataset, including detailed solutions with LaTeX notation | math500 |
 
 
 
 
 
 
94
 
95
  ### Benchmark Results
96
 
97
+ | | **davidkim205<br>Hunminai<br>-1.0-12b** | google<br>gemma-3<br>-12b-it | unsloth<br>gemma-3<br>-12b-it | K-intelligence<br>Midm-2.0<br>-Base-Instruct | LGAI-EXAONE<br>EXAONE-3.5<br>-7.8B-Instruct |
98
+ |---------|----------------------------------------:|-----------------------------:|------------------------------:|---------------------------------------------:|--------------------------------------------:|
99
+ | Avg. | **7.80** | 7.75 | 7.71 | 7.54 | 7.31 |
100
+ | bench | 7.96 | 8.00 | 7.83 | **8.01** | 7.70 |
101
+ | ged | 8.65 | 8.61 | **8.73** | 8.10 | 8.25 |
102
+ | ged:E | **9.72** | **9.72** | 9.51 | **9.72** | 9.65 |
103
+ | ged:M | **9.63** | 9.55 | 9.39 | 9.31 | 9.10 |
104
+ | ged:H | 9.32 | 9.36 | 9.24 | **9.48** | 9.00 |
105
+ | gpqa | **3.18** | 2.88 | 2.98 | 2.68 | 3.13 |
106
+ | math500 | 5.60 | 5.58 | **5.70** | 4.80 | 4.88 |
107
+ | ifeval | **8.37** | 8.30 | 8.33 | 8.24 | 6.76 |
108
+