Update README.md

README.md

@@ -1,10 +1,125 @@
 ---
-license: apache-2.0
-pipeline_tag: text-generation
 language:
 - ko
-library_name: transformers
+- en
+pipeline_tag: text-generation
+inference: false
 tags:
-- sol
+- solar
+- mistral
 - pytorch
----
+- solar-ko
+library_name: transformers
+license: apache-2.0
+---
+
+**Update Log**
+
+- 2024.01.08: Initial Test version Release of Solar-Ko
+
+# **Open-Solar-Ko** ⭐🇰🇷
+
+Solar-Ko represents an advanced iteration of the upstage/SOLAR-10.7B-v1.0 model, featuring an expanded vocabulary and the inclusion of a Korean corpus for enhanced pretraining. 
+
+Open-Solar-Ko exclusively utilizes publicly accessible Korean corpora, including sources such as [AI Hub](https://www.aihub.or.kr), [Modu Corpus, 모두의 말뭉치](https://corpus.korean.go.kr/), and [Korean Wikipedia](https://dumps.wikimedia.org/kowiki/).
+
+As training was conducted solely with publicly available corpora, this model is open for unrestricted use by everyone, adhering to the Apache2.0 open source License.
+
+## Model Details
+
+**Model Developers:** Junbum Lee (Beomi)
+
+**Variations:** Solar-Ko is available with one parameter sizes — 10B with Continual Pretrained version.
+
+**Input:** The model accepts only text input.
+
+**Output:** The model produces text output exclusively.
+
+**Model Architecture:** 
+
+SOLAR-KO-10.7B is an auto-regressive language model that leverages an optimized transformer architecture derived from Llama-2.
+
+| |Training Data|Parameters|Content Length|GQA|Tokens|Learning Rate|
+|---|---|---|---|---|---|---|
+|SOLAR-KO-10.7B|*A curated mix of Publicly Accessible Korean Corpora*|10.7B|4k|O|>15B*|5e<sup>-5</sup>|
+
+**Training Corpus**
+
+The model was trained using selected datasets from AIHub and Modu Corpus. Detailed information about the training datasets is available below:
+
+- AI Hub: [corpus/AI_HUB](./corpus/AI_HUB)
+  - Only the `Training` segment of the data was used.
+  - The `Validation` and `Test` segments were deliberately excluded.
+- Modu Corpus: [corpus/MODU_CORPUS](./corpus/MODU_CORPUS)
+
+The final JSONL dataset used to train this model is approximately 61GB in size.
+
+Total token count: Approximately 15 billion tokens (*using the expanded tokenizer. With the original SOLAR tokenizer, >60 billion tokens.)
+
+**Vocab Expansion**
+
+| Model Name | Vocabulary Size | Description | 
+| --- | --- | --- |
+| Original Solar | 32000 | Sentencepiece BPE |
+| **Expanded SOLAR-KO-10.7B** | 46592 | Sentencepiece BPE. Added Korean vocab and merges |
+
+**Tokenizing "안녕하세요, 오늘은 날씨가 좋네요."**
+
+- SOLAR-10.7B: 26 tokens
+- SOLAR-KO-10.7b: 8 tokens
+
+| Model | Tokens |
+| --- | --- |
+| SOLAR-10.7B | `['▁', '안', '<0xEB>', '<0x85>', '<0x95>', '하', '세', '요', ',', '▁', '오', '<0xEB>', '<0x8A>', '<0x98>', '은', '▁', '날', '<0xEC>', '<0x94>', '<0xA8>', '가', '▁', '좋', '네', '요', '.']` |
+| SOLAR-KO-10.7B | `['▁안녕', '하세요', ',', '▁오늘은', '▁날', '씨가', '▁좋네요', '.']` |
+
+**Tokenizing "Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!"**
+
+- SOLAR-10.7B: 22 tokens
+- SOLAR-KO-10.7b: 22 tokens
+
+| Model | Tokens |
+| --- | --- |
+| SOLAR-10.7B | `['▁Meet', '▁', '1', '0', '.', '7', 'B', '▁Solar', ':', '▁E', 'lev', 'ating', '▁Performance', '▁with', '▁Up', 'stage', '▁Dep', 'th', '▁UP', '▁Scal', 'ing', '!']` |
+| SOLAR-KO-10.7B | `['▁Meet', '▁', '1', '0', '.', '7', 'B', '▁Solar', ':', '▁E', 'lev', 'ating', '▁Performance', '▁with', '▁Up', 'stage', '▁Dep', 'th', '▁UP', '▁Scal', 'ing', '!']` |
+
+# LICENSE
+
+Apache 2.0
+
+# **Model Benchmark**
+
+## LM Eval Harness - Korean (polyglot branch)
+
+- Used EleutherAI's lm-evaluation-harness https://github.com/EleutherAI/lm-evaluation-harness/tree/polyglot
+
+|                                  |        0 |        5 |       10 |       50 |
+|:---------------------------------|---------:|---------:|---------:|---------:|
+| kobest_boolq (macro_f1)          | 0.853949 | 0.88098  | 0.898139 | 0.902354 |
+| kobest_copa (macro_f1)           | 0.804531 | 0.826736 | 0.837656 | 0.860899 |
+| kobest_hellaswag (macro_f1)      | 0.507174 | 0.500983 | 0.487287 | 0.512182 |
+| kobest_sentineg (macro_f1)       | 0.3517   | 0.972291 | 0.977321 | 0.984884 |
+| kohatespeech (macro_f1)          | 0.258111 | 0.403957 | 0.386808 | 0.462393 |
+| kohatespeech_apeach (macro_f1)   | 0.337667 | 0.651697 | 0.705337 | 0.827757 |
+| kohatespeech_gen_bias (macro_f1) | 0.124535 | 0.503464 | 0.498501 | 0.443218 |
+| korunsmile (f1)                  | 0.3814   | 0.356939 | 0.369989 | 0.296193 |
+| nsmc (acc)                       | 0.5356   | 0.87162  | 0.88654  | 0.89632  |
+| pawsx_ko (acc)                   | 0.5435   | 0.5245   | 0.5315   | 0.5385   |
+
+## Citation
+
+```
+@misc {solar_ko_junbum_2023,
+    author       = { {L. Junbum} },
+    title        = { Solar-Ko-10.7b },
+    year         = 2024,
+    url          = { https://huggingface.co/beomi/SOLAR-KO-10.7B },
+    publisher    = { Hugging Face }
+}
+
+```
+
+## Acknowledgements
+
+- Training support was provided by the [TPU Research Cloud](https://sites.research.google/trc/) program.
+- The training corpus includes data from [AI Hub](https://www.aihub.or.kr/), [Modu Corpus](https://corpus.korean.go.kr/), and [Korean Wikipedia](https://dumps.wikimedia.org/kowiki/).