Update README.md

README.md

@@ -10,130 +10,25 @@ tags:
 - llama
 - Open Ko SOLAR
 ---
-### Model Name
+**Model Name**
 - Open_Ko_SOLAR_DPO_Merge_v0.1
 
-### Base Model
+**Base Model**
 - beomi/OPEN-SOLAR-KO-10.7B
 
-### Dataset
+**Training Corpus**
 - maywell/ko_Ultrafeedback_binarized
 
-### Method
-- DPO
-- Adapter Merge
-
---- Below is original Author's explanation
----
-language:
-- ko
-- en
-pipeline_tag: text-generation
-inference: false
-tags:
-- solar
-- mistral
-- pytorch
-- solar-ko
-library_name: transformers
-license: apache-2.0
----
-
 **Update Log**
 
-- 2024.01.08: Initial Test version Release of Solar-Ko
+- 2024.01.25: Initial version Upload
 
-# **Open-Solar-Ko** ⭐🇰🇷
+**LICENSE**
+- Apache 2.0
 
-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|2k|✘|>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
+**Citation**
 
+Original Source : 
 ```
 @misc {solar_ko_junbum_2023,
     author       = { {L. Junbum} },
@@ -145,7 +40,4 @@ Apache 2.0
 
 ```
 
-## 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/).
+**Acknowledgements**