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kb_v4.1_solar

Text Generation
Transformers
Safetensors
PyTorch
Korean
English
llama
solar
mistral
solar-ko
text-generation-inference
Model card Files Community
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  3. config.json +29 -0
  4. generation_config.json +7 -0
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  14. special_tokens_map.json +23 -0
  15. tokenizer.json +0 -0
  16. tokenizer_config.json +3961 -0
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README.md ADDED
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+ ---
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+ language:
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+ - ko
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+ - en
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+ pipeline_tag: text-generation
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+ inference: false
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+ tags:
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+ - solar
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+ - mistral
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+ - pytorch
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+ - solar-ko
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+ library_name: transformers
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+ license: apache-2.0
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+ ---
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+
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+ **Update Log**
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+
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+ - 2024.01.08: Initial Test version Release of Solar-Ko
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+
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+ # **Open-Solar-Ko** ⭐🇰🇷
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+
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+ 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.
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+
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+ 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/).
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+
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+ 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.
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+
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+ ## Model Details
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+
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+ **Model Developers:** Junbum Lee (Beomi)
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+
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+ **Variations:** Solar-Ko is available with one parameter sizes — 10B with Continual Pretrained version.
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+
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+ **Input:** The model accepts only text input.
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+
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+ **Output:** The model produces text output exclusively.
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+
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+ **Model Architecture:**
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+
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+ SOLAR-KO-10.7B is an auto-regressive language model that leverages an optimized transformer architecture derived from Llama-2.
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+
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+ | |Training Data|Parameters|Content Length|GQA|Tokens|Learning Rate|
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+ |---|---|---|---|---|---|---|
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+ |SOLAR-KO-10.7B|*A curated mix of Publicly Accessible Korean Corpora*|10.7B|2k|✘|>15B*|5e<sup>-5</sup>|
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+
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+ **Training Corpus**
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+
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+ The model was trained using selected datasets from AIHub and Modu Corpus. Detailed information about the training datasets is available below:
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+
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+ - AI Hub: [corpus/AI_HUB](./corpus/AI_HUB)
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+ - Only the `Training` segment of the data was used.
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+ - The `Validation` and `Test` segments were deliberately excluded.
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+ - Modu Corpus: [corpus/MODU_CORPUS](./corpus/MODU_CORPUS)
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+
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+ The final JSONL dataset used to train this model is approximately 61GB in size.
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+
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+ Total token count: Approximately 15 billion tokens (*using the expanded tokenizer. With the original SOLAR tokenizer, >60 billion tokens.)
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+
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+ **Vocab Expansion**
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+
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+ | Model Name | Vocabulary Size | Description |
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+ | --- | --- | --- |
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+ | Original Solar | 32000 | Sentencepiece BPE |
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+ | **Expanded SOLAR-KO-10.7B** | 46592 | Sentencepiece BPE. Added Korean vocab and merges |
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+
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+ **Tokenizing "안녕하세요, 오늘은 날씨가 좋네요."**
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+
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+ - SOLAR-10.7B: 26 tokens
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+ - SOLAR-KO-10.7b: 8 tokens
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+
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+ | Model | Tokens |
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+ | --- | --- |
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+ | SOLAR-10.7B | `['▁', '안', '<0xEB>', '<0x85>', '<0x95>', '하', '세', '요', ',', '▁', '오', '<0xEB>', '<0x8A>', '<0x98>', '은', '▁', '날', '<0xEC>', '<0x94>', '<0xA8>', '가', '▁', '좋', '네', '요', '.']` |
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+ | SOLAR-KO-10.7B | `['▁안녕', '하세요', ',', '▁오늘은', '▁날', '씨가', '▁좋네요', '.']` |
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+
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+ **Tokenizing "Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!"**
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+
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+ - SOLAR-10.7B: 22 tokens
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+ - SOLAR-KO-10.7b: 22 tokens
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+
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+ | Model | Tokens |
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+ | --- | --- |
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+ | SOLAR-10.7B | `['▁Meet', '▁', '1', '0', '.', '7', 'B', '▁Solar', ':', '▁E', 'lev', 'ating', '▁Performance', '▁with', '▁Up', 'stage', '▁Dep', 'th', '▁UP', '▁Scal', 'ing', '!']` |
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+ | SOLAR-KO-10.7B | `['▁Meet', '▁', '1', '0', '.', '7', 'B', '▁Solar', ':', '▁E', 'lev', 'ating', '▁Performance', '▁with', '▁Up', 'stage', '▁Dep', 'th', '▁UP', '▁Scal', 'ing', '!']` |
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+
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+ # LICENSE
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+
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+ Apache 2.0
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+
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+ # **Model Benchmark**
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+
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+ ## LM Eval Harness - Korean (polyglot branch)
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+
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+ - Used EleutherAI's lm-evaluation-harness https://github.com/EleutherAI/lm-evaluation-harness/tree/polyglot
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+
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+ | | 0 | 5 | 10 | 50 |
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+ |:---------------------------------|---------:|---------:|---------:|---------:|
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+ | kobest_boolq (macro_f1) | 0.853949 | 0.88098 | 0.898139 | 0.902354 |
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+ | kobest_copa (macro_f1) | 0.804531 | 0.826736 | 0.837656 | 0.860899 |
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+ | kobest_hellaswag (macro_f1) | 0.507174 | 0.500983 | 0.487287 | 0.512182 |
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+ | kobest_sentineg (macro_f1) | 0.3517 | 0.972291 | 0.977321 | 0.984884 |
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+ | kohatespeech (macro_f1) | 0.258111 | 0.403957 | 0.386808 | 0.462393 |
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+ | kohatespeech_apeach (macro_f1) | 0.337667 | 0.651697 | 0.705337 | 0.827757 |
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+ | kohatespeech_gen_bias (macro_f1) | 0.124535 | 0.503464 | 0.498501 | 0.443218 |
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+ | korunsmile (f1) | 0.3814 | 0.356939 | 0.369989 | 0.296193 |
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+ | nsmc (acc) | 0.5356 | 0.87162 | 0.88654 | 0.89632 |
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+ | pawsx_ko (acc) | 0.5435 | 0.5245 | 0.5315 | 0.5385 |
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+
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+ ## Citation
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+
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+ ```
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+ @misc {solar_ko_junbum_2023,
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+ author = { {L. Junbum} },
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+ title = { Solar-Ko-10.7b },
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+ year = 2024,
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+ url = { https://huggingface.co/beomi/SOLAR-KO-10.7B },
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+ publisher = { Hugging Face }
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+ }
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+
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+ ```
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+
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+ ## Acknowledgements
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+
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+ - Training support was provided by the [TPU Research Cloud](https://sites.research.google/trc/) program.
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+ - 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/).
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+ "transformers_version": "4.36.2",
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+ "use_cache": true,
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+ "vocab_size": 46592
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+ }
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247
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249
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252
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254
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255
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256
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257
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258
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260
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261
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262
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263
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264
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265
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266
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267
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268
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269
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270
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271
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272
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273
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276
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277
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278
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280
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281
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284
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285
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286
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287
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288
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292
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293
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294
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303
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305
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312
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316
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320
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