Text Generation
Transformers
PyTorch
English
Korean
llama
text-generation-inference
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@@ -40,22 +40,22 @@ I use A100 GPU 40GB and COLAB, when trianing.
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  | Model Name | Vocabulary Size | Description |
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  | --- | --- | --- |
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- | Original Platypus2 | NaN | Sentencepiece BPE |
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- | **Expanded KO-Platypus-ex** | NaN | Sentencepiece BPE. Added Korean vocab and merges |
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  **Tokenizing "안녕하세요, 오늘은 날씨가 좋네요."**
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  | Model | Tokens |
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  | --- | --- |
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- | Platypus2-7b | `[NaN]` |
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- | KO-Platypus2-7b-ex | `[NaN]` |
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  **Tokenizing "Platypus: Quick, Cheap, and Powerful Refinement of LLMs"**
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  | Model | Tokens |
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  | --- | --- |
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- | Platypus2-7b | `[NaN]` |
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- | KO-Platypus2-7b-ex | `[NaN]` |
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  # **Model Benchmark**
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  | Model Name | Vocabulary Size | Description |
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  | --- | --- | --- |
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+ | Original Platypus2 | 32000 | Sentencepiece BPE |
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+ | **Expanded KO-Platypus-ex** | 46336 | Sentencepiece BPE. Added Korean vocab and merges |
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  **Tokenizing "안녕하세요, 오늘은 날씨가 좋네요."**
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  | Model | Tokens |
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  | --- | --- |
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+ | Platypus2-7b | `['▁', '안', '<0xEB>', '<0x85>', '<0x95>', '하', '세', '요', ',', '▁', '오', '<0xEB>', '<0x8A>', '<0x98>', '은', '▁', '<0xEB>', '<0x82>', '<0xA0>', '씨', '가', '▁', '<0xEC>', '<0xA2>', '<0x8B>', '<0xEB>', '<0x84>', '<0xA4>', '요', '.']` |
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+ | KO-Platypus2-7b-ex | `['▁안녕', '하세요', ',', '▁오늘은', '▁날', '씨가', '▁좋네요', '.']` |
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  **Tokenizing "Platypus: Quick, Cheap, and Powerful Refinement of LLMs"**
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  | Model | Tokens |
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  | --- | --- |
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+ | Platypus2-7b | `['▁Plat', 'yp', 'us', ':', '▁Quick', ',', '▁Che', 'ap', ',', '▁and', '▁Power', 'ful', '▁Re', 'fin', 'ement', '▁of', '▁L', 'LM', 's']` |
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+ | KO-Platypus2-7b-ex | `[▁Plat', 'yp', 'us', ':', '▁Quick', ',', '▁Che', 'ap', ',', '▁and', '▁Power', 'ful', '▁Re', 'fin', 'ement', '▁of', '▁L', 'LM', 's']` |
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  # **Model Benchmark**
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