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--- |
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extra_gated_heading: Access beomi/Yi-Ko-34B on Hugging Face |
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extra_gated_button_content: Submit |
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extra_gated_fields: |
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I agree to share my name, email address and username: checkbox |
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I confirm that I understand this project is for research purposes only, and confirm that I agree to follow the LICENSE of this model: checkbox |
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language: |
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- en |
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- ko |
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pipeline_tag: text-generation |
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inference: false |
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tags: |
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- pytorch |
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- Yi-Ko |
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- 01-ai |
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- Yi |
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library_name: transformers |
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license: apache-2.0 |
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--- |
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**Update** |
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@2024.07.08: Update LICENSE into Apache 2.0!🎉 |
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# **beomi/Yi-Ko-34B** |
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Yi-Ko series models serve as advanced iterations of 01-ai/Yi models, |
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benefiting from an expanded vocabulary and the inclusion of Korean/English corpus in its further pretraining. |
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Just like its predecessor, Yi-Ko series models operate within the broad range of generative text models that stretch from 6 billion to 34 billion parameters. |
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This repository focuses on the **34B** pretrained version, |
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which is tailored to fit the Hugging Face Transformers format. |
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For access to the other models, feel free to consult the index provided below. |
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## Model Details |
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**Model Developers** Junbum Lee (Beomi) |
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**Variations** Yi-Ko-34B will come in a range of parameter sizes — 6B and 34B — with Ko(Korean+English). |
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**Input** Models input text only. |
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**Output** Models generate text only. |
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**Model Architecture** |
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Yi-Ko series models are an auto-regressive language model that uses an optimized transformer architecture based on Llama-2*. |
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<small>*Yi model architecture is based on Llama2, so it can be loaded via `LlamaForCausalLM` class on HF.</small> |
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|Model Name|Training Data|Params|Context Length|GQA|Trained Tokens|LR|Train tokens (per batch)| |
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|---|---|---|---|---|---|---|---| |
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|Yi-Ko-34B|*A mix of Korean + English online data*|34B|4k|O|40B+|5e<sup>-5</sup>|4M| |
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**Vocab Expansion** |
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| Model Name | Vocabulary Size | Description | |
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| --- | --- | --- | |
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| Original Yi-Series | 64000 | Sentencepiece BPE | |
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| **Expanded Yi-Ko Series** | 78464 | Sentencepiece BPE. Added Korean vocab and merges | |
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**Tokenizing "안녕하세요, 오늘은 날씨가 좋네요.ㅎㅎ"** |
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| Model | # of tokens | Tokens | |
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| Original Yi-Series | 47 | `['<0xEC>', '<0x95>', '<0x88>', '<0xEB>', '<0x85>', '<0x95>', '하', '<0xEC>', '<0x84>', '<0xB8>', '<0xEC>', '<0x9A>', '<0x94>', ',', '▁', '<0xEC>', '<0x98>', '<0xA4>', '<0xEB>', '<0x8A>', '<0x98>', '은', '▁', '<0xEB>', '<0x82>', '<0xA0>', '<0xEC>', '<0x94>', '<0xA8>', '가', '▁', '<0xEC>', '<0xA2>', '<0x8B>', '<0xEB>', '<0x84>', '<0xA4>', '<0xEC>', '<0x9A>', '<0x94>', '.', '<0xE3>', '<0x85>', '<0x8E>', '<0xE3>', '<0x85>', '<0x8E>']` | |
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| **Expanded Yi-Ko Series** | 10 | `['▁안녕', '하세요', ',', '▁오늘은', '▁날', '씨가', '▁좋네요', '.', 'ㅎ', 'ㅎ']` | |
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|<small>*Equal Korean vocab with Llama-2-Ko Series</small>|| |
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**Tokenizing "Llama 2: Open Foundation and Fine-Tuned Chat Models"** |
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| Model | # of tokens | Tokens | |
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| Original Yi-Series | 21 | `['The', '▁Y', 'i', '▁series', '▁models', '▁are', '▁large', '▁language', '▁models', '▁trained', '▁from', '▁scratch', '▁by', '▁developers', '▁at', '▁', '0', '1', '.', 'AI', '.']` | |
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| **Expanded Yi-Ko Series** | 21 | `['▁The', '▁Y', 'i', '▁series', '▁models', '▁are', '▁large', '▁language', '▁models', '▁trained', '▁from', '▁scratch', '▁by', '▁developers', '▁at', '▁', '0', '1', '.', 'AI', '.']` | |
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|<small>*Equal Korean vocab with Llama-2-Ko Series</small>| | <small>*Since **Expanded Yi-Ko Series** prepends `_` at the beginning of the text(to ensure same tokenization for Korean sentences), it shows negilible difference for the first token on English tokenization. </small>| |
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# **Model Benchmark** |
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## LM Eval Harness - Korean Benchmarks |
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| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr| |
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|----------------|------:|------|-----:|--------|-----:|---|------| |
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|**kmmlu_direct**|N/A |none | 5|exact_match|**0.5027**|± |0.1019| |
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|kobest_boolq | 1|none | 5|acc |0.9202|± |0.0072| |
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| | |none | 5|f1 |0.9202|± |N/A | |
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|kobest_copa | 1|none | 5|acc |0.8480|± |0.0114| |
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| | |none | 5|f1 |0.8479|± |N/A | |
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|kobest_hellaswag| 1|none | 5|acc |0.5320|± |0.0223| |
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| | |none | 5|f1 |0.5281|± |N/A | |
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| | |none | 5|acc_norm|0.6340|± |0.0216| |
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|kobest_sentineg | 1|none | 5|acc |0.9874|± |0.0056| |
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| | |none | 5|f1 |0.9874|± |N/A | |
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|haerae |N/A |none | 5|acc |0.7965|± |0.0116| |
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| | |none | 5|acc_norm|0.7965|± |0.0116| |
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| - haerae_general_knowledge | 1|none | 5|acc |0.5114|± |0.0378| |
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| | |none | 5|acc_norm|0.5114|± |0.0378| |
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| - haerae_history | 1|none | 5|acc |0.8511|± |0.0260| |
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| | |none | 5|acc_norm|0.8511|± |0.0260| |
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| - haerae_loan_word | 1|none | 5|acc |0.8402|± |0.0283| |
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| | |none | 5|acc_norm|0.8402|± |0.0283| |
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| - haerae_rare_word | 1|none | 5|acc |0.8642|± |0.0170| |
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| | |none | 5|acc_norm|0.8642|± |0.0170| |
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| - haerae_standard_nomenclature| 1|none | 5|acc |0.8301|± |0.0305| |
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| | |none | 5|acc_norm|0.8301|± |0.0305| |
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## LICENSE |
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Apache 2.0 (for research) |
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> For commercial purpose, |
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> mailto: [email protected] to acquire Yi-Ko sereis commercial license. |
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## Acknowledgement |
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The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program. |
