license: other
license_name: yi-license
license_link: LICENSE
language:
- en
- ko
pipeline_tag: text-generation
inference: false
base_model: beomi/Yi-Ko-34B
tags:
- pytorch
- Yi-Ko
- 01-ai
- Yi
library_name: transformers
Yi Ko 34B Instruct
Training Process
- Further trained with Korean corpus.
- SFT
- DPO (Dataset URL)
Model Info
| Context Length | Parameter | Prompt Template | KMMLU(5-shot) |
|---|---|---|---|
| 4k(4096) | 34B | ChatML | 49.03 |
Acknowledgement
The training is supported by Sionic AI.
Original Model Card by beomi
Yi-Ko series models serve as advanced iterations of 01-ai/Yi models, benefiting from an expanded vocabulary and the inclusion of Korean/English corpus in its further pretraining. 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. This repository focuses on the 34B pretrained version, which is tailored to fit the Hugging Face Transformers format. For access to the other models, feel free to consult the index provided below.
Model Details
Model Developers Junbum Lee (Beomi)
Variations Yi-Ko-34B will come in a range of parameter sizes — 6B and 34B — with Ko(Korean+English).
Input Models input text only.
Output Models generate text only.
Model Architecture
Yi-Ko series models are an auto-regressive language model that uses an optimized transformer architecture based on Llama-2*.
*Yi model architecture is based on Llama2, so it can be loaded via LlamaForCausalLM class on HF.
| Model Name | Training Data | Params | Context Length | GQA | Trained Tokens | LR | Train tokens (per batch) |
|---|---|---|---|---|---|---|---|
| Yi-Ko-34B | A mix of Korean + English online data | 34B | 4k | O | 40B+ | 5e-5 | 4M |
Vocab Expansion
| Model Name | Vocabulary Size | Description |
|---|---|---|
| Original Yi-Series | 64000 | Sentencepiece BPE |
| Expanded Yi-Ko Series | 78464 | Sentencepiece BPE. Added Korean vocab and merges |
Tokenizing "안녕하세요, 오늘은 날씨가 좋네요.ㅎㅎ"
| Model | # of tokens | Tokens |
|---|---|---|
| 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>'] |
| Expanded Yi-Ko Series | 10 | ['▁안녕', '하세요', ',', '▁오늘은', '▁날', '씨가', '▁좋네요', '.', 'ㅎ', 'ㅎ'] |
| *Equal Korean vocab with Llama-2-Ko Series |
Tokenizing "Llama 2: Open Foundation and Fine-Tuned Chat Models"
| Model | # of tokens | Tokens |
|---|---|---|
| Original Yi-Series | 21 | ['The', '▁Y', 'i', '▁series', '▁models', '▁are', '▁large', '▁language', '▁models', '▁trained', '▁from', '▁scratch', '▁by', '▁developers', '▁at', '▁', '0', '1', '.', 'AI', '.'] |
| Expanded Yi-Ko Series | 21 | ['▁The', '▁Y', 'i', '▁series', '▁models', '▁are', '▁large', '▁language', '▁models', '▁trained', '▁from', '▁scratch', '▁by', '▁developers', '▁at', '▁', '0', '1', '.', 'AI', '.'] |
| *Equal Korean vocab with Llama-2-Ko Series | *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. |
Model Benchmark
LM Eval Harness - Korean Benchmarks
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| kmmlu_direct | N/A | none | 5 | exact_match | 0.5027 | ± | 0.1019 |
| kobest_boolq | 1 | none | 5 | acc | 0.9202 | ± | 0.0072 |
| none | 5 | f1 | 0.9202 | ± | N/A | ||
| kobest_copa | 1 | none | 5 | acc | 0.8480 | ± | 0.0114 |
| none | 5 | f1 | 0.8479 | ± | N/A | ||
| kobest_hellaswag | 1 | none | 5 | acc | 0.5320 | ± | 0.0223 |
| none | 5 | f1 | 0.5281 | ± | N/A | ||
| none | 5 | acc_norm | 0.6340 | ± | 0.0216 | ||
| kobest_sentineg | 1 | none | 5 | acc | 0.9874 | ± | 0.0056 |
| none | 5 | f1 | 0.9874 | ± | N/A | ||
| haerae | N/A | none | 5 | acc | 0.7965 | ± | 0.0116 |
| none | 5 | acc_norm | 0.7965 | ± | 0.0116 | ||
| - haerae_general_knowledge | 1 | none | 5 | acc | 0.5114 | ± | 0.0378 |
| none | 5 | acc_norm | 0.5114 | ± | 0.0378 | ||
| - haerae_history | 1 | none | 5 | acc | 0.8511 | ± | 0.0260 |
| none | 5 | acc_norm | 0.8511 | ± | 0.0260 | ||
| - haerae_loan_word | 1 | none | 5 | acc | 0.8402 | ± | 0.0283 |
| none | 5 | acc_norm | 0.8402 | ± | 0.0283 | ||
| - haerae_rare_word | 1 | none | 5 | acc | 0.8642 | ± | 0.0170 |
| none | 5 | acc_norm | 0.8642 | ± | 0.0170 | ||
| - haerae_standard_nomenclature | 1 | none | 5 | acc | 0.8301 | ± | 0.0305 |
| none | 5 | acc_norm | 0.8301 | ± | 0.0305 |
LICENSE
Follows Yi License
Citation
Acknowledgement
The training is supported by TPU Research Cloud program.