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Yi-Ko-6B/README.md
ModelHub XC b89985b5c5 初始化项目，由ModelHub XC社区提供模型 
Model: beomi/Yi-Ko-6B
Source: Original Platform
2026-07-04 02:44:17 +08:00

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---
language:
- en
- ko
library_name: transformers
tags:
- pytorch
- Yi-Ko
- 01-ai
- Yi
extra_gated_heading: Access beomi/Yi-Ko-6B on Hugging Face
extra_gated_button_content: Submit
extra_gated_fields:
I agree to share my name, email address and username: checkbox
? 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
pipeline_tag: text-generation
inference: false
model-index:
- name: Yi-Ko-6B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 48.89
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beomi/Yi-Ko-6B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 74.48
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beomi/Yi-Ko-6B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 55.72
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beomi/Yi-Ko-6B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 37.09
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beomi/Yi-Ko-6B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 72.93
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beomi/Yi-Ko-6B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 12.51
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beomi/Yi-Ko-6B
name: Open LLM Leaderboard
license: apache-2.0
---
> Update @ 2024.01.29 New Model [beomi/Yi-Ko-DUS-9B](https://huggingface.co/beomi/Yi-Ko-DUS-9B) Released! 🎉
> Update @ 2023.12.03 Yi-Ko(KoEN)-6B Achieved #1🥇 Pretrained Models at [Open Korean LLM Leaderboard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)! 🎉
> Update @ 2023.12.01 Alpha Release of Yi-Ko(KoEN)-6B model 🎉
# **beomi/Yi-Ko-6B**
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 **6B** 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 series will come in a range of parameter sizes — 6B and 34B variations.
**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*.
<small>*Yi model architecture is based on Llama2, so it can be loaded via `LlamaForCausalLM` class on HF.</small>
|Model Name|Training Data|Params|Context Length|GQA|Trained Tokens|LR|Batch Size(per step)|
|---|---|---|---|---|---|---|---|
|Yi-Ko-6B|*A mix of Korean + English online data*|6B|4k|O|>60B|5e<sup>-5</sup>|2048|
**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']` |
|<small>*Equal Korean vocab with Llama-2-Ko Series</small>||
**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', '.']` |
|<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>|
# **Model Benchmark**
## LM Eval Harness - Korean (polyglot branch)
| beomi/Yi-Ko-6B | 0 | 5 | 10 | 50 |
|:---------------------------------|---------:|---------:|---------:|---------:|
| kobest_boolq (macro_f1) | 0.705806 | 0.79905 | 0.814299 | 0.81704 |
| kobest_copa (macro_f1) | 0.775604 | 0.808899 | 0.816866 | 0.842943 |
| kobest_hellaswag (macro_f1) | 0.500876 | 0.498673 | 0.493507 | 0.492183 |
| kobest_sentineg (macro_f1) | 0.404371 | 0.967254 | 0.982368 | 0.974811 |
| kohatespeech (macro_f1) | 0.353428 | 0.351804 | 0.402423 | 0.503764 |
| kohatespeech_apeach (macro_f1) | 0.337667 | 0.498679 | 0.471962 | 0.608401 |
| kohatespeech_gen_bias (macro_f1) | 0.124535 | 0.484745 | 0.474475 | 0.461714 |
| korunsmile (f1) | 0.382804 | 0.349344 | 0.391383 | 0.432875 |
| nsmc (acc) | 0.55064 | 0.8801 | 0.89866 | 0.9071 |
| pawsx_ko (acc) | 0.5145 | 0.54 | 0.538 | 0.5165 |
## LICENSE
Apache 2.0 (for research)
> For commercial purpose,
> mailto: jun@beomi.net to acquire Yi-Ko sereis commercial license.
## Citation
Please use this bibtex below:
```
@misc {lee_junbum_2024,
author = { {Lee Junbum} },
title = { Yi-Ko-6B (Revision 205083a) },
year = 2024,
url = { https://huggingface.co/beomi/Yi-Ko-6B },
doi = { 10.57967/hf/1708 },
publisher = { Hugging Face }
}
```
## Acknowledgement
The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_beomi__Yi-Ko-6B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |50.27|
|AI2 Reasoning Challenge (25-Shot)|48.89|
|HellaSwag (10-Shot) |74.48|
|MMLU (5-Shot) |55.72|
|TruthfulQA (0-shot) |37.09|
|Winogrande (5-shot) |72.93|
|GSM8k (5-shot) |12.51|
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