From b0fb6e33a6836a3dede7fded9c31c845a9ab1efd Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Wed, 20 May 2026 20:06:12 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: EleutherAI/polyglot-ko-12.8b Source: Original Platform --- .gitattributes | 49 +++ README.md | 200 ++++++++++ config.json | 27 ++ configuration.json | 1 + generation_config.json | 6 + model-00001-of-00028.safetensors | 3 + model-00002-of-00028.safetensors | 3 + model-00003-of-00028.safetensors | 3 + model-00004-of-00028.safetensors | 3 + model-00005-of-00028.safetensors | 3 + model-00006-of-00028.safetensors | 3 + model-00007-of-00028.safetensors | 3 + model-00008-of-00028.safetensors | 3 + model-00009-of-00028.safetensors | 3 + model-00010-of-00028.safetensors | 3 + 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filter=lfs diff=lfs merge=lfs -text + +tokenizer.json filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..8414416 --- /dev/null +++ b/README.md @@ -0,0 +1,200 @@ +--- +language: +- ko +tags: +- pytorch +- causal-lm +license: apache-2.0 + +--- +# Polyglot-Ko-12.8B + +## Model Description +Polyglot-Ko is a series of large-scale Korean autoregressive language models made by the EleutherAI polyglot team. + +| Hyperparameter | Value | +|----------------------|----------------------------------------------------------------------------------------------------------------------------------------| +| \\(n_{parameters}\\) | 12,898,631,680 | +| \\(n_{layers}\\) | 40 | +| \\(d_{model}\\) | 5120 | +| \\(d_{ff}\\) | 20,480 | +| \\(n_{heads}\\) | 40 | +| \\(d_{head}\\) | 128 | +| \\(n_{ctx}\\) | 2,048 | +| \\(n_{vocab}\\) | 30,003 / 30,080 | +| Positional Encoding | [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864) | +| RoPE Dimensions | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) | + +The model consists of 40 transformer layers with a model dimension of 5120, and a feedforward dimension of 20480. The model +dimension is split into 40 heads, each with a dimension of 128. Rotary Position Embedding (RoPE) is applied to 64 +dimensions of each head. The model is trained with a tokenization vocabulary of 30003. + +## Training data + +Polyglot-Ko-12.8B was trained on 863 GB of Korean language data (1.2TB before processing), a large-scale dataset curated by [TUNiB](https://tunib.ai/). The data collection process has abided by South Korean laws. This dataset was collected for the purpose of training Polyglot-Ko models, so it will not be released for public use. + +| Source |Size (GB) | Link | +|-------------------------------------|---------|------------------------------------------| +| Korean blog posts | 682.3 | - | +| Korean news dataset | 87.0 | - | +| Modu corpus | 26.4 |corpus.korean.go.kr | +| Korean patent dataset | 19.0 | - | +| Korean Q & A dataset | 18.1 | - | +| KcBert dataset | 12.7 | github.com/Beomi/KcBERT | +| Korean fiction dataset | 6.1 | - | +| Korean online comments | 4.2 | - | +| Korean wikipedia | 1.4 | ko.wikipedia.org | +| Clova call | < 1.0 | github.com/clovaai/ClovaCall | +| Naver sentiment movie corpus | < 1.0 | github.com/e9t/nsmc | +| Korean hate speech dataset | < 1.0 | - | +| Open subtitles | < 1.0 | opus.nlpl.eu/OpenSubtitles.php | +| AIHub various tasks datasets | < 1.0 |aihub.or.kr | +| Standard Korean language dictionary | < 1.0 | stdict.korean.go.kr/main/main.do | + +Furthermore, in order to avoid the model memorizing and generating personally identifiable information (PII) in the training data, we masked out the following sensitive information in the pre-processing stage: + +* `<|acc|>` : bank account number +* `<|rrn|>` : resident registration number +* `<|tell|>` : phone number + +## Training procedure +Polyglot-Ko-12.8B was trained for 167 billion tokens over 301,000 steps on 256 A100 GPUs with the [GPT-NeoX framework](https://github.com/EleutherAI/gpt-neox). It was trained as an autoregressive language model, using cross-entropy loss to maximize the likelihood of predicting the next token. + +## How to use + +This model can be easily loaded using the `AutoModelForCausalLM` class: + +```python +from transformers import AutoTokenizer, AutoModelForCausalLM + +tokenizer = AutoTokenizer.from_pretrained("EleutherAI/polyglot-ko-12.8b") +model = AutoModelForCausalLM.from_pretrained("EleutherAI/polyglot-ko-12.8b") +``` + +## Evaluation results + +We evaluate Polyglot-Ko-3.8B on [KOBEST dataset](https://arxiv.org/abs/2204.04541), a benchmark with 5 downstream tasks, against comparable models such as skt/ko-gpt-trinity-1.2B-v0.5, kakaobrain/kogpt and facebook/xglm-7.5B, using the prompts provided in the paper. + +The following tables show the results when the number of few-shot examples differ. You can reproduce these results using the [polyglot branch of lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/polyglot) and the following scripts. For a fair comparison, all models were run under the same conditions and using the same prompts. In the tables, `n` refers to the number of few-shot examples. + +In case of WiC dataset, all models show random performance. + +```console +python main.py \ + --model gpt2 \ + --model_args pretrained='EleutherAI/polyglot-ko-3.8b' \ + --tasks kobest_copa,kobest_hellaswag \ + --num_fewshot $YOUR_NUM_FEWSHOT \ + --batch_size $YOUR_BATCH_SIZE \ + --device $YOUR_DEVICE \ + --output_path $/path/to/output/ +``` + +### COPA (F1) + +| Model | params | 0-shot | 5-shot | 10-shot | 50-shot | +|----------------------------------------------------------------------------------------------|--------|--------|--------|---------|---------| +| [skt/ko-gpt-trinity-1.2B-v0.5](https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5) | 1.2B | 0.6696 | 0.6477 | 0.6419 | 0.6514 | +| [kakaobrain/kogpt](https://huggingface.co/kakaobrain/kogpt) | 6.0B | 0.7345 | 0.7287 | 0.7277 | 0.7479 | +| [facebook/xglm-7.5B](https://huggingface.co/facebook/xglm-7.5B) | 7.5B | 0.6723 | 0.6731 | 0.6769 | 0.7119 | +| [EleutherAI/polyglot-ko-1.3b](https://huggingface.co/EleutherAI/polyglot-ko-1.3b) | 1.3B | 0.7196 | 0.7193 | 0.7204 | 0.7206 | +| [EleutherAI/polyglot-ko-3.8b](https://huggingface.co/EleutherAI/polyglot-ko-3.8b) | 3.8B | 0.7595 | 0.7608 | 0.7638 | 0.7788 | +| [EleutherAI/polyglot-ko-5.8b](https://huggingface.co/EleutherAI/polyglot-ko-5.8b) | 5.8B | 0.7745 | 0.7676 | 0.7775 | 0.7887 | +| **[EleutherAI/polyglot-ko-12.8b](https://huggingface.co/EleutherAI/polyglot-ko-12.8b) (this)** | **12.8B** | **0.7937** | **0.8108** | **0.8037** | **0.8369** | + + + +### HellaSwag (F1) + +| Model | params | 0-shot | 5-shot | 10-shot | 50-shot | +|----------------------------------------------------------------------------------------------|--------|--------|--------|---------|---------| +| [skt/ko-gpt-trinity-1.2B-v0.5](https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5) | 1.2B | 0.5243 | 0.5272 | 0.5166 | 0.5352 | +| [kakaobrain/kogpt](https://huggingface.co/kakaobrain/kogpt) | 6.0B | 0.5590 | 0.5833 | 0.5828 | 0.5907 | +| [facebook/xglm-7.5B](https://huggingface.co/facebook/xglm-7.5B) | 7.5B | 0.5665 | 0.5689 | 0.5565 | 0.5622 | +| [EleutherAI/polyglot-ko-1.3b](https://huggingface.co/EleutherAI/polyglot-ko-1.3b) | 1.3B | 0.5247 | 0.5260 | 0.5278 | 0.5427 | +| [EleutherAI/polyglot-ko-3.8b](https://huggingface.co/EleutherAI/polyglot-ko-3.8b) | 3.8B | 0.5707 | 0.5830 | 0.5670 | 0.5787 | +| [EleutherAI/polyglot-ko-5.8b](https://huggingface.co/EleutherAI/polyglot-ko-5.8b) | 5.8B | 0.5976 | 0.5998 | 0.5979 | 0.6208 | +| **[EleutherAI/polyglot-ko-12.8b (this)](https://huggingface.co/EleutherAI/polyglot-ko-12.8b)** | **12.8B** | **0.5954** | **0.6306** | **0.6098** | **0.6118** | + + + +### BoolQ (F1) + +| Model | params | 0-shot | 5-shot | 10-shot | 50-shot | +|----------------------------------------------------------------------------------------------|--------|--------|--------|---------|---------| +| [skt/ko-gpt-trinity-1.2B-v0.5](https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5) | 1.2B | 0.3356 | 0.4014 | 0.3640 | 0.3560 | +| [kakaobrain/kogpt](https://huggingface.co/kakaobrain/kogpt) | 6.0B | 0.4514 | 0.5981 | 0.5499 | 0.5202 | +| [facebook/xglm-7.5B](https://huggingface.co/facebook/xglm-7.5B) | 7.5B | 0.4464 | 0.3324 | 0.3324 | 0.3324 | +| [EleutherAI/polyglot-ko-1.3b](https://huggingface.co/EleutherAI/polyglot-ko-1.3b) | 1.3B | 0.3552 | 0.4751 | 0.4109 | 0.4038 | +| [EleutherAI/polyglot-ko-3.8b](https://huggingface.co/EleutherAI/polyglot-ko-3.8b) | 3.8B | 0.4320 | 0.5263 | 0.4930 | 0.4038 | +| [EleutherAI/polyglot-ko-5.8b](https://huggingface.co/EleutherAI/polyglot-ko-5.8b) | 5.8B | 0.4356 | 0.5698 | 0.5187 | 0.5236 | +| **[EleutherAI/polyglot-ko-12.8b (this)](https://huggingface.co/EleutherAI/polyglot-ko-12.8b)** | **12.8B** | **0.4818** | **0.6041** | **0.6289** | **0.6448** | + + + +### SentiNeg (F1) + +| Model | params | 0-shot | 5-shot | 10-shot | 50-shot | +|----------------------------------------------------------------------------------------------|--------|--------|--------|---------|---------| +| [skt/ko-gpt-trinity-1.2B-v0.5](https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5) | 1.2B | 0.6065 | 0.6878 | 0.7280 | 0.8413 | +| [kakaobrain/kogpt](https://huggingface.co/kakaobrain/kogpt) | 6.0B | 0.3747 | 0.8942 | 0.9294 | 0.9698 | +| [facebook/xglm-7.5B](https://huggingface.co/facebook/xglm-7.5B) | 7.5B | 0.3578 | 0.4471 | 0.3964 | 0.5271 | +| [EleutherAI/polyglot-ko-1.3b](https://huggingface.co/EleutherAI/polyglot-ko-1.3b) | 1.3B | 0.6790 | 0.6257 | 0.5514 | 0.7851 | +| [EleutherAI/polyglot-ko-3.8b](https://huggingface.co/EleutherAI/polyglot-ko-3.8b) | 3.8B | 0.4858 | 0.7950 | 0.7320 | 0.7851 | +| [EleutherAI/polyglot-ko-5.8b](https://huggingface.co/EleutherAI/polyglot-ko-5.8b) | 5.8B | 0.3394 | 0.8841 | 0.8808 | 0.9521 | +| **[EleutherAI/polyglot-ko-12.8b (this)](https://huggingface.co/EleutherAI/polyglot-ko-12.8b)** | **12.8B** | **0.9117** | **0.9015** | **0.9345** | **0.9723** | + + + +### WiC (F1) + +| Model | params | 0-shot | 5-shot | 10-shot | 50-shot | +|----------------------------------------------------------------------------------------------|--------|--------|--------|---------|---------| +| [skt/ko-gpt-trinity-1.2B-v0.5](https://huggingface.co/skt/ko-gpt-trinity-1.2B-v0.5) | 1.2B | 0.3290 | 0.4313 | 0.4001 | 0.3621 | +| [kakaobrain/kogpt](https://huggingface.co/kakaobrain/kogpt) | 6.0B | 0.3526 | 0.4775 | 0.4358 | 0.4061 | +| [facebook/xglm-7.5B](https://huggingface.co/facebook/xglm-7.5B) | 7.5B | 0.3280 | 0.4903 | 0.4945 | 0.3656 | +| [EleutherAI/polyglot-ko-1.3b](https://huggingface.co/EleutherAI/polyglot-ko-1.3b) | 1.3B | 0.3297 | 0.4850 | 0.4650 | 0.3290 | +| [EleutherAI/polyglot-ko-3.8b](https://huggingface.co/EleutherAI/polyglot-ko-3.8b) | 3.8B | 0.3390 | 0.4944 | 0.4203 | 0.3835 | +| [EleutherAI/polyglot-ko-5.8b](https://huggingface.co/EleutherAI/polyglot-ko-5.8b) | 5.8B | 0.3913 | 0.4688 | 0.4189 | 0.3910 | +| **[EleutherAI/polyglot-ko-12.8b](https://huggingface.co/EleutherAI/polyglot-ko-12.8b) (this)** | **12.8B** | **0.3985** | **0.3683** | **0.3307** | **0.3273** | + + + +## Limitations and Biases + +Polyglot-Ko has been trained to optimize next token prediction. Language models such as this are often used for a wide variety of tasks and it is important to be aware of possible unexpected outcomes. For instance, Polyglot-Ko will not always return the most factual or accurate response but the most statistically likely one. In addition, Polyglot may produce socially unacceptable or offensive content. We recommend having a human curator or other filtering mechanism to censor sensitive content. + +## Citation and Related Information +### BibTeX entry +If you find our work useful, please consider citing: +```bibtex +@misc{ko2023technical, + title={A Technical Report for Polyglot-Ko: Open-Source Large-Scale Korean Language Models}, + author={Hyunwoong Ko and Kichang Yang and Minho Ryu and Taekyoon Choi and Seungmu Yang and jiwung Hyun and Sungho Park}, + year={2023}, + eprint={2306.02254}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` + +### Licensing +All our models are licensed under the terms of the Apache License 2.0. + +``` +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +``` + +### Acknowledgement + +This project was made possible thanks to the computing resources from [Stability.ai](https://stability.ai), and thanks to [TUNiB](https://tunib.ai) for providing a large-scale Korean dataset for this work. diff --git a/config.json b/config.json new file mode 100644 index 0000000..ae2c5ab --- /dev/null +++ b/config.json @@ -0,0 +1,27 @@ +{ + "_name_or_path": "./polyglot-ko-12.8b/", + "architectures": [ + "GPTNeoXForCausalLM" + ], + "bos_token_id": 0, + "classifier_dropout": 0.1, + "eos_token_id": 2, + "hidden_act": "gelu", + "hidden_size": 5120, + "initializer_range": 0.02, + "intermediate_size": 20480, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 2048, + "model_type": "gpt_neox", + "num_attention_heads": 40, + "num_hidden_layers": 40, + "num_steps": 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