---
license: cc-by-nc-4.0
base_model: EleutherAI/polyglot-ko-12.8b
tags:
- generated_from_trainer
model-index:
- name: gollm-12.8b-instruct-v2.3
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# gollm-12.8b-instruct-v2.3

This model is a fine-tuned version of [EleutherAI/polyglot-ko-12.8b](https://huggingface.co/EleutherAI/polyglot-ko-12.8b) on a custom mixed dataset

## Model description

- No-context template

```
아래는 작업을 설명하는 질문어와 추가 컨텍스트를 제공하는 맥락이 함께 제공됩니다. 요청을 적절히 완료하는 답변을 작성하세요.

### 질문:
{instruction}

### 답변:

```

- With context template

```
아래는 작업을 설명하는 질문어와 추가 컨텍스트를 제공하는 맥락이 함께 제공됩니다. 요청을 적절히 완료하는 답변을 작성하세요.

### 맥락:
{input}

### 질문:
{instruction}

### 답변:

```

## Intended uses & limitations

More information needed

## Training and evaluation data

- self-introduction (20 samples)
- High-quality reasoning dataset from private documents, QAs generated by Claude AI (1.3k samples)
- EverythingLM-v2 (0.9k samples)
- KoCoT (2k samples)
- Private MRC dataset - answer generated by GPT-4 (32k samples)
Original data have ~12k question-answer pairs with context, and augmentation is applied to make 20k samples with triplet contexts case (1 correct context out of 3)

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- saved_checkpoint_at_epoch: 1 (condition: loss < 0.3)


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3