README.md

---
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
datasets:
- maywell/ko_Ultrafeedback_binarized
base model:
- yanolja/EEVE-Korean-Instruct-10.8B-v1.0
---

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f22e4076fedc4fd11e978f/MoTedec_ZL8GM2MmGyAPs.png)




# T3Q-LLM-sft1.0-dpo1.0

## This model is a version of T3Q-LLM/T3Q-LLM-solar10.8-sft-v1.0 that has been fine-tuned with DPO.

## Model Developers Chihoon Lee(chihoonlee10), T3Q

## Prompt Template
```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
Human: {prompt}
Assistant:
```
## How to Use it
```python
from transformers import AutoTokenizer
from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0")
tokenizer = AutoTokenizer.from_pretrained("T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0")

prompt_template = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\nHuman: {prompt}\nAssistant:\n"
text = '한국의 수도는 어디인가요? 아래 선택지 중 골라주세요.\n\n(A) 경성\n(B) 부산\n(C) 평양\n(D) 서울\n(E) 전주'
model_inputs = tokenizer(prompt_template.format(prompt=text), return_tensors='pt')

outputs = model.generate(**model_inputs, max_new_tokens=256)
output_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
print(output_text)
```

### Example Output
```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
Human: 한국의 수도는 어디인가요? 아래 선택지 중 골라주세요.

(A) 경성
(B) 부산
(C) 평양
(D) 서울
(E) 전주
Assistant:
(D) 서울이 한국의 수도입니다. 서울은 나라의 북동부에 위치해 있으며, 정치, 경제, 문화의 중심지입니다. 약 1,000만 명이 넘는 인구를 가진 세계에서 가장 큰 도시 중 하나입니다. 서울은 높은 빌딩, 현대적인 인프라, 활기 문화 장면으로 유명합니다. 또한, 많은 역사적 명소와 박물관이 있어 방문객들에게 풍부한 문화 체험을 제공합니다.
```


|      Task      |Version| Metric |Value |   |Stderr|
|----------------|------:|--------|-----:|---|-----:|
|kobest_boolq    |      0|acc     |0.9387|±  |0.0064|
|                |       |macro_f1|0.9387|±  |0.0064|
|kobest_copa     |      0|acc     |0.7590|±  |0.0135|
|                |       |macro_f1|0.7585|±  |0.0135|
|kobest_hellaswag|      0|acc     |0.5080|±  |0.0224|
|                |       |acc_norm|0.5580|±  |0.0222|
|                |       |macro_f1|0.5049|±  |0.0224|
|kobest_sentineg |      0|acc     |0.8489|±  |0.0180|
|                |       |macro_f1|0.8483|±  |0.0180|

hf-causal-experimental (pretrained=nlpai-lab/KULLM3,use_accelerate=true,trust_remote_code=true), limit: None, provide_description: False, num_fewshot: 0, batch_size: 8
|      Task      |Version| Metric |Value |   |Stderr|
|----------------|------:|--------|-----:|---|-----:|
|kobest_boolq    |      0|acc     |0.8896|±  |0.0084|
|                |       |macro_f1|0.8888|±  |0.0084|
|kobest_copa     |      0|acc     |0.6930|±  |0.0146|
|                |       |macro_f1|0.6925|±  |0.0147|
|kobest_hellaswag|      0|acc     |0.4640|±  |0.0223|
|                |       |acc_norm|0.5240|±  |0.0224|
|                |       |macro_f1|0.4612|±  |0.0223|
|kobest_sentineg |      0|acc     |0.6297|±  |0.0243|
|                |       |macro_f1|0.6255|±  |0.0244|