File size: 3,130 Bytes
25b7d16 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
---
license: apache-2.0
tags:
- generated_from_trainer
base_model: yanolja/EEVE-Korean-2.8B-v1.0
---
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<p align="left">
<img src="https://huggingface.co/yanolja/EEVE-Korean-Instruct-2.8B-v1.0/resolve/main/eeve_logo.webp" width="50%"/>
<p>
# "We must sleep, but AI Never Sleeps!"
## 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:
```
## Simple-Usage
```python
from transformers import AutoTokenizer
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("yanolja/EEVE-Korean-Instruct-2.8B-v1.0", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("yanolja/EEVE-Korean-Instruct-2.8B-v1.0", trust_remote_code=True)
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 = 'λ€μ΄μ΄νΈμ λ©λ΄λ₯Ό μΆμ²ν΄μ£ΌμΈμ.\n\n(A) μλ¬λ\n(B) μΉν¨\n(C) νΌμ\n(D) νμ€ν'
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: λ€μ΄μ΄νΈμ λ©λ΄λ₯Ό μΆμ²ν΄μ£ΌμΈμ.
(A) μλ¬λ
(B) μΉν¨
(C) νΌμ
(D) νμ€ν
Assistant:
(A) μλ¬λλ₯Ό μΆμ²λ립λλ€. μλ¬λλ μ μΉΌλ‘리μ΄λ©΄μλ μμμκ° νλΆν΄ λ€μ΄μ΄νΈμμΌλ‘ μ ν©ν©λλ€. λ€μν μ±μμ λ¨λ°±μ§μ μΆκ°νμ¬ κ· ν μ‘ν μμ¬λ₯Ό λ§λμ€ μ μμ΅λλ€.
```
## About the Model
First of all, Overwhelming gratitude to 'yanolja/EEVE' Model & Team!
This model is a fine-tuned version of [crimsonjoo/Neversleep-3B-v0.1](https://huggingface.co/crimsonjoo/Neversleep-3B-v0.1), which is a Korean vocabulary-extended version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2). Specifically, we utilized Direct Preference Optimization (DPO) through the use of [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl).
For more details, please refer to our technical report: [Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models](https://arxiv.org/abs/2402.14714).
## Training Data
- Korean-translated version of [Open-Orca/SlimOrca-Dedup](https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup)
- Korean-translated version of [argilla/ultrafeedback-binarized-preferences-cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned)
- No other dataset was used |