README.md

metadata

tags:
  - text-generation
license: cc-by-nc-sa-4.0
language:
  - ko
base_model: hyeogi/SOLAR-10.7B-dpo-v0.1
pipeline_tag: text-generation
datasets:
  - nlpai-lab/kullm-v2

DataVortexS-10.7B-v0.1

Model Details

Base Model

hyeogi/SOLAR-10.7B-dpo-v0.1

Trained On

• OS: Ubuntu 20.04
• GPU: H100 80GB 1ea
• transformers: v4.36.2

Dataset

• nlpai-lab/kullm-v2

Instruction format

It follows Alpaca format.

E.g.

text = """\
당신은 사람들이 정보를 찾을 수 있도록 도와주는 인공지능 비서입니다.

### Instruction:
대한민국의 수도는 어디야?

### Response:
대한민국의 수도는 서울입니다.

### Instruction:
서울 인구는 총 몇 명이야?
"""

Model Benchmark

Ko LM Eval Harness

Task	0-shot	5-shot	10-shot	50-shot
kobest_boolq	0.334282	0.642861	0.691496	0.638754
kobest_copa	0.584962	0.564325	0.570654	0.581035
kobest_hellaswag	0.340022	0.339401	0.341917	0.337713
kobest_sentineg	0.328257	0.414905	0.464711	0.888914
Average	0.39688075	0.490373	0.5171945	0.611604

Ko-LLM-Leaderboard

Average	Ko-ARC	Ko-HellaSwag	Ko-MMLU	Ko-TruthfulQA	Ko-CommonGen V2
35.39	28.48	39.79	35.98	44.72	27.63

Implementation Code

This model contains the chat_template instruction format.
You can use the code below.

from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained("Edentns/DataVortexS-10.7B-v0.1")
tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexS-10.7B-v0.1")

messages = [
    {"role": "system", "content": "당신은 사람들이 정보를 찾을 수 있도록 도와주는 인공지능 비서입니다."},
    {"role": "user", "content": "대한민국의 수도는 어디야?"},
    {"role": "assistant", "content": "대한민국의 수도는 서울입니다."},
    {"role": "user", "content": "서울 인구는 총 몇 명이야?"}
]

encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

model_inputs = encodeds.to(device)
model.to(device)

generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])

License

The model is licensed under the cc-by-nc-sa-4.0 license, which allows others to copy, modify, and share the work non-commercially, as long as they give appropriate credit and distribute any derivative works under the same license.