README.md

metadata

tags:
  - text-generation
license: cc-by-nc-4.0
language:
  - ko
base_model: beomi/OPEN-SOLAR-KO-10.7B
pipeline_tag: text-generation

DataVortexS-10.7B-dpo-v1.9

Model Details

Base Model

beomi/OPEN-SOLAR-KO-10.7B

Trained On

• OS: Ubuntu 22.04
• GPU: H100 80GB 4ea
• transformers: v4.36.2

Instruction format

It follows Alpaca (Chat) format.

E.g.

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

### User:
대한민국의 수도는 어디야?

### Assistant:
대한민국의 수도는 서울입니다.

### User:
서울 인구는 총 몇 명이야?
"""

Model Benchmark

Ko LM Eval Harness

On Benchmarking ...

Task	0-shot	5-shot	10-shot	50-shot
kobest_boolq	0.0	0.0	0.0	0.0
kobest_copa	0.0	0.0	0.0	0.0
kobest_hellaswag	0.0	0.0	0.0	0.0
kobest_sentineg	0.0	0.0	0.0	0.0
Average	0.0	0.0	0.0	0.0

Ko-LLM-Leaderboard

On Benchmarking ...

Average	Ko-ARC	Ko-HellaSwag	Ko-MMLU	Ko-TruthfulQA	Ko-CommonGen V2
0	0	0	0	0	0

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-dpo-v1.9")
tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexS-10.7B-dpo-v1.9")

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

This model is licensed under the cc-by-nc-4.0. which allows others to share and adapt the model for non-commercial purposes.