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metadata
tags:
  - text-generation
license: cc-by-nc-sa-4.0
language:
  - ko
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
pipeline_tag: text-generation
datasets:
  - beomi/KoAlpaca-v1.1a
  - jojo0217/korean_rlhf_dataset
  - kyujinpy/OpenOrca-KO
  - nlpai-lab/kullm-v2
widget:
  - text: |
      <|system|>
      You are a chatbot who answers User's questions.
      <|user|>
      대한민국의 수도는 어디야?
      <|assistant|>

DataVortexTL-1.1B-v0.1

DataVortex

Our Team

Research & Engineering Product Management
Kwangseok Yang Seunghyun Choi
Jeongwon Choi Hyoseok Choi

Model Details

Base Model

TinyLlama/TinyLlama-1.1B-Chat-v1.0

Trained On

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

Dataset

Instruction format

It follows TinyLlama format.

E.g.

text = """\
<|system|>
당신은 사람들이 정보를 찾을 수 있도록 도와주는 인공지능 비서입니다.</s>
<|user|>
대한민국의 수도는 어디야?</s>
<|assistant|>
대한민국의 수도는 서울입니다.</s>
<|user|>
서울 인구는 총 몇 명이야?</s>
"""

Model Benchmark

Ko LM Eval Harness

Task 0-shot 5-shot 10-shot 50-shot
kobest_boolq 0.334282 0.516446 0.500478 0.498941
kobest_copa 0.515061 0.504321 0.492927 0.50809
kobest_hellaswag 0.36253 0.357733 0.355873 0.376502
kobest_sentineg 0.481146 0.657411 0.687417 0.635703
Average 0.42325475 0.50897775 0.50917375 0.504809

Ko-LLM-Leaderboard

Average Ko-ARC Ko-HellaSwag Ko-MMLU Ko-TruthfulQA Ko-CommonGen V2
31.5 25.26 33.53 24.56 43.34 30.81

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/DataVortexTL-1.1B-v0.1")
tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexTL-1.1B-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.