DataVortex Models
Collection
21 items
β’
Updated
Research & Engineering | Product Management |
---|---|
Kwangseok Yang | Seunghyun Choi |
Jeongwon Choi | Hyoseok Choi |
It follows Alpaca format.
E.g.
text = """\
### System:
λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€.
### User:
λνλ―Όκ΅μ μλλ μ΄λμΌ?
### Assistant:
λνλ―Όκ΅μ μλλ μμΈμ
λλ€.
### User:
μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?
"""
Task | 0-shot | 5-shot | 10-shot | 50-shot |
---|---|---|---|---|
kobest_boolq | 0.334282 | 0.334282 | 0.334282 | 0.769923 |
kobest_copa | 0.480501 | 0.475746 | 0.46338 | 0.475528 |
kobest_hellaswag | 0.225818 | 0.240596 | 0.234316 | 0.449779 |
kobest_sentineg | 0.33165 | 0.386189 | 0.366913 | 0.360296 |
Average | 0.34306275 | 0.35920325 | 0.34972275 | 0.5138815 |
Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
---|---|---|---|---|---|
40.75 | 49.06 | 25.66 | 53.63 | 45.76 | 29.63 |
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-v1.0")
tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexS-10.7B-v1.0")
messages = [
{"role": "system", "content": "λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€."},
{"role": "user", "content": "λνλ―Όκ΅μ μλλ μ΄λμΌ?"},
{"role": "assistant", "content": "λνλ―Όκ΅μ μλλ μμΈμ
λλ€."},
{"role": "user", "content": "μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?"}
]
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])
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.