metadata

language:
  - ko
library_name: transformers
pipeline_tag: text-generation
license: cc-by-nc-4.0

Synatra-7B-v0.3-RP🐧

Support Me

시나트라는 개인 프로젝트로, 1인의 자원으로 개발되고 있습니다. 모델이 마음에 드셨다면 약간의 연구비 지원은 어떨까요?

Wanna be a sponser? Contact me on Telegram AlzarTakkarsen

License

This model is strictly non-commercial (cc-by-nc-4.0) use only. The "Model" is completely free (ie. base model, derivates, merges/mixes) to use for non-commercial purposes as long as the the included cc-by-nc-4.0 license in any parent repository, and the non-commercial use statute remains, regardless of other models' licences. The licence can be changed after new model released. If you are to use this model for commercial purpose, Contact me.

Model Details

Base Model
mistralai/Mistral-7B-Instruct-v0.1

Trained On
A6000 48GB * 8

Instruction format

It follows ChatML format.

TODO

~~RP 기반 튜닝 모델 제작~~ ✅
~~데이터셋 정제~~ ✅
언어 이해능력 개선
~~상식 보완~~ ✅
토크나이저 변경

Model Benchmark

Ko-LLM-Leaderboard

On Benchmarking...

Implementation Code

Since, chat_template already contains insturction format above. You can use the code below.

from transformers import AutoModelForCausalLM, AutoTokenizer

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

model = AutoModelForCausalLM.from_pretrained("maywell/Synatra-7B-v0.3-RP")
tokenizer = AutoTokenizer.from_pretrained("maywell/Synatra-7B-v0.3-RP")

messages = [
    {"role": "user", "content": "바나나는 원래 하얀색이야?"},
]

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])

Why It's benchmark score is lower than preview version?

Apparently, Preview model uses Alpaca Style prompt which has no pre-fix. But ChatML do.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	57.38
ARC (25-shot)	62.2
HellaSwag (10-shot)	82.29
MMLU (5-shot)	60.8
TruthfulQA (0-shot)	52.64
Winogrande (5-shot)	76.48
GSM8K (5-shot)	21.15
DROP (3-shot)	46.06