Synatra-7B-v0.3-dpo🐧

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Model Details

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

Trained On
A100 80GB * 1

Instruction format

It follows ChatML format and Alpaca(No-Input) format.

Model Benchmark

KOBEST_BOOLQ, SENTINEG, WIC - ZERO_SHOT

EleutherAI/lm-evaluation-harness를 사용하여 BoolQ, SentiNeg, Wic을 측정했습니다.

Model	COPA	HellaSwag	BoolQ	SentiNeg
EleutherAI/polyglot-ko-12.8b	0.7937	0.5954	0.4818	0.9117
Synatra-7B-v0.3-base	0.6344	0.5140	0.5226	NaN
Synatra-7B-v0.3-dpo	0.6380	0.4780	0.8058	0.8942

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

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])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	53.14
ARC (25-shot)	62.8
HellaSwag (10-shot)	82.58
MMLU (5-shot)	61.46
TruthfulQA (0-shot)	56.46
Winogrande (5-shot)	76.24
GSM8K (5-shot)	23.73
DROP (3-shot)	8.68