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 |