Global LLM Leaderboard Rank1 (2023.12.28) • 5 items • Updated
Sakura-SOLRCA-Math-Instruct-DPO-v2
👁 ImageModel Details
Model Developers Kyujin Han (kyujinpy)
Method
Using DPO method.
With Intel/orca_dpo_pairs and argilla/distilabel-math-preference-dpo.
I shared the merge version kyujinpy/orca_math_dpo.
I shared the information about my model. (training and code)
Please see: ⭐Sakura-SOLAR.
Model Benchmark
Open leaderboard
- Follow up as link.
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|---|---|---|---|---|---|---|---|
| Sakura-SOLRCA-Math-Instruct-DPO-v2 | 74.17 | 71.25 | 88.52 | 66.13 | 72.16 | 83.03 | 63.91 |
| Sakura-SOLRCA-Math-Instruct-DPO-v1 | 74.13 | 71.25 | 88.48 | 66.21 | 72.12 | 82.87 | 63.84 |
| Sakura-SOLRCA-Instruct-DPO | 74.05 | 71.16 | 88.49 | 66.17 | 72.10 | 82.95 | 63.46 |
| Sakura-SOLAR-Instruct-DPO-v2 | 74.14 | 70.90 | 88.41 | 66.48 | 71.86 | 83.43 | 63.76 |
| kyujinpy/Sakura-SOLAR-Instruct | 74.40 | 70.99 | 88.42 | 66.33 | 71.79 | 83.66 | 65.20 |
Implementation Code
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 74.17 |
| AI2 Reasoning Challenge (25-Shot) | 71.25 |
| HellaSwag (10-Shot) | 88.52 |
| MMLU (5-Shot) | 66.13 |
| TruthfulQA (0-shot) | 72.16 |
| Winogrande (5-shot) | 83.03 |
| GSM8k (5-shot) | 63.91 |
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Safetensors
Model size
11B params
Tensor type
F16
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard71.250
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard88.520
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard66.130
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard72.160
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard83.030
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard63.910