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Evangelion-7B
![](https://cdn-uploads.huggingface.co/production/uploads/63ae02ff20176b2d21669dd6/-si1T5gSSjvg1QlfeFKDf.jpeg)
I was just curious to see if something special might happen if one uses:
The underlying model that I used was /Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp
.
Dataset
Dataset: /argilla/distilabel-intel-orca-dpo-pairs
The dataset was roughly ~3000 samples but they were high quality (according to the chosen_score).
The following filters were applied to the original dataset:
dataset = dataset.filter(
lambda r:
r["status"] != "tie" and
r["chosen_score"] >= 8 and
not r["in_gsm8k_train"]
)
Chat Template
I decided to go with the ChatML which is used for OpenHermes2.5 By the way I integreated the chat template into the models tokenizer.
<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{user}<|im_end|>
<|im_start|>assistant
{asistant}<|im_end|>
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 71.71 |
AI2 Reasoning Challenge (25-Shot) | 68.94 |
HellaSwag (10-Shot) | 86.45 |
MMLU (5-Shot) | 63.97 |
TruthfulQA (0-shot) | 64.01 |
Winogrande (5-shot) | 79.95 |
GSM8k (5-shot) | 66.94 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard68.940
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard86.450
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard63.970
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard64.010
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard79.950
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard66.940