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---
license: apache-2.0
library_name: transformers
datasets:
- argilla/distilabel-intel-orca-dpo-pairs
pipeline_tag: text-generation
model-index:
- name: Evangelion-7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 68.94
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VitalContribution/Evangelion-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 86.45
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VitalContribution/Evangelion-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 63.97
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VitalContribution/Evangelion-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 64.01
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VitalContribution/Evangelion-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 79.95
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VitalContribution/Evangelion-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 66.94
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VitalContribution/Evangelion-7B
name: Open LLM Leaderboard
---
<img src="https://cdn-uploads.huggingface.co/production/uploads/63ae02ff20176b2d21669dd6/AID8texkGhpCPrxEtb2MF.png" width="300" />
# Mozaic-7B (prev. Evangelion-7B)
We were curious to see what happens if one uses:
$$
\text{{high-quality DPO dataset}} + \text{{merge of DPO optimized and non-DPO optimized model}}
$$
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:
```python
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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_VitalContribution__Evangelion-7B)
| 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|
|