

<h1 align="center">🏠 Socials</h1>
<p align="center">
  🤗 <a href="https://huggingface.co/VitalContribution" target="_blank">HF Repo</a> • 🐦 <a href="https://twitter.com/VContribution" target="_blank">Twitter</a> 
</p>

# Evangelion-7B

<img src="https://cdn-uploads.huggingface.co/production/uploads/63ae02ff20176b2d21669dd6/-si1T5gSSjvg1QlfeFKDf.jpeg" width="500" height="600">

I was just curious to see if something special might happen 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|>
```
{"license": "apache-2.0", "library_name": "transformers", "datasets": ["argilla/distilabel-intel-orca-dpo-pairs"], "pipeline_tag": "text-generation"}
transformers safetensors mistral text-generation conversational dataset:argilla/distilabel-intel-orca-dpo-pairs license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us