
# OpenHermes-Emojitron-001


![image/png](https://i.postimg.cc/YSKwmNVH/emojitron.jpg)

*🧑‍🎓📜🔄🌌➡️🗣️🌠🔮🖊️👀🤝🔄🧩🛠️👂📡🔐🌟*

## Model description

OpenHermes-Emojitron-001 is a state of the art Emoji Fine-tune, based on the OpenHermes 2.5 model.

It only speaks in emoji.  Mostly.  If using for chat, start it off with a nice 😊 to get going.

Huge thank you to Teknium and Mistral for a wonderful model.

Follow all my updates in ML and AI on Twitter: https://twitter.com/mov_axbx


# Table of Contents
1. [Example Outputs](#example-outputs)
    - [Geography knowlege](#geography)
    - [Chat about food](#food-chat)
    - [Talk about movies](#movie-talk)
2. [Benchmark Results](#benchmark-results)
3. [Prompt Format](#prompt-format)
4. [Quantized Models](#quantized-models)


## Example Outputs
### Geography knowlege:
in what country is london
```  
🇬🇧
```

### Chat about food:
what country has the best food
```
🤔🌎🍽️

1️⃣🇮🇳

2️⃣🇯🇵

3️⃣🇺🇸

4️⃣🇪🇸

5️⃣🇫🇷
```

### Talk about movies:

tell me a time travel story about a delorean
```  
🛸🚗🕰️👨‍🔬🔍📅1985👴🚗💭🔜🌌😱💡💼🔢🔜📅1955🚗👨‍👩‍👧‍👦👀🔜🌉🕰️💥🌀🚗🕰️🔂📅1985👨‍🔬🎉
```

## Benchmark Results

There are no benchmarks for emoji models.  Maybe someone can create one.  EmojiBench 5K let's gooooooo

# Prompt Format

OpenHermes-Emojitron-001 uses ChatML as the prompt format, just like Open Hermes 2.5

It also appears to handle Mistral format great.  Especially since I used that for the finetune (oops)


# Quantized Models:

Coming soon if TheBloke thinks this is worth his 🕰️


{"language": ["en"], "license": "apache-2.0", "tags": ["mistral", "instruct", "finetune", "chatml"], "base_model": "teknium/OpenHermes-2.5-Mistral-7B", "model-index": [{"name": "OpenHermes-Emojitron-001", "results": []}]}
transformers safetensors mistral text-generation instruct finetune chatml conversational en base_model:teknium/OpenHermes-2.5-Mistral-7B license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us