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---
license: mit
language:
- en
- fr
- de
- es
- it
- pt
- ja
- ko
- zh
- ar
- el
- fa
- pl
- id
- cs
- he
- hi
- nl
- ro
- ru
- tr
- uk
- vi
datasets:
- xmanii/mauxi-talk-pro
- xmanii/mauxitalk-persian
---
# Hormoz 8B
## Introduction
This model is an effort in order to make a multi-lingual and _on device_ models which can be executed on the consumer hardware. The model follows the steps used in training _DeepSeek_ model. However, the model is _not a reasoning model_ and a generic question answering, conversational and _uncensored_ model which has been made with a cost of around $4000 USD.
If you're curious about the model you also can see our [GitHub](https://github.com/mann-e/hormoz) and learn more about the benchmarks and costs.
Also, this model is based on _Command R_'s architecture, since that architecture gave us the best results in multilingual chat. Specially with languages such as _Persian_ and _Arabic_. This way, you can consider this model like a commercially useaeble version of _aya expanse_ as well.
### The name
<p align="center">
<img src="https://github.com/Mann-E/hormoz/blob/main/hormoz-logo.png?raw=true" width=768px />
</p>
The name __Hormoz__ comes from the Persian word "هرمز" which has multiple meanings. It can point to the _strait of Hormoz_ in Persian Gulf or _Hormoz Island_ which is part of the Hormozgan Province in the south of Iran. Also it may point to "اورمزد" or _Ourmozd_ which is middle/ancient Persian name for the planet _Jupiter_ and derived from the term _Ahura Mazda_ or the Avestan term for God.
## How to run (transformers)
### Free API
The model is also available through [Jabir Project's API](https://jabirproject.org/api-docs) and [Pollinations.AI](https://pollinations.ai).
### Install transformers
```
pip install transformers --upgrade
```
_Note:_ For better performance, you may need to install `accelerate` package as well.
### Inference
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "mann-e/Hormoz-8B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id).to("cuda")
messages = [{"role": "user", "content": "What is the answer to universe, life and everything?"}]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
gen_tokens = model.generate(
input_ids,
max_new_tokens=1024,
do_sample=True,
temperature=1.0,
)
gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)
```
## License
This model is published under _MIT_ license.
### Commercial Use
Since this model is MIT licensed, you're free to do whatever you want with the model. However since we're a relatively small startup, we recommend you if you are a big corporate and you host this model, give us a capacity of your API as well. This way, we both can benefit from the model. |