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---
language:
- en
license: apache-2.0
---
<div align="center">
<b style="font-size: 40px;">Zion_Alpha_Instruction_Tuned</b>
</div>
<img src="https://i.imgur.com/e1LEQ18.png" alt="Zion_Alpha_Instruction_Tuned" style="width: 50%; min-width: 400px; display: block; margin: auto;">
# Model Details
Zion_Alpha is the first **REAL** Hebrew model in the world. This version WAS fine tuned for tasks. I did the finetune using SOTA techniques and using my insights from years of underwater basket weaving. If you wanna offer me a job, just add me on Facebook.
# Future Plans
I plan to perform a SLERP merge with one of my other fine-tuned models, which has a bit more knowledge about Israeli topics. Additionally, I might create a larger model using MergeKit, but we'll see how it goes.
# Looking for Sponsors
Since all my work is done on-premises, I am constrained by my current hardware. I would greatly appreciate any support in acquiring an A6000, which would enable me to train significantly larger models much faster.
# Papers?
Maybe. We'll see. No promises here 🤓
# Contact Details
I'm not great at self-marketing (tp say the least) and don't have any social media accounts. If you'd like to reach out to me, you can email me at [email protected]. Please note that this email might receive more messages than I can handle, so I apologize in advance if I can't respond to everyone.
# Versions and QUANTS
- Base model: [FP16](https://huggingface.co/SicariusSicariiStuff/Zion_Alpha)
- Instruction tuned: [FP16](https://huggingface.co/SicariusSicariiStuff/Zion_Alpha_Instruction_Tuned) | [GGUF](https://huggingface.co/SicariusSicariiStuff/Zion_Alpha_Instruction_Tuned_GGUF)
# Model architecture
Based on Mistral 7B. I didn't even bother to alter the tokenizer.
# The recommended prompt setting is Debug-deterministic:
```
temperature: 1
top_p: 1
top_k: 1
typical_p: 1
min_p: 1
repetition_penalty: 1
```
# The recommended instruction template is Mistral:
```
{%- for message in messages %}
{%- if message['role'] == 'system' -%}
{{- message['content'] -}}
{%- else -%}
{%- if message['role'] == 'user' -%}
{{-'[INST] ' + message['content'].rstrip() + ' [/INST]'-}}
{%- else -%}
{{-'' + message['content'] + '</s>' -}}
{%- endif -%}
{%- endif -%}
{%- endfor -%}
{%- if add_generation_prompt -%}
{{-''-}}
{%- endif -%}
```
# English to hebrew example:
<div align="center">
<b style="font-size: 40px;">Zion_Alpha English to Hebrew example</b>
</div>
<img src="https://i.imgur.com/JnTuawF.png" alt="Zion_Alpha" style="width: 40%; min-width: 600px; display: block; margin: auto;">
# English to hebrew example:
<div align="center">
<b style="font-size: 40px;">Zion_Alpha Hebrew to English example</b>
</div>
<img src="https://i.imgur.com/Wm2igLJ.png" alt="Zion_Alpha" style="width: 40%; min-width: 600px; display: block; margin: auto;">
<div align="center">
<b style="font-size: 30px;">Unscripted video: live zero shot demonstration at story writing capabilities in Hebrew</b>
[](https://www.youtube.com/watch?v=YYKeovnS0do)
</div>
<div align="center">
<b style="font-size: 30px;">Zion_Alpha VS Mistral 'Hebrew' Live & unscripted in real time</b>
[](https://www.youtube.com/watch?v=DQFtx8M2txc)
</div>
<div align="center">
<b style="font-size: 30px;">Zion_Alpha VS Mistral 'Hebrew' Live & unscripted in real time Long text translation</b>
[](https://www.youtube.com/watch?v=w5fz3Ot6tH8)
</div>
### History
The model was originally trained about 2 month after Mistral (v0.1) was released.
As of 04 June 2024, Zion_Alpha got the **Highest SNLI score in the world** among open source models in Hebrew, surpassing most of the models by a huge margin. (**84.05** score)
<img src="https://i.imgur.com/7HokS5w.png" alt="Zion_Alpha SNLI Score" style="width: 80%; min-width: 700px; display: block; margin: auto;">
### Support
<img src="https://i.imgur.com/0lHHN95.png" alt="GPUs too expensive" style="width: 10%; min-width: 100px; display: block; margin: left;">
- [My Ko-fi page](https://ko-fi.com/sicarius) ALL donations will go for research resources and compute, every bit counts 🙏🏻
- [My Patreon](https://patreon.com/TenebraAI) ALL donations will go for research resources and compute, every bit counts 🙏🏻
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