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---
license: mit
datasets:
- datatab/SrpWikiDataset
- datatab/open-orca-slim-serbian
language:
- sr
base_model:
- datatab/Yugo55A-GPT
---
- **Developed by:** datatab
- **License:** mit
## 🏆 Results
> Results obtained through the [**Serbian LLM Evaluation Benchmark**](https://huggingface.co/datasets/datatab/serbian-llm-benchmark)
<table>
<tr>
<th><strong>MODEL<strong></th>
<th><strong>ARC-E<strong></th>
<th><strong>ARC-C<strong></th>
<th><strong>Hellaswag<strong></th>
<th><strong>PiQA<strong></th>
<th><strong>Winogrande<strong></th>
<th><strong>BoolQ<strong></th>
<th><strong>OpenbookQA<strong></th>
<th><strong>OZ_EVAL<strong></th>
<th>SCORE</th>
</tr>
<tr>
<td><a href="https://huggingface.co/datatab/YugoGPT-Florida/">YugoGPT-Florida</a></td>
<td><strong>0.6918<strong></td>
<td><strong>0.5766<strong></td>
<td><strong>0.4037<strong></td>
<td><strong>0.7374<strong></td>
<td><strong>0.5782<strong></td>
<td><strong>0.8685<strong></td>
<td><strong>0.5918<strong></td>
<td><strong>0.7407<strong></td>
<td><strong>64,85875<strong></td>
</tr>
<tr>
<td><a href="https://huggingface.co/datatab/Yugo55A-GPT/">Yugo55A-GPT</a></td>
<td>0.5846</td>
<td>0.5185</td>
<td>0.3686</td>
<td>0.7076</td>
<td>0.5277</td>
<td>0.8584</td>
<td>0.5485</td>
<td>0.6883</td>
<td>60,0275</td>
</tr>
<tr>
<td><a href="https://huggingface.co/datatab/Yugo60-GPT">Yugo60-GPT</a></td>
<td>0.4948</td>
<td>0.4542</td>
<td>0.3342</td>
<td>0.6897</td>
<td>0.5138</td>
<td>0.8212</td>
<td>0.5155</td>
<td>0.6379</td>
<td>55,76625</td>
</tr>
<tr>
<td><a href="https://huggingface.co/datatab/Yugo45-GPT">Yugo45-GPT</a></td>
<td>0.4049</td>
<td>0.3900</td>
<td>0.2812</td>
<td>0.6055</td>
<td>0.4992</td>
<td>0.5793</td>
<td>0.4433</td>
<td>0.6111</td>
<td>47,68125</td>
</tr>
</table>
# 🏋️ Training Stats
## 💻 Usage
```terminal
!pip -q install git+https://github.com/huggingface/transformers
```
```python
from IPython.display import HTML, display
def set_css():
display(HTML('''
<style>
pre {
white-space: pre-wrap;
}
</style>
'''))
get_ipython().events.register('pre_run_cell', set_css)
```
```python
import torch
import transformers
from transformers import AutoTokenizer, MistralForCausalLM
device = "cuda" if torch.cuda.is_available() else "cpu"
model = MistralForCausalLM.from_pretrained(
"datatab/YugoGPT-Florida",
torch_dtype="auto"
).to(device)
tokenizer = AutoTokenizer.from_pretrained("datatab/YugoGPT-Florida")
```
```python
from typing import Optional
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
def generate(
user_content: str, system_content: Optional[str] = ""
) -> str:
system_content = """Ispod se nalazi uputstvo koje definiše zadatak, zajedno sa unosom koji pruža dodatni kontekst.
Na osnovu ovih informacija, napišite odgovor koji precizno i tačno ispunjava zahtev.
"""
messages = [
{
"role": "system",
"content": system_content,
},
{"role": "user", "content": user_content},
]
tokenized_chat = tokenizer.apply_chat_template(
messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
).to("cuda")
text_streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
output = model.generate(
tokenized_chat,
streamer=text_streamer,
max_new_tokens=2048,
temperature=0.1,
repetition_penalty=1.11,
top_p=0.92,
top_k=1000,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
do_sample=True,
)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
```
```python
generate("Nabroj mi sve planete suncevog sistemai reci mi koja je najveca planeta?")
```
```terminal
Sunčev sistem sadrži osam planeta: Merkur, Venera, Zemlja, Mars, Jupiter, Saturn, Uran i Neptun. Najveća planeta u Sunčevom sistemu je Jupiter.
```
## 💡 Contributions Welcome!
Have ideas, bug fixes, or want to add a custom model? We'd love for you to be part of the journey! Contributions help grow and enhance the capabilities of the **YugoGPT-Florida**.
## 📜 Citation
Thanks for using **YugoGPT-Florida** — where language learning models meet Serbian precision and creativity! Let's build smarter models together. 🚀�
If you find this model useful in your research, please cite it as follows:
```bibtex
@article{YugoGPT-Florida},
title={YugoGPT-Florida},
author={datatab},
year={2024},
url={https://huggingface.co/datatab/YugoGPT-Florida}
}
```
<div id="zastava">
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font-size:14pt
}
</style> |