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---
base_model:
- datatab/Yugo55-GPT-v4
- datatab/Yugo55-GPT-DPO-v1-chkp-600
library_name: transformers
tags:
- mergekit
- merge
- text-generation-inference
- transformers
- mistral
license: mit
language:
- sr
datasets:
- datatab/alpaca-cleaned-serbian-full
- datatab/ultrafeedback_binarized
- datatab/open-orca-slim-serbian
---
# Yugo60-GPT
- **Developed by:** datatab
- **License:** mit
## 🏆 Results
> Results obtained through the Serbian LLM evaluation, released by Aleksa Gordić: [serbian-llm-eval](https://github.com/gordicaleksa/serbian-llm-eval)
> * Evaluation was conducted on a 4-bit version of the model due to hardware resource constraints.
<table>
<tr>
<th>MODEL</th>
<th>ARC-E</th>
<th>ARC-C</th>
<th>Hellaswag</th>
<th>BoolQ</th>
<th>Winogrande</th>
<th>OpenbookQA</th>
<th>PiQA</th>
</tr>
<tr>
<td><a href="https://huggingface.co/datatab/Yugo55-GPT-v4-4bit/">*Yugo55-GPT-v4-4bit</a></td>
<td>51.41</td>
<td>36.00</td>
<td>57.51</td>
<td>80.92</td>
<td><strong>65.75</strong></td>
<td>34.70</td>
<td><strong>70.54</strong></td>
</tr>
<tr>
<td><a href="https://huggingface.co/datatab/Yugo55A-GPT/">Yugo55A-GPT</a></td>
<td><strong>51.52</strong></td>
<td><strong>37.78</strong></td>
<td><strong>57.52</strong></td>
<td><strong>84.40</strong></td>
<td>65.43</td>
<td><strong>35.60</strong></td>
<td>69.43</td>
</tr>
<tr>
<td><a href="https://huggingface.co/datatab/Yugo60-GPT/">Yugo60-GPT</a></td>
<td><strong>tbd</strong></td>
<td><strong>tbd</strong></td>
<td><strong>tbd</strong></td>
<td><strong>tbd</strong></td>
<td><strong>tbd</strong></td>
<td><strong>tbd</strong></td>
<td><strong>tbd</strong></td>
</tr>
</table>
## 💻 Usage
```terminal
!pip -q install git+https://github.com/huggingface/transformers
!pip install -q datasets loralib sentencepiece
!pip -q install bitsandbytes accelerate
```
```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, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"datatab/Yugo60-GPT", torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained(
"datatab/Yugo60-GPT", torch_dtype="auto"
)
```
```python
from typing import Optional
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
def generate(
user_content: str, system_content: Optional[str] = ""
) -> str:
system_content = "Ispod je uputstvo koje opisuje zadatak, upareno sa unosom koji pruža dodatni kontekst. Napišite odgovor koji na odgovarajući način kompletira 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")
```
```python
generate("Koja je razlika između lame, vikune i alpake?")
```
```python
generate("Napišite kratku e-poruku Semu Altmanu dajući razloge za GPT-4 otvorenog koda")
``` |