Yugo55A-GPT / README.md

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	---
	base_model:
	- mlabonne/AlphaMonarch-7B
	- datatab/Yugo55-GPT-v4
	- datatab/Yugo55-GPT-DPO-v1-chkp-300
	- NousResearch/Nous-Hermes-2-Mistral-7B-DPO
	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
	---
	# Yugo55A-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>
	</table>


	# 🔗 Merge Details

	### Merge Method
	> This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
	> This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method.

	### Models Merged

	The following models were included in the merge:
	* [datatab/Yugo55-GPT-v4](https://huggingface.co/datatab/Yugo55-GPT-v4)
	* [datatab/Yugo55-GPT-DPO-v1-chkp-300](https://huggingface.co/datatab/Yugo55-GPT-DPO-v1-chkp-300)
	* [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B)
	* [NousResearch/Nous-Hermes-2-Mistral-7B-DPO](https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO)

	## 🧩 Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	models:
	- model: datatab/Yugo55-GPT-v4
	parameters:
	weight: 1.0
	- model: datatab/Yugo55-GPT-DPO-v1-chkp-300
	parameters:
	weight: 1.0
	- model: mlabonne/AlphaMonarch-7B
	parameters:
	weight: 0.5
	- model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
	parameters:
	weight: 0.5
	merge_method: linear
	dtype: float16
	```


	## 💻 Usage
	```terminal
	!pip -q install git+https://github.com/huggingface/transformers # need to install from github
	!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/Yugo55A-GPT", torch_dtype="auto"
	)

	tokenizer = AutoTokenizer.from_pretrained(
	"datatab/Yugo55A-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")
	```