--- 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.
MODEL ARC-E ARC-C Hellaswag BoolQ Winogrande OpenbookQA PiQA
*Yugo55-GPT-v4-4bit 51.41 36.00 57.51 80.92 65.75 34.70 70.54
Yugo55A-GPT 51.52 37.78 57.52 84.40 65.43 35.60 69.43
Yugo60-GPT tbd tbd tbd tbd tbd tbd tbd
## 💻 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(''' ''')) 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") ```