Yugo60-GPT
- Developed by: datatab
- License: mit
🏆 Results
Results obtained through the Serbian LLM evaluation, released by Aleksa Gordić: 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
!pip -q install git+https://github.com/huggingface/transformers
!pip install -q datasets loralib sentencepiece
!pip -q install bitsandbytes accelerate
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)
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"
)
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)
generate("Nabroj mi sve planete suncevog sistemai reci mi koja je najveca planeta")
generate("Koja je razlika između lame, vikune i alpake?")
generate("Napišite kratku e-poruku Semu Altmanu dajući razloge za GPT-4 otvorenog koda")
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