This is gaokerena-R, a model trained with a limited-data approach to enhance the Persian medical reasoning capabilities of the aya-expanse-8b model. Despite using less data, gaokerena-R outperforms our previous model, gaokerena-V, which was trained on a much larger dataset. This demonstrates the effectiveness of our reasoning-focused training strategy under data-constrained conditions.

Developed by: Mehrdad Ghassabi,Sadra Hakim,Hamidreza Baradaran Kashani, Pedram Rostami,Zahra Kazemi
Model type: Medical Language Model
Funded by: All researcher worked voluntarily
Language: Persian
License: CC BY-NC-SA 4.0 (non-commercial use only)
Finetuned from model : Aya Expanse 8B

Model Sources

Risks and Limitations

While Gaokerena-R aims to provide accurate information, it is not a substitute for professional medical advice. The model may have limitations in:

Handling medical emergencies.
Addressing highly specialized or rare medical conditions.
Offering region-specific guidance, as the training data does not include localized Persian medical practices.

How to Get Started with the Model

Since the model has been built upon Aya, you can use this model in a single or multi-modal configuration.

Single modal inference

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from peft.peft_model import PeftModel

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.bfloat16

model = AutoModelForCausalLM.from_pretrained(
    "CohereForAI/aya-expanse-8b",
    torch_dtype=dtype,
    device_map=device
)
tokenizer = AutoTokenizer.from_pretrained("CohereForAI/aya-expanse-8b")

model = PeftModel.from_pretrained(model = model,model_id = "gaokerena/gaokerena-r1.0")
model = model.merge_and_unload()

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
pipe_output = pipe([{"role": "user", "content": "چگونه استرس می‌تواند باعث ایجاد آفت دهان شود؟"}],
                       max_new_tokens=1024,
                       eos_token_id=[tokenizer.eos_token_id],
                       do_sample=False,
)

output = pipe_output[0]["generated_text"][-1]["content"]
print(output)

Multi modal inference

python

from transformers import AutoProcessor, AutoModelForImageTextToText
import torch
from peft.peft_model import PeftModel

model_id = "CohereForAI/aya-vision-8b"

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id, device_map="auto", torch_dtype=torch.float16
)
model = PeftModel.from_pretrained(model=model,model_id="gaokerena/gaokerena-v1.0")
model = model.merge_and_unload()

messages = [
    {"role": "user",
     "content": [
       {"type": "image", "url": "./chest-pic.jpeg"},
        {"type": "text", "text": "در مورد این تصویر توضیح بده"},
    ]},
    ]

inputs = processor.apply_chat_template(
    messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
).to(model.device)

gen_tokens = model.generate(
    **inputs,
    max_new_tokens=1024,
    do_sample=True,
    temperature=0.3,
)
print(processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Training Details

The Gaokerena-R model was post-trained on only 11000 preferred--rejected pairs, which were synthesized by another larger AI.

Environmental Impact

Hardware Type: NVIDIA H100 PCIe 80 GB GPU
Hours used: 1
Carbon Emitted: 0.0301 KG CO₂ eq.

Bibtex

if you found our model useful feel free to give us a cite!

@misc{Gaokerena-r1.0,
  title={Enhancing Reasoning Skills in Small Persian Medical Language Models Can Outperform Large-Scale Data Training},
  author={Ghassabi, Mehrdad and Hakim, Sadra and Baradaran Kashani, Hamidreza and Rostami, Pedram and Kazemi, Zahra},
  year={2025}
  eprint={2510.20059},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}