Dorna2 Llama3.1 8B Instruct

🚀 Dorna2-Llama3.1-8B-Instruct Model

The Dorna2-Llama3.1-8B-Instruct model is a decoder-only model, fine - tuned on Persian data by Part AI. It is built upon the Meta Llama 3.1 Instruct model, offering enhanced performance in Persian language tasks.

🚀 Quick Start

💻 Usage Examples

Basic Usage

To test and use the model freely on Hugging Face Spaces, click here!

You can also run conversational inference using the Transformers Auto classes with the generate() function. Here is an example:

import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM


tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system",
     "content": "You are a helpful Persian assistant. Please answer questions in the asked language."},
    {"role": "user", "content": "مثال A4 اندازه صفحه و A5 چیست؟"},
]

input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = model.generate(
    input_ids,
    max_new_tokens=256,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.3,
    top_p=0.85,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))

You can also use the notebook below to test the model in Google Colab.

📚 Documentation

🔧 Technical Details

The Dorna models are a family of decoder-only models, specifically trained/fine-tuned on Persian data, developed by Part AI. As a new release, an 8B instruct model from this family is being made available. Dorna2-Llama3.1-8B-Instruct is built using the Meta Llama 3.1 Instruct model.

📊 Evaluation

Comparative Evaluation

This evaluation compares Dorna2-Llama3.1-8B-Instruct, Llama3.1-8B-Instruct, and other fine-tuned Llama3.1-8B models. For broader comparisons among various large language models (LLMs), please refer to the Open Persian LLM Leaderboard, which provides a comprehensive evaluation across multiple LLMs.

Tasks and Evaluation Framework

Five specialized tasks have been carefully curated to evaluate and benchmark the models. Each task has been designed to challenge different aspects of the models' capabilities. These tasks include:

• Part Multiple Choice: Focuses on common knowledge and reasoning in a multiple-choice format.
• ARC Easy: Tests on easy-level general knowledge.
• ARC Challenge: Assesses models on harder questions requiring advanced reasoning.
• MMLU Pro: Covers professional-level exams.
• AUT Multiple Choice Persian: Specialized Persian-language examination.

Each dataset is entirely in Persian, offering a unique and robust testing ground for LLMs in non-English settings. Collectively, the datasets contain over 40k samples, spanning diverse linguistic and technical challenges such as Common Knowledge, Reasoning, Summarization, and Specialized Examinations.

Evaluation Results

📄 License

The model is under the llama3.1 license.

📞 Contact us

If you have any questions regarding this model, you can reach us via the community on Hugging Face.