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Model Details

The Dorna models are a family of decoder-only models, specifically trained/fine-tuned on Persian data, developed by Part AI. As an initial release, an 8B instruct model from this family is being made available. Dorna-Llama3-8B-Instruct is built using the Meta Llama 3 Instruct model.

How to use

You can run conversational inference using the Transformers Auto classes with the generate() function. Let's look at 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": "Ϊ©Ψ§ΨΊΨ° A4 Ψ¨Ψ²Ψ±Ϊ― ΨͺΨ± Ψ§Ψ³Ψͺ یا A5؟"},
]

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=0.6,
    top_p=0.9,
)
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.

Colab Code

Evaluation

This model is evaluated on questions across various tasks, including Boolean Questions, Code Generation, Long Response, Math, News QA, Paraphrasing, General Knowledge, and Summarization. Most categories typically have two main difficulty levels: Hard and Easy.

Both human evaluation and automatic evaluation (with GPT-4 as the judge) are performed.

In both tables, Dorna-8B-it is used as an abbreviated form of Dorna-Llama3-8B-Instruct.

Overall human evaluation results are as follows:

Model Pairs Parameters Win % Lose % Tie %
Dorna-8B-it vs. Meta-Llama-3-8B-Instruct 8B 36.94 17.39 45.67
Dorna-8B-it vs. GPT 3.5 turbo-1106 N.A. 32.01 26.94 41.05
Dorna-8B-it vs. Persian Mind 7B 55.77 10.49 33.74

Category-based human evaluation results are as follows:

Win/Lose/Tie % is reported for each category.

Model Pairs Parameters Bool Complex Bool Easy Code Gen General Long Response Historical Long Response Math Complex Math Easy News QA Complex News QA Easy Paraphrasing General Knowledge Easy General Knowledge Hard Summarization
Dorna-8B-it vs. Meta-Llama-3-8B-Instruct 8B 0.25/0.25/0.5 0.28/0.35/0.38 0.6/0.1/0.3 0.8/0.08/0.12 0.4/0.3/0.3 0.28/0.08/0.65 0.47/0.00/0.53 0.55/0.07/0.38 0.43/0.15/0.42 0.1/0.05/0.85 0.31/0.2/0.49 0.59/0.13/0.28 0.28/0.2/0.53
Dorna-8B-it vs. GPT 3.5 turbo-1106 N.A. 0.35/0.35/0.3 0.3/0.3/0.4 0.1/0.3/.06 0.2/0.45/0.35 0.46/0.27/0.27 0.25/0.1/0.65 0.05/0.1/0.85 0.12/0.35/0.53 0.15/0.1/0.75 0.25/0.15/0.6 0.3/0.32/0.38 0.22/0.53/0.25 0.35/0.55/0.1
Dorna-8B-it vs. Persian Mind 7B 0.47/0.25/0.28 0.57/0.15/0.28 0.9/0.1/0.0 0.82/0.08/0.1 0.4/0.17/0.42 0.3/0.0/0.7 0.22/0.08/0.7 0.72/0.07/0.2 0.7/0.0/0.3 0.7/0.05/0.25 0.51/0.12/0.37 0.61/0.1/0.29 0.93/0.0/0.07

Automatic evaluation results are as follows:

Model Pairs Parameters Overall Win Rate % Easy Win Rate % Hard Win Rate %
Dorna-8B-it vs. Llama 3 base 8B 58.96 56.00 64.49
Dorna-8B-it vs. Part Mistral 7B 77.20 73.00 85.05
Dorna-8B-it vs. Persian Mind 7B 90.88 87.50 97.20
Dorna-8B-it vs. Neuraorca Gemma 7b 7B 86.32 86.50 85.98
Dorna-8B-it vs. Maral 7b 7B 97.39 97.00 98.13
Dorna-8B-it vs. PersianLlama 7b 7B 98.70 98.00 100.00
Dorna-8B-it vs. Aya-23-8B 8B 52.77 56.50 45.79
Dorna-8B-it vs. Aya-23-35B 35B 45.93 54.00 30.84
Dorna-8B-it vs. Command R 35B 58.63 61.00 54.21

Contact us

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

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