library_name, pipeline_tag, tags, license, datasets, language, metrics, base_model

library_name	pipeline_tag	tags	license	datasets	language	metrics	base_model
transformers	text-generation	base_model:adapter:unsloth/Qwen3-1.7B lora sft transformers trl unsloth	apache-2.0	az-llm/az_academic_qa-v1.0 az-llm/az_creative-v1.0 tahmaz/azerbaijani_text_math_qa1 omar07ibrahim/Alpaca_Stanford_Azerbaijan	az	accuracy	unsloth/Qwen3-1.7B

Nizami-1.7B

A Lightweight Language Model

Model Description 📝

Nizami-1.7B is a fine-tuned version of Qwen3-1.7B in Azerbaijani. It was trained on a curated dataset of 35,916 examples from historical, legal, math, philosophical, and social science texts.

Key Features ✨

• Architecture: Transformer-based language model 🏗️
• Developed by: Rustam Shiriyev
• Language(s): Azerbaijani
• License: MIT
• Fine-Tuning Method: Supervised fine-tuning
• Domain: Academic texts (History, Math, Law, Philosophy, Social Sciences) 📚
• Finetuned from model: unsloth/Qwen3-1.7B

Intended Use

• Academic research assistance in Azerbaijani 🏆
• Question answering on humanities/social science topics 🎯
• Knowledge exploration in Azerbaijani⚡

Limitations ⚠️

• Generating factual statements without verification
• Limited dataset size (35,916 examples) → may not generalize perfectly outside training domains.
• Possible hallucinations if asked for factual details.

Evaluation 📊

AARA: khazarai/AARA_Azerbaijani_LLM_Benchmark

How to Get Started with the Model 💻

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("khazarai/Nizami-1.7B")
model = AutoModelForCausalLM.from_pretrained(
    "khazarai/Nizami-1.7B",
    device_map={"": 0}
)

model = PeftModel.from_pretrained(base_model,"khazarai/Nizami-1.7B")

question = """
Əldə olunan arxeoloji qazıntı materiallarına əsasən, Eneolit dövründə Azərbaycanda metalın ilk istifadəsi ilə bağlı hansı konkret obyektlər tapılmışdır və bu obyektlər həmin dövrdə cəmiyyətin sosial strukturunun inkişafına necə təsir etmişdir? Əlavə olaraq, həmin dövrdə metallurgiya və metalişləmə sənətkarlığının inkişafının iqtisadi və mədəni aspektləri haqqında nə deyə bilərsiniz?
"""

messages = [
    {"role" : "user", "content" : question}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize = False,
    add_generation_prompt = True, 
    enable_thinking = False,
)

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 1800,
    temperature = 0.7,
    top_p = 0.8,
    top_k = 20,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

Training Data

Dataset I: az-llm/az_academic_qa-v1.0 Description: A 7,000-example dataset for academic-style comprehension and reasoning in Azerbaijani.

Dataset II: az-llm/az_creative-v1.0 Description: A 4,000-example creative dataset with imaginative Azerbaijani prompts and expressive responses. Includes role-based instructions (e.g., Galileo, interstellar assistant, detective), fictional narratives, poetic reasoning, and emotional simulations.

Dataset III: tahmaz/azerbaijani_text_math_qa1 Description: A dataset of 6,500 high school math examples in Azerbaijani.

Dataset IV: omar07ibrahim/Alpaca_Stanford_Azerbaijan Description: Azerbaijani version of the Alpaca dataset for instruction-following tasks.