Model Card for Qwen3-1.7B-azerbaijani-math
Model Details
This model is a fine-tuned version of the Qwen3-1.7B, adapted for instruction-following in Azerbaijani, with focus on mathematical problem solving. The fine-tuning process improves the model’s ability to:
- Understand and solve math problems written in Azerbaijani
- Respond in a fluent, natural Azerbaijani style
- Follow task-specific instructions with improved alignment and chat capability.
Model Description
- Developed by: Rustam Shiriyev
- Language(s) (NLP): Azerbaijani
- License: MIT
- Finetuned from model: unsloth/Qwen3-1.7B
Uses
Direct Use
This model is best suited for:
- Solving and explaining math problems in Azerbaijani
- Educational assistants and tutoring bots for Azerbaijani students
Out-of-Scope Use
- Not fine-tuned for factual correctness or safety filtering.
How to Get Started with the Model
Use the code below to get started with the model.
from huggingface_hub import login
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
login(token="")
tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen3-1.7B",)
base_model = AutoModelForCausalLM.from_pretrained(
"unsloth/Qwen3-1.7B",
device_map={"": 0}, token=""
)
model = PeftModel.from_pretrained(base_model,"Rustamshry/Qwen3-1.7B-azerbaijani-math")
question = "Bir f(x) funksiyası verilib: f(x) = 2x^2 + 3x + 4. Bu funksiyanın maksimum və ya minimum nöqtəsini hesablayın və nəticəni geniş izah edin."
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 = 512,
temperature = 0.7, top_p = 0.8, top_k = 20,
streamer = TextStreamer(tokenizer, skip_prompt = True),
)
Training Details
Training Data
The model was fine-tuned on a curated combination of:
OnlyCheeini/azerbaijani-math-gpt4o — 100,000 examples of Azerbaijani math instructions generated via GPT-4o, focused on algebra, geometry, and applied math.
mlabonne/FineTome-100k — 35,000 chat-style instruction samples (35% of the full dataset) to improve general-purpose instruction following and conversational ability.
Framework versions
- PEFT 0.14.0
- Downloads last month
- 22
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