---
base_model: unsloth/Qwen3-1.7B
library_name: peft
license: mit
datasets:
- OnlyCheeini/azerbaijani-math-gpt4o
- mlabonne/FineTome-100k
language:
- az
pipeline_tag: question-answering
tags:
- math
---

# 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.

```python 
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