---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE
language:
- tr
- en
datasets:
- ytu-ce-cosmos/gsm8k_tr
base_model:
- erayalp/qwen2.5-0.5b-instruct-SFT-v2-tr-math-medium
pipeline_tag: text-generation
library_name: transformers
tags:
- group-relative-policy-optimization
- reinforcement-learning
- curriculum-learning
- math
- supervised-fine-tuning
- reasoning
- turkish
---
## Objective
This model is the **final product** of the multi-stage training pipeline designed to improve the Turkish mathematical reasoning capabilities of the compact [Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) model.

Starting from [`erayalp/qwen2.5-0.5b-instruct-sft-v2-tr-math-medium`](https://huggingface.co/erayalp/qwen2.5-0.5b-instruct-sft-v2-tr-math-medium), which was fine-tuned on 2-3 step reasoning Turkish math problems, this version continues training using [`ytu-ce-cosmos/gsm8k_tr`](https://huggingface.co/datasets/ytu-ce-cosmos/gsm8k_tr) to improve the model’s step-by-step reasoning and generalization to multi step grade school math performance on such small model.

#### This model is intended for:
- Researchers exploring **reinforcement learning on small LLMs**.
- Research on curriculum learning and multi-step math reasoning in small models.
- Comparative baselines for evaluating Turkish math reasoning tasks of grade school math complexity.

### Limitations
- With only 0.5B parameters, it may not perform as robustly as larger models.
- Math-specific hallucinations may persist in underrepresented edge patterns.
- Prompt sensitivity and reasoning depth are **open to future improvements**.

### Roadmap
1. ~~Phase 1: SFT with basic arithmatic and math problems~~
2. ~~Phase 2: SFT with moderately difficult math problems~~
3. **Phase 3: SFT with full-scale GSM8K-TR complexity**
4. **Phase 4: GRPO-based training to optimize multi-step reasoning and reduce hallucinations**

## How to Use

You can easily run inference using the Transformers library:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "erayalp/qwen2.5-0.5B-instruct-GRPO-v3-tr-math-gsm8k"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Bir bahçede 25 gül var. 40 lale vardır. 35 papatya var. Çiçeklerin yüzde kaçı gül değildir?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```