Instructions to use erayalp/qwen2.5-0.5b-instruct-GRPO-v3-tr-math-gsm8k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use erayalp/qwen2.5-0.5b-instruct-GRPO-v3-tr-math-gsm8k with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="erayalp/qwen2.5-0.5b-instruct-GRPO-v3-tr-math-gsm8k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("erayalp/qwen2.5-0.5b-instruct-GRPO-v3-tr-math-gsm8k", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use erayalp/qwen2.5-0.5b-instruct-GRPO-v3-tr-math-gsm8k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "erayalp/qwen2.5-0.5b-instruct-GRPO-v3-tr-math-gsm8k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "erayalp/qwen2.5-0.5b-instruct-GRPO-v3-tr-math-gsm8k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/erayalp/qwen2.5-0.5b-instruct-GRPO-v3-tr-math-gsm8k

SGLang

How to use erayalp/qwen2.5-0.5b-instruct-GRPO-v3-tr-math-gsm8k with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "erayalp/qwen2.5-0.5b-instruct-GRPO-v3-tr-math-gsm8k" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "erayalp/qwen2.5-0.5b-instruct-GRPO-v3-tr-math-gsm8k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "erayalp/qwen2.5-0.5b-instruct-GRPO-v3-tr-math-gsm8k" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "erayalp/qwen2.5-0.5b-instruct-GRPO-v3-tr-math-gsm8k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use erayalp/qwen2.5-0.5b-instruct-GRPO-v3-tr-math-gsm8k with Docker Model Runner:
```
docker model run hf.co/erayalp/qwen2.5-0.5b-instruct-GRPO-v3-tr-math-gsm8k
```

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 model.

Starting from 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 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

~~Phase 1: SFT with basic arithmatic and math problems~~
~~Phase 2: SFT with moderately difficult math problems~~
Phase 3: SFT with full-scale GSM8K-TR complexity
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:

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))