Instructions to use VolkanSimsir/LLaMA-3-8B-GRPO-math-tr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VolkanSimsir/LLaMA-3-8B-GRPO-math-tr with Transformers:

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

pipe = pipeline("text-generation", model="VolkanSimsir/LLaMA-3-8B-GRPO-math-tr")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("VolkanSimsir/LLaMA-3-8B-GRPO-math-tr")
model = AutoModelForCausalLM.from_pretrained("VolkanSimsir/LLaMA-3-8B-GRPO-math-tr", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use VolkanSimsir/LLaMA-3-8B-GRPO-math-tr with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VolkanSimsir/LLaMA-3-8B-GRPO-math-tr"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VolkanSimsir/LLaMA-3-8B-GRPO-math-tr",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VolkanSimsir/LLaMA-3-8B-GRPO-math-tr

SGLang

How to use VolkanSimsir/LLaMA-3-8B-GRPO-math-tr 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 "VolkanSimsir/LLaMA-3-8B-GRPO-math-tr" \
    --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": "VolkanSimsir/LLaMA-3-8B-GRPO-math-tr",
		"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 "VolkanSimsir/LLaMA-3-8B-GRPO-math-tr" \
        --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": "VolkanSimsir/LLaMA-3-8B-GRPO-math-tr",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use VolkanSimsir/LLaMA-3-8B-GRPO-math-tr with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for VolkanSimsir/LLaMA-3-8B-GRPO-math-tr to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for VolkanSimsir/LLaMA-3-8B-GRPO-math-tr to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for VolkanSimsir/LLaMA-3-8B-GRPO-math-tr to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="VolkanSimsir/LLaMA-3-8B-GRPO-math-tr",
    max_seq_length=2048,
)

Docker Model Runner
How to use VolkanSimsir/LLaMA-3-8B-GRPO-math-tr with Docker Model Runner:
```
docker model run hf.co/VolkanSimsir/LLaMA-3-8B-GRPO-math-tr
```

Model Details

Base Model: ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1
Training Time: 3 hours with A40

Lora config:

lora_r: 32
lora_alpha:32

Model Description

This model has been trained using the GRPO (Guided Reward Preference Optimization) method with the Turkish GSM8K dataset to enhance its mathematical reasoning capabilities. However, as with similar large language models, its responses may contain errors or biases. Therefore, outputs should be carefully evaluated, especially in applications where accuracy is critical, and additional verification steps are recommended.

Example Usage

Install

!pip install -U transformers bitsandbytes

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

model_id = "VolkanSimsir/LLaMA-3-8B-GRPO-math-tr"
tokenizer = AutoTokenizer.from_pretrained(model_id)

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype="float16",  
    bnb_4bit_quant_type="nf4",        
    bnb_4bit_use_double_quant=True
)


model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=quantization_config,
    device_map="auto"
)


question = """ Randy'nin çiftliğinde 60 mango ağacı var.
Ayrıca mango ağaçlarının yarısından 5 tane daha az Hindistan cevizi ağacı var.
Randy'nin çiftliğinde toplam kaç ağaç var?
"""

inputs = tokenizer(question, return_tensors="pt").to(0)
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Evaluation

Citation Information

@software{VolkanSimsir,
  author = {VolkanSimsir},
  title = {VolkanSimsir/LLaMA-3-8B-GRPO-math-tr},
  year = 2025,
  url = {https://huggingface.co/VolkanSimsir/LLaMA-3-8B-GRPO-math-tr}
}