Instructions to use Metin/LLaMA-3-8B-Math-Majority-Vote-GRPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Metin/LLaMA-3-8B-Math-Majority-Vote-GRPO with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Metin/LLaMA-3-8B-Math-Majority-Vote-GRPO")
model = AutoModelForCausalLM.from_pretrained("Metin/LLaMA-3-8B-Math-Majority-Vote-GRPO")
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

vLLM

How to use Metin/LLaMA-3-8B-Math-Majority-Vote-GRPO with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Metin/LLaMA-3-8B-Math-Majority-Vote-GRPO

SGLang

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

Unsloth Studio new

How to use Metin/LLaMA-3-8B-Math-Majority-Vote-GRPO 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 Metin/LLaMA-3-8B-Math-Majority-Vote-GRPO 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 Metin/LLaMA-3-8B-Math-Majority-Vote-GRPO to start chatting

Using HuggingFace Spaces for Unsloth

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

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Metin/LLaMA-3-8B-Math-Majority-Vote-GRPO",
    max_seq_length=2048,
)

Docker Model Runner
How to use Metin/LLaMA-3-8B-Math-Majority-Vote-GRPO with Docker Model Runner:
```
docker model run hf.co/Metin/LLaMA-3-8B-Math-Majority-Vote-GRPO
```

Improve model card: add pipeline tag, library name, language, license, paper, and code links

by nielsr HF Staff - opened Jul 1, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+15

-9

Files changed (1) hide show

README.md +15 -9

README.md CHANGED Viewed

@@ -1,5 +1,11 @@
 ---
 base_model: ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1
 tags:
 - text-generation-inference
 - transformers
@@ -8,9 +14,6 @@ tags:
 - trl
 - grpo
 - test-time-reinforcement-learning
-license: llama3
-language:
-- en
 ---
 <img src="https://huggingface.co/Metin/LLaMA-3-8B-Math-Majority-Vote-GRPO/resolve/main/llama_clones.png"
@@ -20,6 +23,9 @@ alt="A scene from a famous movie" width="800"/>
 Metin/LLaMA-3-8B-Math-Majority-Vote-GRPO is a [Test Time Reinforcement Learning (TTRL)](https://arxiv.org/abs/2504.16084) trained version of ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1. It is trained on Turkish math word problems using GRPO method and a majority vote reward function.
 ## Training Info
 - **Base Model**: [Turkish-Llama-8b-DPO-v0.1](https://huggingface.co/ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1)
@@ -88,11 +94,11 @@ print(result)
 ```
 # Citation
-```
-@article{Metin,
-  title={Metin/LLaMA-3-8B-Math-Majority-Vote-GRPO},
-  author={Metin Usta},
-  year={2024},
-  url={https://huggingface.co/Metin/LLaMA-3-8B-Math-Majority-Vote-GRPO}
 }
 ```

 ---
 base_model: ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1
+language:
+- en
+- tr
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
 tags:
 - text-generation-inference
 - transformers
 - trl
 - grpo
 - test-time-reinforcement-learning
 ---
 <img src="https://huggingface.co/Metin/LLaMA-3-8B-Math-Majority-Vote-GRPO/resolve/main/llama_clones.png"
 Metin/LLaMA-3-8B-Math-Majority-Vote-GRPO is a [Test Time Reinforcement Learning (TTRL)](https://arxiv.org/abs/2504.16084) trained version of ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1. It is trained on Turkish math word problems using GRPO method and a majority vote reward function.
+**Paper:** [TTRL: Test-Time Reinforcement Learning](https://huggingface.co/papers/2504.16084)
+**Code:** [https://github.com/PRIME-RL/TTRL](https://github.com/PRIME-RL/TTRL)
 ## Training Info
 - **Base Model**: [Turkish-Llama-8b-DPO-v0.1](https://huggingface.co/ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1)
 ```
 # Citation
+```bibtex
+@article{zuo2025ttrl,
+  title={Ttrl: Test-time reinforcement learning},
+  author={Zuo, Yuxin and Zhang, Kaiyan and Qu, Shang and Sheng, Li and Zhu, Xuekai and Qi, Biqing and Sun, Youbang and Cui, Ganqu and Ding, Ning and Zhou, Bowen},
+  journal={arXiv preprint arXiv:2504.16084},
+  year={2025}
 }
 ```