Instructions to use sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP with Transformers:

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

pipe = pipeline("text-generation", model="sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP")
model = AutoModelForCausalLM.from_pretrained("sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP", 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 sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP

SGLang

How to use sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP 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 "sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP" \
    --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": "sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP",
		"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 "sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP" \
        --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": "sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP with Docker Model Runner:
```
docker model run hf.co/sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP
```

OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP / README.md

Xuandong

Improve model card metadata and add paper/code links (#1)

0782f14 5 months ago

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	---
	base_model:
	- allenai/OLMo-2-1124-7B-SFT
	datasets:
	- math
	language:
	- en
	license: apache-2.0
	metrics:
	- accuracy
	pipeline_tag: text-generation
	library_name: transformers
	---

	# OLMo-2-7B-SFT-GRPO-MATH-1EPOCH-SYSP

	Description:

	A GRPO-fine-tuned version of [allenai/OLMo-2-1124-7B-SFT](https://huggingface.co/allenai/OLMo-2-1124-7B-SFT) trained on the MATH dataset with a system prompt.

	This model was developed as part of the research presented in the paper [Learning to Reason without External Rewards](https://huggingface.co/papers/2505.19590). It utilizes the Intuitor method, an instantiation of Reinforcement Learning from Internal Feedback (RLIF), which enables models to learn from intrinsic signals like self-certainty without requiring external rewards or labeled gold solutions.

	## Resources
	- Paper: [Learning to Reason without External Rewards](https://huggingface.co/papers/2505.19590)
	- Repository: [sunblaze-ucb/Intuitor](https://github.com/sunblaze-ucb/Intuitor)

	---

	## Citation

	```bibtex
	@article{zhao2025learning,
	title={Learning to Reason without External Rewards},
	author={Zhao, Xuandong and Kang, Zhewei and Feng, Aosong and Levine, Sergey and Song, Dawn},
	journal={arXiv preprint arXiv:2505.19590},
	year={2025}
	}
	```