Instructions to use PRIME-RL/Eurus-2-7B-PRIME with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PRIME-RL/Eurus-2-7B-PRIME with Transformers:

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

pipe = pipeline("text-generation", model="PRIME-RL/Eurus-2-7B-PRIME")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PRIME-RL/Eurus-2-7B-PRIME")
model = AutoModelForCausalLM.from_pretrained("PRIME-RL/Eurus-2-7B-PRIME")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use PRIME-RL/Eurus-2-7B-PRIME with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/PRIME-RL/Eurus-2-7B-PRIME

SGLang

How to use PRIME-RL/Eurus-2-7B-PRIME 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 "PRIME-RL/Eurus-2-7B-PRIME" \
    --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": "PRIME-RL/Eurus-2-7B-PRIME",
		"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 "PRIME-RL/Eurus-2-7B-PRIME" \
        --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": "PRIME-RL/Eurus-2-7B-PRIME",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PRIME-RL/Eurus-2-7B-PRIME with Docker Model Runner:
```
docker model run hf.co/PRIME-RL/Eurus-2-7B-PRIME
```

hanbin commited on Feb 4, 2025

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@@ -139,10 +139,14 @@ We achieved this with only 1/10 data and model resources compared with Qwen-Math
 ## Citation
 ```latex
-@misc{cui2024process,
-  title={Process Reinforcement through Implicit Rewards},
-  author={Ganqu Cui and Lifan Yuan and Zefan Wang and Hanbin Wang and Wendi Li and Bingxiang He and Yuchen Fan and Tianyu Yu and Qixin Xu and Weize Chen and Jiarui Yuan and Huayu Chen and Kaiyan Zhang and Xingtai Lv and Shuo Wang and Yuan Yao and Hao Peng and Yu Cheng and Zhiyuan Liu and Maosong Sun and Bowen Zhou and Ning Ding},
-  year={2025}
 }
 ```

 ## Citation
 ```latex
+@misc{cui2025processreinforcementimplicitrewards,
+      title={Process Reinforcement through Implicit Rewards},
+      author={Ganqu Cui and Lifan Yuan and Zefan Wang and Hanbin Wang and Wendi Li and Bingxiang He and Yuchen Fan and Tianyu Yu and Qixin Xu and Weize Chen and Jiarui Yuan and Huayu Chen and Kaiyan Zhang and Xingtai Lv and Shuo Wang and Yuan Yao and Xu Han and Hao Peng and Yu Cheng and Zhiyuan Liu and Maosong Sun and Bowen Zhou and Ning Ding},
+      year={2025},
+      eprint={2502.01456},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2502.01456},
 }
 ```