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 Jan 14, 2025

Commit

fb79037

verified ·

1 Parent(s): d5462e5

update: fix chat template

Browse files

Files changed (1) hide show

tokenizer_config.json +1 -1

tokenizer_config.json CHANGED Viewed

@@ -195,7 +195,7 @@
     "<|video_pad|>"
   ],
   "bos_token": null,
-  "chat_template": "{% set system_message = '\nWhen tackling complex reasoning tasks, you have access to the following actions. Use them as needed to progress through your thought process.\n\n[ASSESS]\n\n[ADVANCE]\n\n[VERIFY]\n\n[SIMPLIFY]\n\n[SYNTHESIZE]\n\n[PIVOT]\n\n[OUTPUT]\n\nYou should strictly follow the format below:\n\n[ACTION NAME]\n\n# Your action step 1\n\n# Your action step 2\n\n# Your action step 3\n\n...\n\nNext action: [NEXT ACTION NAME]\n' %}{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ '<|im_start|>system\n' + system_message + '<|im_end|>\n' }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\n' + content + '<|im_end|>\n<|im_start|>assistant\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '\n' }}{% endif %}{% endfor %}",
   "clean_up_tokenization_spaces": false,
   "eos_token": "<|im_end|>",
   "errors": "replace",

     "<|video_pad|>"
   ],
   "bos_token": null,
+  "chat_template": "{% set system_message = '\\\\nWhen tackling complex reasoning tasks, you have access to the following actions. Use them as needed to progress through your thought process.\\\\n\\\\n[ASSESS]\\\\n\\\\n[ADVANCE]\\\\n\\\\n[VERIFY]\\\\n\\\\n[SIMPLIFY]\\\\n\\\\n[SYNTHESIZE]\\\\n\\\\n[PIVOT]\\\\n\\\\n[OUTPUT]\\\\n\\\\nYou should strictly follow the format below:\\\\n\\\\n[ACTION NAME]\\\\n\\\\n# Your action step 1\\\\n\\\\n# Your action step 2\\\\n\\\\n# Your action step 3\\\\n\\\\n...\\\\n\\\\nNext action: [NEXT ACTION NAME]\\\\n' %}{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ '<|im_start|>system\n' + system_message + '<|im_end|>\n' }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\n' + content + '<|im_end|>\n<|im_start|>assistant\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '\n' }}{% endif %}{% endfor %}",
   "clean_up_tokenization_spaces": false,
   "eos_token": "<|im_end|>",
   "errors": "replace",