Instructions to use OrionLLM/GRM-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OrionLLM/GRM-1.5b with Transformers:

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

pipe = pipeline("text-generation", model="OrionLLM/GRM-1.5b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OrionLLM/GRM-1.5b")
model = AutoModelForCausalLM.from_pretrained("OrionLLM/GRM-1.5b")
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 OrionLLM/GRM-1.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OrionLLM/GRM-1.5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OrionLLM/GRM-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OrionLLM/GRM-1.5b

SGLang

How to use OrionLLM/GRM-1.5b 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 "OrionLLM/GRM-1.5b" \
    --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": "OrionLLM/GRM-1.5b",
		"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 "OrionLLM/GRM-1.5b" \
        --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": "OrionLLM/GRM-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OrionLLM/GRM-1.5b with Docker Model Runner:
```
docker model run hf.co/OrionLLM/GRM-1.5b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

A newer version of this model is available: OrionLLM/GRM-2.6-Plus

logo

GRM-1.5b is a general-purpose reasoning-focused 1.5B model fine-tuned to improve multi-domain reasoning (math, logic, coding, and broad problem-solving). It is designed to be a strong, lightweight “daily driver” for general reasoning tasks and as a solid base for further fine-tuning.

Key features

Dedicated reasoning behavior for general tasks (stepwise problem solving, better consistency).
Small & efficient (1.5B) — practical for local inference and experimentation.
Multi-domain mixture: reasoning + code + math + (some) medical reasoning data.
Fine-tune friendly: intended as a good starting point for your own SFT/GRPO/DPO pipelines.

Benchmarks

Model	AIME24	AIME25	AMC23	MATH500	HMMT O2/25	LCB 06/24-01/25	CodeElo	CodeForces	GPQA-D	JEEBench
GRM-1.5b	52.0	41.7	87.0	86.4	27.3	39.4	12.9	15.5	29.5	51.9
DeepSeek-R1-Distill-Qwen-1.5B	32.3	23.7	71.8	80.8	15.3	27.2	8.8	8.5	31.1	32.5
Nemotron-Research-Reasoning-Qwen-1.5B	47.7	32.0	87.5	86.0	21.7	31.4	54.7	40.3	41.8	52.6
Qwen3-1.7B	52.0	35.3	83.8	87.2	23.3	27.7	20.7	20.0	49.3	60.7
Qwen2.5-1.5B-Instruct	3.0	0.7	30.8	50.2	0.0	5.5	0.8	2.2	24.7	16.4