Instructions to use gaotang/RM-R1-Qwen2.5-Instruct-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gaotang/RM-R1-Qwen2.5-Instruct-32B with Transformers:

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

pipe = pipeline("text-generation", model="gaotang/RM-R1-Qwen2.5-Instruct-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gaotang/RM-R1-Qwen2.5-Instruct-32B")
model = AutoModelForCausalLM.from_pretrained("gaotang/RM-R1-Qwen2.5-Instruct-32B")
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 gaotang/RM-R1-Qwen2.5-Instruct-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gaotang/RM-R1-Qwen2.5-Instruct-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gaotang/RM-R1-Qwen2.5-Instruct-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gaotang/RM-R1-Qwen2.5-Instruct-32B

SGLang

How to use gaotang/RM-R1-Qwen2.5-Instruct-32B 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 "gaotang/RM-R1-Qwen2.5-Instruct-32B" \
    --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": "gaotang/RM-R1-Qwen2.5-Instruct-32B",
		"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 "gaotang/RM-R1-Qwen2.5-Instruct-32B" \
        --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": "gaotang/RM-R1-Qwen2.5-Instruct-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gaotang/RM-R1-Qwen2.5-Instruct-32B with Docker Model Runner:
```
docker model run hf.co/gaotang/RM-R1-Qwen2.5-Instruct-32B
```

gaotang commited on May 6, 2025

Commit

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+![image/png](https://cdn-uploads.huggingface.co/production/uploads/654d784d71a30c4bca09a319/Q7MVJfIHDerQ24c1zwZwK.png)
+<font size=3><div align='center' >
+[[**🤗 Model & Dataset**](https://huggingface.co/collections/gaotang/rm-r1-681128cdab932701cad844c8)]
+[[**📊 Code**](https://github.com/RM-R1-UIUC/RM-R1)]
+[[**📖 Paper**](https://arxiv.org/abs/2505.02387)]
+</div></font>
+# 🚀 Can we cast reward modeling as a reasoning task?
+**RM-R1** is a training framework for *Reasoning Reward Model* (ReasRM) that judges two candidate answers by first **thinking out loud**—generating rubrics or reasoning traces—then emitting its preference.
+Compared with prior scalar or vanilla generative reward models, RM-R1 delivers up to **+13.8 % absolute accuracy gains** on public reward model benchmarks while providing *fully interpretable* critiques.
+## TL;DR
+* **Two-stage training**
+  1. **Distillation** of ~8.7 K high-quality reasoning traces (Chain-of-Rubrics).
+  2. **Reinforcement Learning with Verifiable Rewards** (RLVR) on ~64 K preference pairs.
+* **Backbones** released: 7 B / 14 B / 32 B Qwen-2.5-Instruct variants + DeepSeek-distilled checkpoints.
+## Intended uses
+* **RLHF / RLAIF**: plug-and-play reward function for policy optimisation.
+* **Automated evaluation**: LLM-as-a-judge for open-domain QA, chat, and reasoning.
+* **Research**: study process supervision, chain-of-thought verification, or rubric generation.