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
```

RM-R1-Qwen2.5-Instruct-32B / README.md

nielsr HF Staff

Add library_name and pipeline_tag metadata

c1b1763 verified about 1 year ago

1.58 kB

base_model:
  - Qwen/Qwen2.5-32B-Instruct
language:
  - en
license: mit
pipeline_tag: text-ranking
library_name: transformers

[🤗 Model & Dataset] [📊 Code] [📖 Paper]

🚀 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.