Instructions to use gaotang/RM-R1-Qwen2.5-Instruct-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gaotang/RM-R1-Qwen2.5-Instruct-14B 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-14B")
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-14B")
model = AutoModelForCausalLM.from_pretrained("gaotang/RM-R1-Qwen2.5-Instruct-14B")
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

vLLM

How to use gaotang/RM-R1-Qwen2.5-Instruct-14B 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-14B"
# 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-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gaotang/RM-R1-Qwen2.5-Instruct-14B

SGLang

How to use gaotang/RM-R1-Qwen2.5-Instruct-14B 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-14B" \
    --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-14B",
		"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-14B" \
        --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-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gaotang/RM-R1-Qwen2.5-Instruct-14B with Docker Model Runner:
```
docker model run hf.co/gaotang/RM-R1-Qwen2.5-Instruct-14B
```

Add pipeline tag and library name, add usage example and missing sections

by nielsr HF Staff - opened May 7, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+52

-5

nielsr

May 7, 2025

This PR improves the model card's metadata by adding the pipeline_tag and library_name. This improves discoverability on the Hugging Face Hub, particularly for users searching for text ranking models and those using the Transformers library. Also, a code snippet demonstrating basic usage has been added, and the sections "Training", "Evaluation", "Use Our Model", "Build Your Own Dataset", "Features", "Acknowledgement" and "Citations" from the Github README were added to the model card.

Add pipeline tag and library name, add usage example and missing sections42b4aaf4

gaotang

Owner May 7, 2025

Thank you very much! This model card will be continuously updated.

gaotang changed pull request status to merged May 7, 2025

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