Instructions to use knoveleng/Open-RS2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use knoveleng/Open-RS2 with Transformers:

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

pipe = pipeline("text-generation", model="knoveleng/Open-RS2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("knoveleng/Open-RS2")
model = AutoModelForCausalLM.from_pretrained("knoveleng/Open-RS2")
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 knoveleng/Open-RS2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "knoveleng/Open-RS2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "knoveleng/Open-RS2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/knoveleng/Open-RS2

SGLang

How to use knoveleng/Open-RS2 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 "knoveleng/Open-RS2" \
    --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": "knoveleng/Open-RS2",
		"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 "knoveleng/Open-RS2" \
        --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": "knoveleng/Open-RS2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use knoveleng/Open-RS2 with Docker Model Runner:
```
docker model run hf.co/knoveleng/Open-RS2
```

Add library_name metadata

by nielsr HF Staff - opened Mar 22, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-9

nielsr

Mar 22, 2025

This PR adds the library_name metadata to the model card, specifying that the model is compatible with the Hugging Face transformers library. This improves discoverability and allows users to easily integrate the model into their projects using the familiar transformers API.

Add library_name metadata26a96034

quyanh changed pull request status to merged Mar 24, 2025

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