Instructions to use ArliAI/DS-R1-Distill-70B-ArliAI-RpR-v4-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ArliAI/DS-R1-Distill-70B-ArliAI-RpR-v4-Large with Transformers:

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

pipe = pipeline("text-generation", model="ArliAI/DS-R1-Distill-70B-ArliAI-RpR-v4-Large")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ArliAI/DS-R1-Distill-70B-ArliAI-RpR-v4-Large")
model = AutoModelForCausalLM.from_pretrained("ArliAI/DS-R1-Distill-70B-ArliAI-RpR-v4-Large")
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 ArliAI/DS-R1-Distill-70B-ArliAI-RpR-v4-Large with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ArliAI/DS-R1-Distill-70B-ArliAI-RpR-v4-Large"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArliAI/DS-R1-Distill-70B-ArliAI-RpR-v4-Large",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ArliAI/DS-R1-Distill-70B-ArliAI-RpR-v4-Large

SGLang

How to use ArliAI/DS-R1-Distill-70B-ArliAI-RpR-v4-Large 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 "ArliAI/DS-R1-Distill-70B-ArliAI-RpR-v4-Large" \
    --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": "ArliAI/DS-R1-Distill-70B-ArliAI-RpR-v4-Large",
		"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 "ArliAI/DS-R1-Distill-70B-ArliAI-RpR-v4-Large" \
        --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": "ArliAI/DS-R1-Distill-70B-ArliAI-RpR-v4-Large",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ArliAI/DS-R1-Distill-70B-ArliAI-RpR-v4-Large with Docker Model Runner:
```
docker model run hf.co/ArliAI/DS-R1-Distill-70B-ArliAI-RpR-v4-Large
```

OwenArli commited on Jun 7, 2025

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@@ -172,4 +172,4 @@ This is different from training a model in a particular domain and needing the m
 ## Try It Out!
-Model preference is subjective, so please do try Qwen3-30B-A3B-ArliAI-RpR-v4 for yourself. Your feedback both good and bad is always valueable and will help us improve the future RPMax and RpR models.


172
173	## Try It Out!
174
175	+ Model preference is subjective, so please do try this model for yourself. Your feedback both good and bad is always valueable and will help us improve the future RPMax and RpR models.