Instructions to use microsoft/MAI-DS-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/MAI-DS-R1 with Transformers:

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

pipe = pipeline("text-generation", model="microsoft/MAI-DS-R1", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("microsoft/MAI-DS-R1", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("microsoft/MAI-DS-R1", trust_remote_code=True)
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 microsoft/MAI-DS-R1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/microsoft/MAI-DS-R1

SGLang

How to use microsoft/MAI-DS-R1 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 "microsoft/MAI-DS-R1" \
    --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": "microsoft/MAI-DS-R1",
		"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 "microsoft/MAI-DS-R1" \
        --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": "microsoft/MAI-DS-R1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use microsoft/MAI-DS-R1 with Docker Model Runner:
```
docker model run hf.co/microsoft/MAI-DS-R1
```

add AIBOM

#14

by sabato-nocera - opened Jun 14, 2025

base: refs/heads/main

←

from: refs/pr/14

Discussion Files changed

+66

-0

Files changed (1) hide show

microsoft_MAI-DS-R1.json +66 -0

microsoft_MAI-DS-R1.json ADDED Viewed

	@@ -0,0 +1,66 @@

+{
+    "bomFormat": "CycloneDX",
+    "specVersion": "1.6",
+    "serialNumber": "urn:uuid:0c1f0396-4043-4195-820e-4870a03c00ac",
+    "version": 1,
+    "metadata": {
+        "timestamp": "2025-06-05T09:40:31.549137+00:00",
+        "component": {
+            "type": "machine-learning-model",
+            "bom-ref": "microsoft/MAI-DS-R1-8d11dfdf-fb8a-57f6-bde0-49aa8aab572c",
+            "name": "microsoft/MAI-DS-R1",
+            "externalReferences": [
+                {
+                    "url": "https://huggingface.co/microsoft/MAI-DS-R1",
+                    "type": "documentation"
+                }
+            ],
+            "modelCard": {
+                "modelParameters": {
+                    "task": "text-generation",
+                    "architectureFamily": "deepseek_v3",
+                    "modelArchitecture": "DeepseekV3ForCausalLM"
+                },
+                "properties": [
+                    {
+                        "name": "library_name",
+                        "value": "transformers"
+                    },
+                    {
+                        "name": "base_model",
+                        "value": "deepseek-ai/DeepSeek-R1"
+                    }
+                ]
+            },
+            "authors": [
+                {
+                    "name": "microsoft"
+                }
+            ],
+            "licenses": [
+                {
+                    "license": {
+                        "id": "MIT",
+                        "url": "https://spdx.org/licenses/MIT.html"
+                    }
+                }
+            ],
+            "description": "MAI-DS-R1 is a DeepSeek-R1 reasoning model that has been post-trained by Microsoft AI team to fill in information gaps in the previous version of the model and to improve its risk profile, while maintaining R1 reasoning capabilities. The model was trained using 110k Safety and Non-Compliance examples from [Tulu](https://huggingface.co/datasets/allenai/tulu-3-sft-mixture) 3 SFT dataset, in addition to a dataset of ~350k multilingual examples internally developed capturing various topics with reported biases.MAI-DS-R1 has successfully unblocked the majority of previously blocked queries from the original R1 model while outperforming the recently published R1-1776 model (post-trained by Perplexity) in relevant safety benchmarks.  These results were achieved while preserving the general reasoning capabilities of the original DeepSeek-R1.*Please note: Microsoft has post-trained this model to address certain limitations relevant to its outputs, but previous limitations and considerations for the model remain, including security considerations.*",
+            "tags": [
+                "transformers",
+                "safetensors",
+                "deepseek_v3",
+                "text-generation",
+                "conversational",
+                "custom_code",
+                "base_model:deepseek-ai/DeepSeek-R1",
+                "base_model:finetune:deepseek-ai/DeepSeek-R1",
+                "license:mit",
+                "autotrain_compatible",
+                "text-generation-inference",
+                "endpoints_compatible",
+                "region:us"
+            ]
+        }
+    }
+}