Instructions to use DMindAI/DMind-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DMindAI/DMind-3 with Transformers:

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

pipe = pipeline("text-generation", model="DMindAI/DMind-3")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("DMindAI/DMind-3")
model = AutoModelForImageTextToText.from_pretrained("DMindAI/DMind-3")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DMindAI/DMind-3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DMindAI/DMind-3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DMindAI/DMind-3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DMindAI/DMind-3

SGLang

How to use DMindAI/DMind-3 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 "DMindAI/DMind-3" \
    --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": "DMindAI/DMind-3",
		"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 "DMindAI/DMind-3" \
        --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": "DMindAI/DMind-3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DMindAI/DMind-3 with Docker Model Runner:
```
docker model run hf.co/DMindAI/DMind-3
```

yuzhe commited on Mar 23

Commit

de117c4

verified ·

1 Parent(s): 28692fa

Fix DMind-3 base model tag

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -19,7 +19,7 @@ metrics:
 pipeline_tag: text-generation
 inference: false
 base_model:
-- openai/gpt-oss-20b
 ---
 > [!IMPORTANT]
@@ -153,4 +153,4 @@ The evaluation compares DMind-3 (21B) against top-tier frontier models (GPT-5.1,
 - **Probabilistic Nature**: All forecasts are probabilistic and based on the data available up to the knowledge cutoff. The model cannot predict black swan events and is subject to the inherent unpredictability of markets.
 - **Knowledge Cutoff**: The core model has a knowledge cutoff of June 2025. While it can process real-time data provided via the API, its foundational understanding is based on its training corpus.
----

 pipeline_tag: text-generation
 inference: false
 base_model:
+- Qwen/Qwen3.5-27B
 ---
 > [!IMPORTANT]
 - **Probabilistic Nature**: All forecasts are probabilistic and based on the data available up to the knowledge cutoff. The model cannot predict black swan events and is subject to the inherent unpredictability of markets.
 - **Knowledge Cutoff**: The core model has a knowledge cutoff of June 2025. While it can process real-time data provided via the API, its foundational understanding is based on its training corpus.
+---