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

Libraries

How to use DMindAI/DMind-3-mini with Transformers:

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

pipe = pipeline("text-generation", model="DMindAI/DMind-3-mini")
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-mini")
model = AutoModelForImageTextToText.from_pretrained("DMindAI/DMind-3-mini")
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-mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DMindAI/DMind-3-mini"
# 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-mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

yuzhe commited on Jan 11

Commit

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Update README.md

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README.md CHANGED Viewed

@@ -64,7 +64,7 @@ The DMind lineage was born from a singular conviction that decentralized finance
 **DMind-3-mini** introduces **Contrastive Chain-of-Correction Supervised Fine-Tuning (C³-SFT)**. Unlike standard SFT which models a direct mapping \\(P(y|x)\\), C³-SFT forces the model to navigate a Correction Trajectory by contrasting against plausible but flawed reasoning.
-![Figure 1: C3-SFT Paradigm](https://your-image-host-url/figure1-c3sft.png)
 *(Figure 1: The C³-SFT training pipeline, illustrating the Triplet Data Structure and Contextual Loss Masking)*
 ### Mathematical Formalization
@@ -105,7 +105,7 @@ DMind-3-mini is not just a coder. It is a **Financial Risk Assessor**.
 ### The Brain & Shield Ecosystem
 For maximum security, we recommend the **DMind Local Stack**:
-![Figure 2: Brain and Shield Ecosystem](https://your-image-host-url/figure2-ecosystem.png)
 *(Figure 2: The On-Device Inference Ecosystem showing the synergy between Nano and Mini)*
 * **The Brain (DMind-3-mini):** Runs on your high-performance laptop. Handles complex strategy formulation, deep research, and System 2 logic.

 **DMind-3-mini** introduces **Contrastive Chain-of-Correction Supervised Fine-Tuning (C³-SFT)**. Unlike standard SFT which models a direct mapping \\(P(y|x)\\), C³-SFT forces the model to navigate a Correction Trajectory by contrasting against plausible but flawed reasoning.
+![Figure 1: C3-SFT Paradigm](./Figures/Figure1.png)
 *(Figure 1: The C³-SFT training pipeline, illustrating the Triplet Data Structure and Contextual Loss Masking)*
 ### Mathematical Formalization
 ### The Brain & Shield Ecosystem
 For maximum security, we recommend the **DMind Local Stack**:
+![Figure 2: Brain and Shield Ecosystem](./Figures/Figure2.png)
 *(Figure 2: The On-Device Inference Ecosystem showing the synergy between Nano and Mini)*
 * **The Brain (DMind-3-mini):** Runs on your high-performance laptop. Handles complex strategy formulation, deep research, and System 2 logic.