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 25

Commit

90afe36

verified ·

1 Parent(s): 78dd4ce

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -9

README.md CHANGED Viewed

@@ -124,16 +124,9 @@ The model was fine-tuned on **82,000** high-value private samples:
 ## 6. 🏆 Performance Benchmarks
-Evaluated on **Web3-Finance-Eval-2026**:
-| Metric | DMind-3-mini (4B) | Llama-3-70B | GPT-4o (General) |
-| :--- | :---: | :---: | :---: |
-| **Ponzi Logic Detection** | **96.4%** | 78.2% | 85.5% |
-| **Impermanent Loss Calc** | **99.1%** | 85.0% | 92.3% |
-| **Contract Exploit ID** | **92.3%** | 88.5% | 89.1% |
-| **Inference Cost** | **Local (Free)** | High | High |
-*DMind-3-mini outperforms generalist models 15x its size in specific vertical tasks, validating the C³-SFT approach.*
 ## 7. ⚖️ Limitations & Disclaimer

 ## 6. 🏆 Performance Benchmarks
+Evaluated on three key benchmarks: DMind Benchmark (Web3 Native Logic), FinanceQA (Financial Domain Knowledge), and AIME 2025 (Advanced Mathematical Reasoning).
+The evaluation compares DMind-3-mini (4B) against top-tier frontier models (GPT-5.1, Claude Sonnet 4.5) and other efficient models. Despite its compact size, the Mini model demonstrates exceptional efficiency, particularly in specialized domain tasks where it outperforms significantly larger generalist models.
 ## 7. ⚖️ Limitations & Disclaimer