Instructions to use fariasultana/MiniMind with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fariasultana/MiniMind with Transformers:

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

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

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("fariasultana/MiniMind", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use fariasultana/MiniMind with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/fariasultana/MiniMind

SGLang

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

Docker Model Runner
How to use fariasultana/MiniMind with Docker Model Runner:
```
docker model run hf.co/fariasultana/MiniMind
```

🚀 Introducing MiniMind Max2 - Efficient LLMs for Edge Devices

by fariasultana - opened Dec 4, 2025

Discussion

fariasultana

Owner Dec 4, 2025

MiniMind Max2 is Here! 🎉

We're excited to release MiniMind Max2, a family of efficient language models designed for edge deployment!

Key Features

Only 25% parameters activated per token using Mixture of Experts
75% memory savings with Grouped Query Attention (4:1 ratio)
Mobile-ready: Runs on smartphones, tablets, and IoT devices
Easy export: ONNX, GGUF (llama.cpp), Android NDK

Model Sizes

Model	Total	Active	INT4 Size
max2-nano	500M	125M	~300MB
max2-lite	1.5B	375M	~900MB
max2-pro	3B	750M	~1.8GB

Try It Now!

🎮 Demo: MiniMind-API Space
📖 Documentation: Check the README for full details

We'd love your feedback! Let us know what you think. 🙏

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