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
```

MiniMind

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# MiniMind Max2 - Efficient Edge LLM
# Docker Hub: sultanafariabd/minimind-max2

FROM python:3.11-slim

LABEL maintainer="MiniMind Team <contact@minimind.ai>"
LABEL org.opencontainers.image.title="MiniMind Max2"
LABEL org.opencontainers.image.description="Efficient LLM with MoE (8 experts, 25% activation) + GQA"
LABEL org.opencontainers.image.version="1.0.0"
LABEL org.opencontainers.image.source="https://huggingface.co/fariasultana/MiniMind"
LABEL org.opencontainers.image.licenses="Apache-2.0"
LABEL ai.model.architecture="MoE+GQA"
LABEL ai.model.parameters="500M-3B"
LABEL ai.model.active_ratio="25%"

ENV PYTHONUNBUFFERED=1
ENV MODEL_VARIANT=max2-nano
ENV PORT=8000

WORKDIR /app

# Install dependencies
RUN pip install --no-cache-dir \
    torch>=2.1.0 \
    numpy>=1.24.0 \
    fastapi>=0.100.0 \
    uvicorn>=0.23.0 \
    safetensors>=0.4.0 \
    pydantic>=2.0.0

# Copy application
COPY serve.py /app/
COPY model_info.json /app/

EXPOSE 8000

HEALTHCHECK --interval=30s --timeout=10s --start-period=30s \
    CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')" || exit 1

CMD ["python", "serve.py"]