Instructions to use Lucasgitguy-co/minimind-mermaid-run2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lucasgitguy-co/minimind-mermaid-run2 with Transformers:

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

pipe = pipeline("text-generation", model="Lucasgitguy-co/minimind-mermaid-run2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Lucasgitguy-co/minimind-mermaid-run2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Lucasgitguy-co/minimind-mermaid-run2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lucasgitguy-co/minimind-mermaid-run2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lucasgitguy-co/minimind-mermaid-run2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Lucasgitguy-co/minimind-mermaid-run2

SGLang

How to use Lucasgitguy-co/minimind-mermaid-run2 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 "Lucasgitguy-co/minimind-mermaid-run2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lucasgitguy-co/minimind-mermaid-run2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Lucasgitguy-co/minimind-mermaid-run2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lucasgitguy-co/minimind-mermaid-run2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Lucasgitguy-co/minimind-mermaid-run2 with Docker Model Runner:
```
docker model run hf.co/Lucasgitguy-co/minimind-mermaid-run2
```

minimind-mermaid-run2

MiniMind Mermaid run 2 checkpoint collection. The repository stores separate ep1, ep2, and ep3 folders, each with model weights, configuration, and tokenizer files.

File	Size (decimal GB)
`ep1/model.safetensors`	0.256
`ep2/model.safetensors`	0.256
`ep3/model.safetensors`	0.256

Load a checkpoint

The inspected ep3/config.json declares Qwen3ForCausalLM, float16 weights, 8 hidden layers, hidden size 768, and vocabulary size 6,400. This export is a text-generation model; no vision projector is included.

from transformers import AutoTokenizer, AutoModelForCausalLM

repo = "Lucasgitguy-co/minimind-mermaid-run2"
checkpoint = "ep3"  # Select ep1, ep2, or ep3 explicitly.
tokenizer = AutoTokenizer.from_pretrained(repo, subfolder=checkpoint)
model = AutoModelForCausalLM.from_pretrained(
    repo, subfolder=checkpoint, torch_dtype="auto"
)

The export records Transformers 4.57.6. ep3 is an example selection, not a documented best checkpoint.

Evaluation and provenance

This card describes the repository files and configuration. Detailed training data, training procedure, and benchmark results are not documented in the inspected artifacts. Loading examples are guidance, not a claim of measured model quality.

Downloads last month: -; Downloads are not tracked for this model. How to track