Instructions to use umaimaparveennineteen/tiny-random-MiniCPM-0-2_6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use umaimaparveennineteen/tiny-random-MiniCPM-0-2_6 with Transformers:

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

pipe = pipeline("text-generation", model="umaimaparveennineteen/tiny-random-MiniCPM-0-2_6", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("umaimaparveennineteen/tiny-random-MiniCPM-0-2_6", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use umaimaparveennineteen/tiny-random-MiniCPM-0-2_6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "umaimaparveennineteen/tiny-random-MiniCPM-0-2_6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "umaimaparveennineteen/tiny-random-MiniCPM-0-2_6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/umaimaparveennineteen/tiny-random-MiniCPM-0-2_6

SGLang

How to use umaimaparveennineteen/tiny-random-MiniCPM-0-2_6 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 "umaimaparveennineteen/tiny-random-MiniCPM-0-2_6" \
    --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": "umaimaparveennineteen/tiny-random-MiniCPM-0-2_6",
		"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 "umaimaparveennineteen/tiny-random-MiniCPM-0-2_6" \
        --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": "umaimaparveennineteen/tiny-random-MiniCPM-0-2_6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use umaimaparveennineteen/tiny-random-MiniCPM-0-2_6 with Docker Model Runner:
```
docker model run hf.co/umaimaparveennineteen/tiny-random-MiniCPM-0-2_6
```

Tiny-Random MiniCPM-o-2_6 for Testing

This is a minimal, randomly initialized version of the MiniCPM-o-2_6 architecture. It is designed specifically for CI/CD testing and unit tests in libraries like optimum-intel.

Model Details

Architecture: MiniCPMForCausalLM
Hidden Size: 24
Layers: 1
Precision: BF16
Total Size: ~5.9 MB (under the 6MB requirement)

Intended Use

This model is NOT for actual inference or text generation. It is used to verify that integration pipelines (like OpenVINO conversion and quantization) are working correctly without the need to download several gigabytes of data.

Verification

The model has been verified to:

Load via AutoModelForCausalLM.
Perform a successful forward pass.
Fit within the strict 6MB size limit for lightweight testing suites.

Downloads last month: 8

Safetensors

Model size

2.95M params

Tensor type

BF16