Instructions to use hrithik-dev8/tiny-random-MiniCPM-o-2_6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hrithik-dev8/tiny-random-MiniCPM-o-2_6 with Transformers:

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

pipe = pipeline("image-text-to-text", model="hrithik-dev8/tiny-random-MiniCPM-o-2_6", trust_remote_code=True)
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("hrithik-dev8/tiny-random-MiniCPM-o-2_6", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hrithik-dev8/tiny-random-MiniCPM-o-2_6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hrithik-dev8/tiny-random-MiniCPM-o-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": "hrithik-dev8/tiny-random-MiniCPM-o-2_6",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/hrithik-dev8/tiny-random-MiniCPM-o-2_6

SGLang

How to use hrithik-dev8/tiny-random-MiniCPM-o-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 "hrithik-dev8/tiny-random-MiniCPM-o-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": "hrithik-dev8/tiny-random-MiniCPM-o-2_6",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "hrithik-dev8/tiny-random-MiniCPM-o-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": "hrithik-dev8/tiny-random-MiniCPM-o-2_6",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use hrithik-dev8/tiny-random-MiniCPM-o-2_6 with Docker Model Runner:
```
docker model run hf.co/hrithik-dev8/tiny-random-MiniCPM-o-2_6
```

Tiny Random MiniCPM-o-2_6

A tiny (~42 MB) randomly-initialized version of MiniCPM-o-2.6 designed for testing purposes in the optimum-intel library.

Purpose

This model was created to replace the existing test model at optimum-intel-internal-testing/tiny-random-MiniCPM-o-2_6 (185 MB) with a smaller alternative for CI/CD testing. Smaller test models reduce:

Download times in CI pipelines
Storage requirements
Test execution time

Size Comparison

Model	Total Size	Model Weights
openbmb/MiniCPM-o-2_6 (Original)	17.4 GB	~17 GB
optimum-intel-internal-testing/tiny-random-MiniCPM-o-2_6 (Current Test Model)	185 MB	169 MB
hrithik-dev8/tiny-random-MiniCPM-o-2_6 (This Model)	~42 MB	41.55 MB

Result: 4× smaller than Intel's current test model

Model Configuration

Component	This Model	Original
Vocabulary	5,000 tokens	151,700 tokens
LLM Hidden Size	128	3,584
LLM Layers	1	40
LLM Attention Heads	8	28
Vision Hidden Size	128	1,152
Vision Layers	1	27
Image Size	980 (preserved)	980
Patch Size	14 (preserved)	14
Audio d_model	64	1,280
TTS Hidden Size	128	-

Parameter Breakdown

Component	Parameters	Size (MB)
TTS/DVAE	19,339,766	36.89
LLM	1,419,840	2.71
Vision	835,328	1.59
Resampler	91,392	0.17
Audio	56,192	0.11
Other	20,736	0.04
Total	21,763,254	~41.5

Technical Details

Why Keep TTS/DVAE Components?

The TTS (Text-to-Speech) component, which includes the DVAE (Discrete Variational Auto-Encoder), accounts for approximately 37 MB (~85%) of the model size. While the optimum-intel tests do not exercise TTS functionality (they only test image+text → text generation), we retain this component because:

Structural Consistency: Removing TTS via init_tts=False causes structural differences in the model that lead to numerical divergence between PyTorch and OpenVINO outputs
Test Compatibility: The test_compare_to_transformers test compares PyTorch vs OpenVINO outputs and requires exact structural matching
Architecture Integrity: The MiniCPM-o architecture expects TTS weights to be present during model loading

Tokenizer Shrinking

The vocabulary was reduced from 151,700 to 5,000 tokens:

Base tokens: IDs 0-4899 (first 4,900 most common tokens)
Special tokens: IDs 4900-4949 (remapped from original high IDs)
BPE merges: Filtered from 151,387 to 4,644 (only merges involving retained tokens)

Key token mappings:

Token	ID
`<unk>`	4900
`<\|endoftext\|>`	4901
`<\|im_start\|>`	4902
`<\|im_end\|>`	4903

Reproducibility

Model weights are initialized with a fixed random seed (42) to ensure:

Reproducible outputs between runs
Consistent behavior between PyTorch and OpenVINO
Passing of test_compare_to_transformers which compares framework outputs

Test Results

Tested with pytest tests/openvino/test_seq2seq.py -k "minicpmo" -v:

Test	Status	Notes
`test_compare_to_transformers`	✅ PASSED	PyTorch/OpenVINO outputs match
`test_generate_utils`	✅ PASSED	Generation pipeline works
`test_model_can_be_loaded_after_saving`	⚠️ FAILED	Windows file locking issue (not model-related)

The third test failure is a Windows-specific issue where OpenVINO keeps file handles open, preventing cleanup of temporary directories. This is a known platform limitation, not a model defect. The test passes on Linux/macOS.

Usage

For optimum-intel Testing

# In optimum-intel/tests/openvino/utils_tests.py, update MODEL_NAMES:
MODEL_NAMES = {
    # ... other models ...
    "minicpmo": "hrithik-dev8/tiny-random-MiniCPM-o-2_6",
}

Then run tests:

pytest tests/openvino/test_seq2seq.py -k "minicpmo" -v

Basic Model Loading

from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained(
    "hrithik-dev8/tiny-random-MiniCPM-o-2_6",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
    "hrithik-dev8/tiny-random-MiniCPM-o-2_6",
    trust_remote_code=True
)

Files Included

File	Size	Description
`model.safetensors`	41.55 MB	Model weights (bfloat16)
`config.json`	5.33 KB	Model configuration
`tokenizer.json`	338.27 KB	Shrunk tokenizer (5,000 tokens)
`tokenizer_config.json`	12.78 KB	Tokenizer settings
`vocab.json`	85.70 KB	Vocabulary mapping
`merges.txt`	36.58 KB	BPE merge rules
`preprocessor_config.json`	1.07 KB	Image processor config
`generation_config.json`	121 B	Generation settings
`added_tokens.json`	1.13 KB	Special tokens
`special_tokens_map.json`	1.24 KB	Special token mappings

Requirements

Python 3.8+
transformers >= 4.45.0, < 4.52.0
torch
For OpenVINO testing: optimum-intel with OpenVINO backend

Limitations

⚠️ This model is for testing only - it produces random/meaningless outputs and should not be used for inference.

Downloads last month: 51

Safetensors

Model size

21.8M params

Tensor type

BF16

Model tree for hrithik-dev8/tiny-random-MiniCPM-o-2_6

Base model

openbmb/MiniCPM-o-2_6

Finetuned

(8)

this model