Instructions to use openbmb/MiniCPM-V-4.7-1B-DSpark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openbmb/MiniCPM-V-4.7-1B-DSpark with Transformers:

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

pipe = pipeline("text-generation", model="openbmb/MiniCPM-V-4.7-1B-DSpark")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("openbmb/MiniCPM-V-4.7-1B-DSpark")
model = AutoModel.from_pretrained("openbmb/MiniCPM-V-4.7-1B-DSpark", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openbmb/MiniCPM-V-4.7-1B-DSpark with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openbmb/MiniCPM-V-4.7-1B-DSpark"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openbmb/MiniCPM-V-4.7-1B-DSpark",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/openbmb/MiniCPM-V-4.7-1B-DSpark

SGLang

How to use openbmb/MiniCPM-V-4.7-1B-DSpark 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 "openbmb/MiniCPM-V-4.7-1B-DSpark" \
    --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": "openbmb/MiniCPM-V-4.7-1B-DSpark",
		"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 "openbmb/MiniCPM-V-4.7-1B-DSpark" \
        --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": "openbmb/MiniCPM-V-4.7-1B-DSpark",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use openbmb/MiniCPM-V-4.7-1B-DSpark with Docker Model Runner:
```
docker model run hf.co/openbmb/MiniCPM-V-4.7-1B-DSpark
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MiniCPM-V-4.7-1B-DSpark

MiniCPM-V-4.7-1B-DSpark is a DSpark draft checkpoint trained for exact pairing with MiniCPM-V-4.7-1B and its tokenizer.

The llama.cpp version please see: MiniCPM-V-4.7-1B-DSpark-gguf

Model Specification

Property	Value
Target model	MiniCPM-V-4.7-1B
Draft layers	5
Draft parameters	213,547,777
Draft tokens per forward pass	15
Target hidden-state layers	`[3, 7, 11, 15, 19]`
Checkpoint precision	BF16

Training Recipe

Training responses were generated by MiniCPM-V-4.7-1B from a mixture of Vision & Text prompts.

Setting	Value
Training sequences	2M
Epochs	6
Optimizer	AdamW
Objective	CE + L1 + confidence loss

Evaluation

Acceptance length is defined as the total number of completion tokens divided by the total number of speculative verification steps. Evaluation uses natural EOS termination and max_new_tokens=8192.

Domain	Accept Length
Vision
MMMU	3.452
MathVista	4.333
CharXiv_reasoning_val	4.452
OCRBench	4.752
Text
Math	5.891
Code	5.347
General	3.370

Usage with vLLM

vllm serve \
  --model openbmb/MiniCPM-V-4.7-1B \
  --served-model-name MiniCPM-V-4.7-1B \
  --max-model-len 40960 \
  --max-num-batched-tokens 40960 \
  --max-num-seqs 1 \
  --attention-backend FLASH_ATTN \
  --mm-encoder-attn-backend FLASH_ATTN \
  --mm-processor-kwargs '{"downsample_mode":"4x"}' \
  --trust-request-mm-kwargs \
  --speculative-config '{"method":"dspark","model":"openbmb/MiniCPM-V-4.7-1B-DSpark","num_speculative_tokens":15,"enable_adaptive_verification":false}' \
  --default-chat-template-kwargs '{"enable_thinking":false}' \

Limitations and Disclaimer

This model has no autonomous intent or legal personhood; its outputs are text generated from statistical patterns and may be inaccurate, biased, or offensive, and may be manipulated by carefully crafted prompts ("jailbreaks") into producing unintended content. Its responses on sensitive topics such as politics, health, finance, and law are not reviewed by experts and should not be treated as professional advice.

This model is provided "AS IS", without warranty of any kind, express or implied, and the developers are not liable for any damages arising from its use. Users must employ the model only for lawful, compliant, and ethical purposes, configure their own safeguards, and label AI-generated content where required; deliberate jailbreaking, injection attacks, or inducing harmful output is prohibited, and any such testing is at the user's own risk.

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Safetensors

Model size

0.2B params

Tensor type

BF16

Model tree for openbmb/MiniCPM-V-4.7-1B-DSpark

Quantizations

2 models

Collection including openbmb/MiniCPM-V-4.7-1B-DSpark

MiniCPM-V 4.7

Collection

A new chapter in on-device multimodal Al. • 15 items • Updated 5 days ago • 74

Paper for openbmb/MiniCPM-V-4.7-1B-DSpark

DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation

Paper • 2607.05147 • Published Jul 6 • 52