Instructions to use openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf with Transformers:

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

pipe = pipeline("text-generation", model="openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf:Q4_K_M

Use Docker

docker model run hf.co/openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf:Q4_K_M

SGLang

How to use openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf 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-4.7-35B-A3B-DSpark-gguf" \
    --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": "openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf",
		"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 "openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf" \
        --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": "openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf with Ollama:
```
ollama run hf.co/openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf:Q4_K_M
```
Unsloth Desktop

How to use openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf with Docker Model Runner:
```
docker model run hf.co/openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf:Q4_K_M
```

Lemonade

How to use openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf:Q4_K_M

Run and chat with the model

lemonade run user.MiniCPM-V-4.7-35B-A3B-DSpark-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

This repository hosts the GGUF (llama.cpp) quantized version of MiniCPM-V-4.7-35B-A3B-DSpark. For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.7-35B-A3B-DSpark.

MiniCPM-V-4.7-35B-A3B-DSpark

MiniCPM-V-4.7-35B-A3B-DSpark is a DSpark draft checkpoint trained for exact pairing with MiniCPM-V-4.7-35B-A3B and its tokenizer.

Model Specification

Property	Value
Target model	MiniCPM-V-4.7-35B-A3B
Draft layers	5
Draft parameters	389,223,169
Draft tokens per forward pass	15
Target hidden-state layers	`[3, 11, 19, 27, 35]`
Checkpoint precision	BF16

Training Recipe

Training responses were generated by MiniCPM-V-4.7-35B-A3B 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	5.098
MathVista	6.126
CharXiv_reasoning_val	5.479
OCRBench	5.407
Text
Math	6.687
Code	7.387
General	3.501

Usage with vLLM

vllm serve \
  --model openbmb/MiniCPM-V-4.7-35B-A3B \
  --served-model-name MiniCPM-V-4.7-35B-A3B \
  --tensor-parallel-size 2 \
  --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 \
  --disable-custom-all-reduce \
  --compilation-config '{"pass_config":{"fuse_allreduce_rms":false}}' \
  --speculative-config '{"method":"dspark","model":"openbmb/MiniCPM-V-4.7-35B-A3B-DSpark","num_speculative_tokens":15,"enable_adaptive_verification":false}' \
  --default-chat-template-kwargs '{"enable_thinking":false}'

Usage with llama.cpp

Download the target model, its vision projector (mmproj), and this DSpark draft:

huggingface-cli download openbmb/MiniCPM-V-4.7-35B-A3B-gguf MiniCPM-V-4_7-35B-A3B-Q4_K_M.gguf mmproj-model-f16.gguf --local-dir ./35B-A3B-gguf
huggingface-cli download openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf dspark-MiniCPM-V-4_7-35B-A3B-Q8_0.gguf --local-dir ./35B-A3B-gguf

Run with speculative decoding (-md = draft, --spec-type draft-dspark). llama-cli handles both the vision projector and the draft:

llama-cli \
  -m ./35B-A3B-gguf/MiniCPM-V-4_7-35B-A3B-Q4_K_M.gguf \
  --mmproj ./35B-A3B-gguf/mmproj-model-f16.gguf \
  -md ./35B-A3B-gguf/dspark-MiniCPM-V-4_7-35B-A3B-Q8_0.gguf \
  --spec-type draft-dspark \
  --image image.jpg -p "Describe this image." \
  -ngl 99 -ngld 99 -fa on -c 8192

For a multimodal server:

llama-server \
  -m ./35B-A3B-gguf/MiniCPM-V-4_7-35B-A3B-Q4_K_M.gguf \
  --mmproj ./35B-A3B-gguf/mmproj-model-f16.gguf \
  -md ./35B-A3B-gguf/dspark-MiniCPM-V-4_7-35B-A3B-Q8_0.gguf \
  --spec-type draft-dspark --spec-draft-n-max 15 \
  -ngl 99 -ngld 99 -fa on -c 8192 --jinja --port 8080

Requires a llama.cpp build with the dflash architecture and the DSpark Markov head. The draft must be paired with the matching MiniCPM-V 4.7 35B-A3B target. Draft quantization only affects acceptance length (never the output), so pick the smallest quant that fits — Q8_0 / Q6_K are good defaults.

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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Model size

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Architecture

dflash

Hardware compatibility

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Model tree for openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf

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Collection including openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf

MiniCPM-V 4.7

Collection

A new chapter in on-device multimodal Al. • 15 items • Updated 1 day ago • 64

Paper for openbmb/MiniCPM-V-4.7-35B-A3B-DSpark-gguf

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

Paper • 2607.05147 • Published Jul 6 • 51