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

Libraries

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

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/openbmb-MiniCPM-V-4.7-1B-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 prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/openbmb-MiniCPM-V-4.7-1B-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 prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/openbmb-MiniCPM-V-4.7-1B-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 prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/openbmb-MiniCPM-V-4.7-1B-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 prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/openbmb-MiniCPM-V-4.7-1B-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": "prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF",
		"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/prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/openbmb-MiniCPM-V-4.7-1B-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 "prithivMLmods/openbmb-MiniCPM-V-4.7-1B-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": "prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF",
		"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 "prithivMLmods/openbmb-MiniCPM-V-4.7-1B-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": "prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/openbmb-MiniCPM-V-4.7-1B-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": "prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.openbmb-MiniCPM-V-4.7-1B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/openbmb-MiniCPM-V-4.7-1B-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 prithivMLmods/openbmb-MiniCPM-V-4.7-1B-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 prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/openbmb-MiniCPM-V-4.7-1B-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 "prithivMLmods/openbmb-MiniCPM-V-4.7-1B-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"

openbmb-MiniCPM-V-4.7-1B-GGUF

MiniCPM-V 4.7 1B is the latest model in OpenBMB's MiniCPM-V series, a roughly 1B-parameter multimodal model for single-image, multi-image and video understanding, designed for on-device use. It improves on MiniCPM-V 4.6's foundation capabilities and, in Instruct mode, is said to substantially outperform Qwen3.5 0.8B in both overall score and inference efficiency, which the card presents as advancing the performance-efficiency Pareto frontier. It uses intra-ViT early compression from LLaVA-UHD v4, which compresses visual tokens inside the vision encoder to cut computation in later layers. It also supports mixed 4x/16x visual token compression, so developers can trade visual detail against cost. The benchmark comparisons appear only as images, so the individual scores aren't readable from the text. The acknowledgements credit Qwen3.6 as the language backbone and SigLIP2 as the vision module. Deployment targets include iOS, Android and HarmonyOS, with day-0 support for Rockchip, Houmo, Intel, AMD and Arm chips and an OpenVINO path for Intel hardware. It loads through Transformers (>=5.18.0), can be served with transformers serve, and is supported by SGLang, vLLM, llama.cpp and Ollama. Fine-tuning works with SWIFT and LLaMA-Factory, and the card introduces Self-Replay Fine-Tuning, meant to extend capabilities cheaply while reducing forgetting. Variants include int4 (bitsandbytes), AWQ, GPTQ, GGUF and FP8 quantizations, plus a DSpark draft model for speculative decoding. It is released under Apache 2.0.

Model Files

File Name	Quant Type	File Size	File Link	Description
MiniCPM-V-4.7-1B.BF16.gguf	BF16	2.02 GB	Link	Full BF16 weights. Highest quality, largest file size.
MiniCPM-V-4.7-1B.MXFP4_MOE.gguf	MXFP4_MOE	1.08 GB	Link	Mixed precision / MoE optimized quantization, very small size.
MiniCPM-V-4.7-1B.Q3_K_L.gguf	Q3_K_L	600 MB	Link	Lower quality but usable, good for low RAM availability.
MiniCPM-V-4.7-1B.Q3_K_M.gguf	Q3_K_M	575 MB	Link	Low quality.
MiniCPM-V-4.7-1B.Q4_K_M.gguf	Q4_K_M	672 MB	Link	Good quality, default size for most use cases, recommended.
MiniCPM-V-4.7-1B.Q4_K_S.gguf	Q4_K_S	648 MB	Link	Slightly lower quality with more space savings, recommended.
MiniCPM-V-4.7-1B.Q5_K_M.gguf	Q5_K_M	752 MB	Link	High quality, recommended.
MiniCPM-V-4.7-1B.Q5_K_S.gguf	Q5_K_S	738 MB	Link	High quality, recommended.
MiniCPM-V-4.7-1B.Q6_K.gguf	Q6_K	838 MB	Link	Very high quality, near perfect, recommended.
MiniCPM-V-4.7-1B.Q8_0.gguf	Q8_0	1.08 GB	Link	Extremely high quality, generally unneeded but max available quant.
MiniCPM-V-4.7-1B.mmproj-bf16.gguf	mmproj-bf16	1.11 GB	Link	Multimodal projection file in BF16 format. Used for vision/language models.

llama.cpp

LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp

Downloads last month: 1,269

GGUF

Model size

1B params

Architecture

qwen35

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for prithivMLmods/openbmb-MiniCPM-V-4.7-1B-GGUF

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October 2026

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