openbmb/MiniCPM-V-4.7-1B

MiniCPM-V 4.7

[Evaluation](#evaluation) · [Examples](#examples) · [Usage](#usages) · [Model weights](#model-weights)

MiniCPM-V 4.7 is the latest model in the MiniCPM-V series, available in a 1B variant. It builds on the family's single-image, multi-image, and video understanding capabilities, combining stronger foundation capabilities with efficient visual encoding and on-device inference. Notable features of MiniCPM-V 4.7 include:

• 🔥 A New Pareto Frontier in Performance and Efficiency.

As model capabilities continue to advance, inference efficiency has become an important measure of a model's value. On resource-constrained devices, delivering the strongest possible capabilities within a limited resource budget calls for higher capability density. The 1B variant of MiniCPM-V 4.7 advances the performance–efficiency Pareto frontier for MLLMs. In Instruct mode, the 1B variant continues to improve foundation capabilities over MiniCPM-V 4.6 and substantially outperforms Qwen3.5 0.8B in both overall score and inference efficiency.

• 🚀 Efficient Architecture.

Based on the intra-ViT early compression technique from LLaVA-UHD v4, MiniCPM-V 4.7 compresses visual tokens early within the vision encoder, reducing computation in subsequent encoder layers. It also supports mixed 4x/16x visual token compression, allowing developers to balance visual detail and inference cost across tasks.

• 💻 Broad Platform and Chip Support.

MiniCPM-V 4.7 supports iOS, Android, and HarmonyOS, together with open-source on-device deployment support. It also provides day-0 support for chip platforms from Rockchip, Houmo, Intel, AMD, and Arm, extending deployment options across mobile and local computing devices.

• 🛠️ Developer Friendly.

MiniCPM-V 4.7 supports inference frameworks including SGLang, vLLM, llama.cpp, and Ollama, along with fine-tuning frameworks including SWIFT and LLaMA-Factory. It provides GGUF, BNB, AWQ, and GPTQ quantized variants and adds FP8 quantization support. We also release Self-Replay Fine-Tuning, a fine-tuning approach that helps developers extend model capabilities at low cost while reducing forgetting of existing capabilities, making further customization easier.

Get started

Model weights · Inference · Fine-tuning · Live demo · Local deployment · API · Cookbook

Evaluation

Significantly Advancing the Performance–Efficiency Pareto Frontier

The 1B variant of MiniCPM-V 4.7 advances the performance–efficiency Pareto frontier. In Instruct mode, 1B substantially outperforms Qwen3.5 0.8B in both overall score and inference efficiency.

Overall Performance (1B, Instruct)

Examples

MiniCPM-V 4.7 can run on phones and tablets. The clips below are unedited screen recordings captured directly on the devices.

1B iPhone 17 Pro Max

Usages

Inference with Transformers

Click to expand the Transformers inference examples.

##### Installation

pip install "transformers[torch]>=5.18.0" torchvision torchcodec

> Note on CUDA compatibility: torchcodec (used for video decoding) may have compatibility issues with certain CUDA versions. For example, torch>=2.11 bundles CUDA 13.1 by default, while environments with CUDA 12.x may encounter errors such as RuntimeError: Could not load libtorchcodec. Two workarounds: > > 1. Replace `torchcodec` with `PyAV` — supports both image and video inference without CUDA version constraints: > ``bash > pip install "transformers[torch]>=5.18.0" torchvision av > > 2. **Pin the CUDA version** when installing torch to match your environment (e.g. CUDA 12.8): > bash > pip install "transformers>=5.18.0" torchvision torchcodec --index-url https://download.pytorch.org/whl/cu128 >

##### Load the Model

import torch
from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "openbmb/MiniCPM-V-4.7-1B"

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
model_id, torch_dtype="auto", device_map="auto"
)

# Flash Attention 2 is recommended for faster inference and lower GPU memory usage,
# especially in multi-image and video scenarios.
# model = AutoModelForImageTextToText.from_pretrained(
# model_id,
# torch_dtype=torch.bfloat16,
# attn_implementation="flash_attention_2",
# device_map="auto",
# )

##### Image Inference

messages = [
{
"role": "user",
"content": [
{"type": "image", "url": "https://huggingface.co/datasets/openbmb/DemoCase/resolve/main/refract.png"},
{"type": "text", "text": "What causes this phenomenon?"},
],
}
]

downsample_mode = "16x" # Using `downsample_mode="4x"` for Finer Detail

inputs = processor.apply_chat_template(
messages, tokenize=True, add_generation_prompt=True,
return_dict=True, return_tensors="pt",
downsample_mode=downsample_mode,
max_slice_nums=36,
enable_thinking=False,
).to(model.device)

generated_ids = model.generate(**inputs, downsample_mode=downsample_mode, max_new_tokens=512)
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text[0])

##### Video Inference

messages = [
{
"role": "user",
"content": [
{"type": "video", "url": "https://huggingface.co/datasets/openbmb/DemoCase/resolve/main/football.mp4"},
{"type": "text", "text": "Describe this video in detail. Follow the timeline and focus on on-screen text, interface changes, main actions, and scene changes."},
],
}
]

downsample_mode = "16x" # Using `downsample_mode="4x"` for Finer Detail

inputs = processor.apply_chat_template(
messages, tokenize=True, add_generation_prompt=True,
return_dict=True, return_tensors="pt",
downsample_mode=downsample_mode,
max_num_frames=128,
stack_frames=1,
max_slice_nums=1,
use_image_id=False,
enable_thinking=False,
).to(model.device)

generated_ids = model.generate(**inputs, downsample_mode=downsample_mode, max_new_tokens=2048)
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)...