---
title: MiniCPM-Llama3-V-2_5-int4
canonical_url: "https://www.modelscope.cn/models/OpenBMB/MiniCPM-Llama3-V-2_5-int4"
md_url: "https://www.modelscope.cn/models/OpenBMB/MiniCPM-Llama3-V-2_5-int4.md"
repository: OpenBMB/MiniCPM-Llama3-V-2_5-int4
last_updated: 2025-04-24
pipeline_tag: visual-question-answering
tasks:
  - visual-question-answering
model_type:
  - minicpmv
architectures:
  - MiniCPMV
base_model:
  - openbmb/MiniCPM-Llama3-V-2_5
base_model_relation: quantized
parameters: 5.0B
tensor_type:
  - F32
  - U8
  - F16
library_name:
  - pytorch
  - transformer
  - safetensors
frameworks:
  - Pytorch
inference_backends:
  - "deploy_task text/emb/vlm"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 81436
stars: 17
---

# MiniCPM-Llama3-V-2_5-int4

> MiniCPM-Llama3-V-2_5-int4 - OpenBMB 在 ModelScope 开源的模型。MiniCPM-Llama3-V 2.5 int4 This is the int4 quantized version of MiniCPM-Llama3-V 2.5. Running with int4 version would use lower GPU memory (about 9GB).

OpenBMB/MiniCPM-Llama3-V-2_5-int4 是 ModelScope 魔搭社区上的 5.0B 参数visual-question-answering模型，基于 openbmb/MiniCPM-Llama3-V-2_5 构建，可用 deploy_task text/emb/vlm、lmdeploy_turbomind 0.9.1、sglang 0.5.2 部署。

- **Repository**: OpenBMB/MiniCPM-Llama3-V-2_5-int4
- **Tasks**: visual-question-answering
- **Parameters**: 5.0B
- **Base model**: openbmb/MiniCPM-Llama3-V-2_5
- **Inference backends**: deploy_task text/emb/vlm, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Downloads**: 81436
- **Stars**: 17
- **Last updated**: 2025-04-24

Source: https://www.modelscope.cn/models/OpenBMB/MiniCPM-Llama3-V-2_5-int4

---

## MiniCPM-Llama3-V 2.5 int4
This is the int4 quantized version of [MiniCPM-Llama3-V 2.5](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5).   
Running with int4 version would use lower GPU memory (about 9GB).


## Usage
Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.10：
```
Pillow==10.1.0
torch==2.1.2
torchvision==0.16.2
transformers==4.40.0
sentencepiece==0.1.99
accelerate==0.30.1
bitsandbytes==0.43.1
```

```python
# test.py
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5-int4', trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5-int4', trust_remote_code=True)
model.eval()

image = Image.open('xx.jpg').convert('RGB')
question = 'What is in the image?'
msgs = [{'role': 'user', 'content': question}]

res = model.chat(
    image=image,
    msgs=msgs,
    tokenizer=tokenizer,
    sampling=True, # if sampling=False, beam_search will be used by default
    temperature=0.7,
    # system_prompt='' # pass system_prompt if needed
)
print(res)

## if you want to use streaming, please make sure sampling=True and stream=True
## the model.chat will return a generator
res = model.chat(
    image=image,
    msgs=msgs,
    tokenizer=tokenizer,
    sampling=True,
    temperature=0.7,
    stream=True
)

generated_text = ""
for new_text in res:
    generated_text += new_text
    print(new_text, flush=True, end='')
```