---
title: nanoVLM-222M
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/nanoVLM-222M"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/nanoVLM-222M.md"
repository: AI-ModelScope/nanoVLM-222M
last_updated: 2025-05-11
license: apache-2.0
pipeline_tag: image-text-to-text
tasks:
  - image-text-to-text
parameters: 222.1M
tensor_type:
  - F32
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 408
stars: 0
tags:
  - vision-language
  - multimodal
  - pytorch
  - small-model
  - efficient
  - research
  - VLM
---

# nanoVLM-222M

> nanoVLM-222M - AI-ModelScope 在 ModelScope 开源的模型。nanoVLM is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within 750…

AI-ModelScope/nanoVLM-222M 是 ModelScope 魔搭社区上的 222.1M 参数image-text-to-text模型，采用 apache-2.0 许可。

- **Repository**: AI-ModelScope/nanoVLM-222M
- **License**: apache-2.0
- **Tasks**: image-text-to-text
- **Parameters**: 222.1M
- **Tags**: vision-language, multimodal, pytorch, small-model, efficient, research, VLM
- **Downloads**: 408
- **Stars**: 0
- **Last updated**: 2025-05-11

Source: https://www.modelscope.cn/models/AI-ModelScope/nanoVLM-222M

---

**nanoVLM** is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. 
  Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. 
  It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), 
  resulting in a compact 222M parameter model. The model achieves 35.3% accuracy on MMStar after training for ~6 hours on a single H100 GPU 
  using 1.7M samples from [the cauldron](https://huggingface.co/datasets/HuggingFaceM4/the_cauldron) dataset, 
  making it a strong baseline for low-resource VLM research.

  The model is ideal for researchers and developers interested in exploring VLM training with minimal computational overhead, 
  and serves as a perfect starting point for tinkering with multimodal architectures.

**Model Architecture:**
  - Vision Transformer (SigLIP-B/16)
  - Causal Language Model (SmolLM2)
  - Modality Projection Layer

**Training:**
  - Trained on ~1.7M samples from the `the_cauldron` dataset
  - 6 hours on a single NVIDIA H100 GPU
  - Resulting model size: 222M parameters

**Evaluation:**
  - MMStar Accuracy: 35.3%

**Usage:**  
Usable through the nanoVLM repository: https://github.com/huggingface/nanoVLM  
For more details, see: https://github.com/huggingface/nanoVLM?tab=readme-ov-file#hub-integration
  ```python
  from models.vision_language_model import VisionLanguageModel
  model = VisionLanguageModel.from_pretrained("lusxvr/nanoVLM-222M")
  ```