---
title: nanoVLM-460M-8k
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/nanoVLM-460M-8k"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/nanoVLM-460M-8k.md"
repository: AI-ModelScope/nanoVLM-460M-8k
last_updated: 2025-09-10
license: mit
pipeline_tag: image-text-to-text
tasks:
  - image-text-to-text
parameters: 460.1M
tensor_type:
  - F32
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 104
stars: 0
tags:
  - vision-language
  - multimodal
  - research
---

# nanoVLM-460M-8k

> nanoVLM-460M-8k - AI-ModelScope 在 ModelScope 开源的模型。Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM. Follow the install instructions and run the following code:

AI-ModelScope/nanoVLM-460M-8k 是 ModelScope 魔搭社区上的 460.1M 参数image-text-to-text模型，采用 mit 许可。

- **Repository**: AI-ModelScope/nanoVLM-460M-8k
- **License**: mit
- **Tasks**: image-text-to-text
- **Parameters**: 460.1M
- **Tags**: vision-language, multimodal, research
- **Downloads**: 104
- **Stars**: 0
- **Last updated**: 2025-09-10

Source: https://www.modelscope.cn/models/AI-ModelScope/nanoVLM-460M-8k

---

**Usage:**

Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM.
Follow the install instructions and run the following code:

```python
from models.vision_language_model import VisionLanguageModel

model = VisionLanguageModel.from_pretrained("lusxvr/nanoVLM-460M-8k")
```

```
"results": {
        "docvqa_val_anls": 0.7695125277111309,
        "docvqa_val_anls_stderr": 0.00541393399604242,
        "infovqa_val_anls": 0.3571108969338406,
        "infovqa_val_anls_stderr": 0.007752114157367035,
        "mme_mme_cognition_score": 302.5,
        "mme_mme_perception_score": 1259.329131652661,
        "mmmu_val_mmmu_acc": 0.31889,
        "mmstar_coarse perception": 0.5657538907367322,
        "mmstar_average": 0.36039272693114954,
        "mmstar_fine-grained perception": 0.30654016212232865,
        "mmstar_instance reasoning": 0.4100153400624586,
        "mmstar_logical reasoning": 0.36221314439136226,
        "mmstar_math": 0.2498719148177849,
        "mmstar_science & technology": 0.26796190945623083,
        "ocrbench_ocrbench_accuracy": 0.688,
        "scienceqa_exact_match": 0.565432680971469,
        "scienceqa_exact_match_stderr": 0.007612653385710115,
        "textvqa_val_exact_match": 0.6537400000000001,
        "textvqa_val_exact_match_stderr": 0.006407293747178485,
        "chartqa_relaxed_overall": 0.7288,
        "chartqa_relaxed_overall_stderr": 0.008893360486581982,
        "chartqa_relaxed_human_split": 0.552,
        "chartqa_relaxed_human_split_stderr": 0.01407107658130413,
        "chartqa_relaxed_augmented_split": 0.9056,
        "chartqa_relaxed_augmented_split_stderr": 0.00827318974367371,
        "ai2d_exact_match": 0.4375,
        "ai2d_exact_match_stderr": 0.0625,
        "mathvista_testmini_cot_gpt_eval_score": 34.9,
        "mathvista_testmini_format_gpt_eval_score": 40.3,
        "mathvista_testmini_solution_gpt_eval_score": 35.2
    }
```