---
title: Simple-VL-8B
canonical_url: "https://www.modelscope.cn/models/swift/Simple-VL-8B"
md_url: "https://www.modelscope.cn/models/swift/Simple-VL-8B.md"
repository: swift/Simple-VL-8B
last_updated: 2025-05-12
license: "Apache License 2.0"
pipeline_tag: image-text-to-text
tasks:
  - image-text-to-text
model_type:
  - qwen
architectures:
  - Qwen2_5_VLForConditionalGeneration
base_model:
  - Qwen/Qwen3-8B
  - Qwen/Qwen2.5-VL-7B-Instruct
base_model_relation: finetune
parameters: 8.1B
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - Pytorch
language:
  - en
domain:
  - multi-modal
inference_backends:
  - "deploy_task vlm/text/emb"
  - "lmdeploy 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 1452
stars: 6
---

# Simple-VL-8B

> Simple-VL-8B - swift 在 ModelScope 开源的模型。Simple-VL-8B is a vision-language (VL) model trained by integrating the language modeling capabilities of Qwen3-8B with the visual understanding architecture of Qwen2.5-VL-7B-Instruct .

swift/Simple-VL-8B 是 ModelScope 魔搭社区上的 8.1B 参数image-text-to-text模型，采用 Apache License 2.0 许可，基于 Qwen/Qwen3-8B、Qwen/Qwen2.5-VL-7B-Instruct 构建，可用 deploy_task vlm/text/emb、lmdeploy 0.9.1、sglang 0.5.2 部署。

- **Repository**: swift/Simple-VL-8B
- **License**: Apache License 2.0
- **Tasks**: image-text-to-text
- **Parameters**: 8.1B
- **Base model**: Qwen/Qwen3-8B, Qwen/Qwen2.5-VL-7B-Instruct
- **Inference backends**: deploy_task vlm/text/emb, lmdeploy 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Downloads**: 1452
- **Stars**: 6
- **Last updated**: 2025-05-12

Source: https://www.modelscope.cn/models/swift/Simple-VL-8B

---

Simple-VL-8B is a vision-language (VL) model trained by integrating the language modeling capabilities of Qwen3-8B with the visual understanding architecture of Qwen2.5-VL-7B-Instruct . 

The model is trained under [ms-swift](https://github.com/modelscope/ms-swift/tree/main) framework, the SOP process document can be found [here](https://swift.readthedocs.io/en/latest/BestPractices/Rapidly-Training-VL-model.html)

Base Models :
- [Qwen2.5-VL-7B-Instruct](https://www.modelscope.cn/models/Qwen/Qwen2.5-VL-7B-Instruct)
- [Qwen3-8B](https://www.modelscope.cn/models/Qwen/Qwen3-8B)

The Simple-VL-8B model was created through a two-stage fine-tuning process:

1. Architecture Modification : The original Qwen2.5-VL-7B-Instruct model's LLM component was replaced with weights from Qwen3-8B. Several key parameters in the configuration were updated to match Qwen3-8B's structure.
2. Two-Stage Training :
    1. Stage 1 : Only the vision-to-language aligner (merger layer) was trained while keeping the ViT and LLM components frozen.
    2. Stage 2 : All components were unfrozen and jointly fine-tuned to enhance overall performance.

Here we show a code snippet to show you how to use the chat model

```python
from modelscope import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

# default: Load the model on the available device(s)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "swift/Simple-VL-8B", torch_dtype="auto", device_map="auto"
)

# default processer
processor = AutoProcessor.from_pretrained("swift/Simple-VL-8B")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "Describe this image."},
        ],
    }
]

# Preparation for inference
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to("cuda")

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
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)

```