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---
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE
language:
- en
pipeline_tag: image-text-to-text
tags:
- multimodal
- food
- recipe
library_name: transformers
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
datasets:
- AdaptLLM/food-visual-instructions
---
# Adapting Multimodal Large Language Models to Domains via Post-Training (EMNLP 2025)
This repos contains the **food MLLM developed from Qwen2.5-VL-3B-Instruct** in our paper: [On Domain-Adaptive Post-Training for Multimodal Large Language Models](https://huggingface.co/papers/2411.19930). The correspoding training dataset is in [food-visual-instructions](https://huggingface.co/datasets/AdaptLLM/food-visual-instructions).
The main project page is: [Adapt-MLLM-to-Domains](https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains)
## 1. To Chat with AdaMLLM
Our model architecture aligns with the base model: Qwen2.5-VL-3B-Instruct. We provide a usage example below, and you may refer to the official [Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) for more advanced usage instructions.
**Note:** For AdaMLLM, always place the image at the beginning of the input instruction in the messages.
<details>
<summary> Click to expand </summary>
The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command:
```
pip install git+https://github.com/huggingface/transformers accelerate
```
or you might encounter the following error:
```
KeyError: 'qwen2_5_vl'
```
Qwen2.5-VL offers a toolkit to help you handle various types of visual input more conveniently, as if you were using an API. This includes base64, URLs, and interleaved images and videos. You can install it using the following command:
```bash
# It's highly recommanded to use `[decord]` feature for faster video loading.
pip install qwen-vl-utils[decord]==0.0.8
```
If you are not using Linux, you might not be able to install `decord` from PyPI. In that case, you can use `pip install qwen-vl-utils` which will fall back to using torchvision for video processing. However, you can still [install decord from source](https://github.com/dmlc/decord?tab=readme-ov-file#install-from-source) to get decord used when loading video.
### Using 🤗 Transformers to Chat
Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_vl_utils`:
```python
from transformers 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(
"AdaptLLM/food-Qwen2.5-VL-3B-Instruct", torch_dtype="auto", device_map="auto"
)
# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
# "AdaptLLM/food-Qwen2.5-VL-3B-Instruct",
# torch_dtype=torch.bfloat16,
# attn_implementation="flash_attention_2",
# device_map="auto",
# )
# default processer
processor = AutoProcessor.from_pretrained("AdaptLLM/food-Qwen2.5-VL-3B-Instruct")
# The default range for the number of visual tokens per image in the model is 4-16384.
# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
# min_pixels = 256*28*28
# max_pixels = 1280*28*28
# processor = AutoProcessor.from_pretrained("AdaptLLM/food-Qwen2.5-VL-3B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
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)
```
</details>
## 2. To Evaluate Any MLLM on Domain-Specific Benchmarks
Please refer to the [food-VQA-benchmark](https://huggingface.co/datasets/AdaptLLM/food-VQA-benchmark) to reproduce our results and evaluate many other MLLMs on domain-specific benchmarks.
## 3. To Reproduce this Domain-Adapted MLLM
Using our training data, [food-visual-instructions](https://huggingface.co/datasets/AdaptLLM/food-visual-instructions), you can easily reproduce our models based on the [LlamaFactory](https://github.com/hiyouga/LLaMA-Factory) repository.
For reference, we train from Qwen2.5-VL-3B-Instruct for 1 epoch with a learning rate of 1e-5, and a global batch size of 128.
## Citation
If you find our work helpful, please cite us.
[Adapt MLLM to Domains](https://huggingface.co/papers/2411.19930) (EMNLP 2025 Findings)
```bibtex
@article{adamllm,
title={On Domain-Adaptive Post-Training for Multimodal Large Language Models},
author={Cheng, Daixuan and Huang, Shaohan and Zhu, Ziyu and Zhang, Xintong and Zhao, Wayne Xin and Luan, Zhongzhi and Dai, Bo and Zhang, Zhenliang},
journal={arXiv preprint arXiv:2411.19930},
year={2024}
}
```
[Adapt LLM to Domains](https://huggingface.co/papers/2309.09530) (ICLR 2024)
```bibtex
@inproceedings{
cheng2024adapting,
title={Adapting Large Language Models via Reading Comprehension},
author={Daixuan Cheng and Shaohan Huang and Furu Wei},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=y886UXPEZ0}
}
```

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}

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preprocessor_config.json Normal file
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special_tokens_map.json Normal file
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tokenizer_config.json Normal file
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8
train_results.json Normal file
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trainer_log.jsonl Normal file
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