menglc/SliMM-Qwen2-0.5B
010
SliMM: A Simple LMM baseline with Dynamic Visual Resolution 🚀
🔥 Latest Update
- [2024/12/12] Our first version is out! We release a strong 0.5B baseline model SliMM-Qwen2-0.5B and advanced baseline SliMM-DeepStackM-Qwen2-0.5B. We release a strong 2B model SliMM-DeepStackE-Qwen2VL-2B continous fine-tuned from Qwen2VL-2B, which save 4x fewer visual tokens for LLM with. Training scrips are avaliable [here]()!
Introduction
- Advanced Techniques: We incorporate native dynamic resolution, as used in Qwen2-VL, for high-resolution visual encoding, replacing the previous cumbersome Multi-Crop/AnyRes methods. Moreover, building on DeepStack [1], we maintain the same principle of interting stacked visual tokens into multiple layers of the LLMs. We propose two enhanced versions for native resolution vision encoding: DeepStack-MidLayers, which improves performance with negligible additional FLOPs by stacking multi-level visual tokens from the middle layers of the vision encoder, and DeepStack-Efficient, which reduces visual token usage while maintaining high performance.
- Seamless Integration: Easily use LLaVA-format training data in our codebase.
- Training Efficiency: Fine-tuning on the 748K LLaVA-Next-DATA for on epoch takes only 4 hours for 0.5/2B Qwen2 and 6 hours for a 7B on 8xH100, which is more than 2x faster than LLaVA-OV codebase.
- Strong Baseline Model for Small LMMs: We establish a robust baseline using widely-used public available datasets, including LCS-758K (Stage-1), LLaVA-OV-MidStage (Stage 1.5), and LLaVA-OneVision SI (Stage 2).
[1] DeepStack: Deeply Stacking Visual Tokens is Surprisingly Simple and Effective for LMMs
Quick Start
git clone https://github.com/MengLcool/SliMM.git
cd SliMM
pip install -e .# this is very similar to qwen2-vl
from slimm.model.processor import SliMMQwen2VLProcessor
from slimm.model.slimm import SliMMForConditionalGeneration
from slimm.model.utils_vl import process_vision_info
model_path = "menglc/SliMM-Qwen2-0.5B"
model = SliMMForConditionalGeneration.from_pretrained(
model_path, torch_dtype="auto", device_map="auto"
)
processor = SliMMQwen2VLProcessor.from_pretrained(model_path)
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)Benchmarks
🔗 Citation
If you find our work helpful, please consider citing our paper :paperclip: and starring our repo :star2: :
@inproceedings{meng2024deepstack,
title={DeepStack: Deeply Stacking Visual Tokens is Surprisingly Simple and Effective for LMMs},
author={Meng, Lingchen and Yang, Jianwei and Tian, Rui and Dai, Xiyang and Wu, Zuxuan and Gao, Jianfeng and Jiang, Yu-Gang},
booktitle={NeurIPS},
year={2024}
}