MoDA (Modulation Adapter) is a lightweight module that improves fine-grained visual grounding in Multimodal LLMs through instruction-guided, channel-wise modulation of pre-aligned visual features. A stack of cross-attention layers conditions on the language instruction and produces a soft channel-wise mask (via sigmoid) that is applied multiplicatively (Hadamard product) to the aligned visual features before they reach the LLM:

Ṽ_aligned = V_aligned ⊙ σ(W · F(T, V_aligned))

This checkpoint applies MoDA to Qwen3-VL-2B-Instruct, showing that the gains generalize beyond CLIP-style encoders to modern MLLMs. Other variants: MoDA-LLaVA-MoRE-8B-SigLIP-S2 · MoDA-LLaVA-MoRE-8B-CLIP.

Model details


Architecture	Qwen3-VL-2B + MoDA adapter (2 cross-attention decoder layers, 16 heads — paper config; 75.6M adapter params)
Base model	Qwen/Qwen3-VL-2B-Instruct
Training data	LLaVA v1.5 instruction-tuning mix (665K)
Precision	bfloat16

Results

Qwen3-VL-2B-Instruct baseline vs. + MoDA (see the paper for the full evaluation):

Benchmark	Baseline	+ MoDA
ScienceQA	79.3	84.2 (+4.9)
RealWorldQA	64.7	68.8 (+4.1)
GQA	59.4	63.2 (+3.8)
MMStar	53.9	55.3
MMBench	86.5	87.4
POPE	89.4	89.8
CV-Bench	80.9	81.0

Usage

Loads directly with 🤗 transformers — the MoDA classes ship inside this repo (trust_remote_code=True):

python

import torch
from transformers import AutoProcessor, AutoModelForImageTextToText

model_id = "waybarrios/MoDA-Qwen3-VL-2B"
model = AutoModelForImageTextToText.from_pretrained(
    model_id, trust_remote_code=True, dtype=torch.bfloat16, device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_id)

messages = [
    {"role": "user", "content": [
        {"type": "image", "url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"},
        {"type": "text", "text": "Describe this image."},
    ]},
]
inputs = processor.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=True,
    return_dict=True, return_tensors="pt"
).to(model.device)
out = model.generate(**inputs, max_new_tokens=128)
print(processor.batch_decode(out[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0])

Training and evaluation code (lmms-eval integration) lives in the qwen3-vl/ directory of the repository.

Evaluation

We evaluate with lmms-eval, which provides standardized implementations of all benchmarks reported in the paper (GQA, ScienceQA, POPE, MMBench, RealWorldQA, and more).

Citation

bibtex

@inproceedings{barrios2026moda,
  title     = {MoDA: Modulation Adapter for Fine-Grained Visual Understanding in Instructional MLLMs},
  author    = {Barrios, Wayner and Villa, Andr\'es and Leon Alcazar, Juan C. and Jin, SouYoung and Ghanem, Bernard},
  booktitle = {Proceedings of the International Conference on Machine Learning (ICML)},
  year      = {2026}
}

License

Apache-2.0, following the base model Qwen/Qwen3-VL-2B-Instruct.

Acknowledgments

Built on Qwen3-VL by Alibaba. Supported by startup funds from Dartmouth College and by KAUST — Center of Excellence for Generative AI (award 5940).