LLaVA-1.5-7B-GRACE-W4G128

This repository contains a GRACE-trained LLaVA-1.5-7B checkpoint with quantization-aware training (QAT), W4G128 group-wise INT4 quantization, and learned scales.

This model is associated with our ICML 2026 paper:

Gated Relational Alignment via Confidence-based Distillation for Efficient VLMs
Yanlong Chen, Amirhossein Habibian, Luca Benini, Yawei Li
Accepted to the International Conference on Machine Learning (ICML 2026)

• Paper: https://arxiv.org/abs/2601.22709
• DOI: https://doi.org/10.48550/arXiv.2601.22709
• Code: https://github.com/ForeverBlue816/GRACE

Model Details

• Base model: liuhaotian/llava-v1.5-7b
• Model family: LLaVA-1.5
• Method: GRACE
• Quantization: W4G128 group-wise INT4 QAT
• Scale setting: learned scales
• Repository: ForeverBlue/LLaVA-1.5-7B-GRACE-W4G128

Files

This repository includes the merged model checkpoint and GRACE-specific quantized weights:

• model-00001-of-00003.safetensors
• model-00002-of-00003.safetensors
• model-00003-of-00003.safetensors
• model.safetensors.index.json
• qat_quantized_weights.bin
• config.json
• generation_config.json
• tokenizer.model
• tokenizer_config.json
• special_tokens_map.json

Intended Use

This model is intended for research on efficient vision-language models, quantization-aware training, and multimodal model compression.

Potential use cases include:

• evaluating INT4 quantized vision-language models;
• studying quantization-aware training for multimodal models;
• reproducing or extending GRACE-style efficient VLM experiments;
• comparing quantized LLaVA-1.5 variants under multimodal benchmarks.

Loading

Please use a LLaVA-compatible inference environment together with the GRACE quantization-aware loading code.

from transformers import AutoTokenizer, AutoModelForCausalLM

repo_id = "ForeverBlue/LLaVA-1.5-7B-GRACE-W4G128"

tokenizer = AutoTokenizer.from_pretrained(repo_id, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    trust_remote_code=True,
    device_map="auto"
)

Important Notes

This checkpoint includes QAT-specific quantized weights in qat_quantized_weights.bin. Depending on the inference codebase, additional GRACE-specific quantization-aware loading logic may be required.

The standard from_pretrained call may load the model configuration and checkpoint files, but fully reproducing the intended INT4 QAT behavior may require the GRACE repository:

https://github.com/ForeverBlue816/GRACE

Limitations

• This model is released for research purposes.
• The quantized checkpoint may require custom loading logic for QAT-specific weights.
• Performance may vary depending on the evaluation codebase, preprocessing, generation parameters, and multimodal benchmark implementation.
• Users should follow the license and usage restrictions of the original LLaVA-1.5 base model.

Citation

If you use this model, please cite:

@article{chen2026gated,
  title={Gated Relational Alignment via Confidence-based Distillation for Efficient VLMs},
  author={Chen, Yanlong and Habibian, Amirhossein and Benini, Luca and Li, Yawei},
  journal={arXiv preprint arXiv:2601.22709},
  year={2026}
}