Create README.md

README.md

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+---
+base_model: google/t5-v1_1-xxl
+base_model_relation: quantized
+datasets:
+- mit-han-lab/svdquant-datasets
+language:
+- en
+library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- text-generation
+- AWQ
+- Quantization
+
+---
+**This repository has been migrated to https://huggingface.co/nunchaku-tech/nunchaku-t5 and will be hidden in December 2025.**
+
+<p align="center" style="border-radius: 10px">
+  <img src="https://huggingface.co/datasets/nunchaku-tech/cdn/resolve/main/nunchaku/assets/nunchaku.svg" width="30%" alt="Nunchaku Logo"/>
+</p>
+
+# Model Card for nunchaku-t5
+
+This repository contains Nunchaku-quantized versions of [T5-XXL](https://huggingface.co/google/t5-v1_1-xxl), used to encode text prompt to the embeddings. It is used to reduce the memory footprint of the model.
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Nunchaku Team
+- **Model type:** text-generation
+- **License:** apache-2.0
+- **Quantized from model:** [t5_v1_1_xxl](https://huggingface.co/google/t5-v1_1-xxl)
+
+### Model Files
+
+- [`awq-int4-flux.1-t5xxl.safetensors`](./awq-int4-flux.1-t5xxl.safetensors): AWQ quantized W4A16 T5-XXL model for FLUX.1.
+
+
+### Model Sources
+
+- **Inference Engine:** [nunchaku](https://github.com/nunchaku-tech/nunchaku)
+- **Quantization Library:** [deepcompressor](https://github.com/nunchaku-tech/deepcompressor)
+- **Paper:** [SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models](http://arxiv.org/abs/2411.05007)
+- **Demo:** [svdquant.mit.edu](https://svdquant.mit.edu)
+
+## Usage
+
+- Diffusers Usage: See [flux.1-dev-qencoder.py](https://github.com/nunchaku-tech/nunchaku/blob/main/examples/flux.1-dev-qencoder.py). Check our [tutorial](https://nunchaku.tech/docs/nunchaku/usage/qencoder.html) for more advanced usage.
+- ComfyUI Usage: See [nunchaku-flux.1-dev-qencoder.json](https://nunchaku.tech/docs/ComfyUI-nunchaku/workflows/t2i.html#nunchaku-flux-1-dev-qencoder-json).
+
+## Citation
+
+```bibtex
+@inproceedings{
+  li2024svdquant,
+  title={SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models},
+  author={Li*, Muyang and Lin*, Yujun and Zhang*, Zhekai and Cai, Tianle and Li, Xiuyu and Guo, Junxian and Xie, Enze and Meng, Chenlin and Zhu, Jun-Yan and Han, Song},
+  booktitle={The Thirteenth International Conference on Learning Representations},
+  year={2025}
+}
+@inproceedings{
+  lin2023awq,
+  title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
+  author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Chen, Wei-Ming and Wang, Wei-Chen and Xiao, Guangxuan and Dang, Xingyu and Gan, Chuang and Han, Song},
+  booktitle={MLSys},
+  year={2024}
+}
+```