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README.md

@@ -1,3 +1,54 @@
----
-license: mit
----
+---
+base_model:
+- ByteDance-Seed/BAGEL-7B-MoT
+datasets:
+- Uni-Edit/Train-Data
+library_name: transformers
+pipeline_tag: any-to-any
+license: apache-2.0
+---
+
+# Uni-Edit-BAGEL Model
+
+This repository contains the Uni-Edit-BAGEL-14B model presented in [Uni-Edit: Intelligent Editing Is A General Task For Unified Model Tuning]().
+
+[**Project Page**]() | [**GitHub Repository**](https://github.com/zhengdian1/Uni-Edit) | [**Paper**]()
+
+# 👀 Intro
+
+<div align="center">
+  <img src="https://github.com/zhengdian1/Uni-Edit/blob/main/assets/teaser.webp?raw=true" alt="Uni-Edit Teaser" width="80%">
+</div>
+
+We introduce **Uni-Edit**, an intelligent image editing task that serves as the **first general task for Unified Multimodal Model (UMM) tuning**. Unlike conventional mixed multi-task training that suffers from inherent task conflicts and requires complex multi-stage pipelines, Uni-Edit breaks this paradigm. It achieves true mutual reinforcement by **improving image understanding, generation, and editing capabilities simultaneously using only one task, one training stage, and one dataset.**
+
+To overcome the limitations of simplistic existing editing data, we propose the **first automated and scalable data synthesis pipeline** for intelligent editing. By transforming diverse VQA data into complex instructions with embedded questions and nested logic, we build **Uni-Edit-148k**, a dedicated dataset pairing reasoning-intensive instructions with high-quality edited images.
+
+Extensive experiments on BAGEL and Janus-Pro demonstrate that tuning solely on Uni-Edit achieves **comprehensive enhancements across all three multimodal capabilities** without requiring any massive data mixing, balancing tricks, or auxiliary operations.
+
+## 🎥 Demo
+
+Refer to our website [[🌐Project Page]]() 
+
+## 🚀 Training and Inference
+
+For detailed instructions on setup, training, inference, evaluation, data construction, please refer to the [official GitHub repository](https://github.com/zhengdian1/Uni-Edit).
+
+**⚠️ IMPORTANT: Custom Architecture**
+Because this is a custom architecture, you **CANNOT** load it directly via `AutoModel.from_pretrained()`. To run the provided inference code, you **MUST** physically merge these shards into a single `ema.safetensors` file on your local machine.
+
+Run the Python script in the [code](https://github.com/zhengdian1/Uni-Edit/merge.py) where you downloaded the repository. 
+*(Note: You need at least 54GB of free system RAM to perform this merge).*
+
+## 📐 Citation
+
+If you find our work helpful for your research, please consider citing our work:
+
+```bibtex
+@article{zheng2026uniedit,
+  title   = {Uni-Edit: Intelligent Editing Is A General Task For Unified Model Tuning},
+  author  = {Zheng, Dian and Zhang, Manyuan and Li, Hongyu and Liu, Hongbo and Zou, Kai and Feng, Kaituo and Li, Hongsheng},
+  journal = {},
+  year    = {2026}
+}
+```

ae.safetensors

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config.json

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+{
+  "name": [
+    "Uni-Edit-BAGEL"
+  ],
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "pad_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
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+  ],
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_p": 0.8,
+  "top_k": 20,
+  "transformers_version": "4.37.0"
+}

llm_config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 1000000.0,
+  "sliding_window": 131072,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.43.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

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tokenizer_config.json

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+}

vit_config.json

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+}