diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..467b6911eaf59eb307847200a56651220622fa4a
--- /dev/null
+++ b/README.md
@@ -0,0 +1,93 @@
+---
+base_model:
+  - HiDream-ai/HiDream-I1-Full
+base_model_relation: quantized
+pipeline_tag: text-to-image
+tags:
+- dfloat11
+- df11
+- lossless compression
+- 70% size, 100% accuracy
+---
+
+# DFloat11 Compressed Model: `HiDream-ai/HiDream-I1-Full`
+
+This is a **DFloat11 losslessly compressed** version of the original `HiDream-ai/HiDream-I1-Full` model. It reduces model size by **30%** compared to the original BFloat16 model, while maintaining **bit-identical outputs** and supporting **efficient GPU inference**.
+
+🔥🔥🔥 Thanks to DFloat11 compression, HiDream-I1-Full can now run smoothly on a single 32GB GPU without any quality loss. 🔥🔥🔥
+
+### 📊 Performance Comparison
+
+| Metric                                          | HiDream-I1-Full (BFloat16) | HiDream-I1-Full (DFloat11) |
+| ----------------------------------------------- | ------------------- | ------------------- |
+| Model Size                                      | 34.21 GB            | 24.19 GB            |
+| Peak GPU Memory<br>(1024×1024 image generation) | 35.61 GB            | 26.42 GB            |
+| Generation Time<br>(A100 GPU)                   | 140 seconds          | 161 seconds          |
+
+### 🔧 How to Use
+
+1. Install or upgrade the DFloat11 pip package *(installs the CUDA kernel automatically; requires a CUDA-compatible GPU and PyTorch installed)*:
+
+    ```bash
+    pip install -U dfloat11[cuda12]
+    # or if you have CUDA version 11:
+    # pip install -U dfloat11[cuda11]
+    ```
+
+2. Install or upgrade the diffusers library.
+
+    ```bash
+    pip install -U diffusers
+    ```
+
+3. To use the DFloat11 model, run the following example code in Python:
+
+    ```python
+    import torch
+    from transformers import AutoTokenizer
+    from diffusers import HiDreamImagePipeline
+    from dfloat11 import DFloat11Model
+
+    tokenizer_4 = AutoTokenizer.from_pretrained("DFloat11/Llama-3.1-8B-Instruct-DF11")
+    text_encoder_4 = DFloat11Model.from_pretrained("DFloat11/Llama-3.1-8B-Instruct-DF11", device="cpu")
+    text_encoder_4.config.output_hidden_states = True
+    text_encoder_4.config.output_attentions = True
+
+    pipe = HiDreamImagePipeline.from_pretrained(
+        "HiDream-ai/HiDream-I1-Full",
+        tokenizer_4=tokenizer_4,
+        text_encoder_4=text_encoder_4,
+        torch_dtype=torch.bfloat16,
+    )
+    DFloat11Model.from_pretrained(
+        "DFloat11/HiDream-I1-Full-DF11",
+        device="cpu",
+        bfloat16_model=pipe.transformer,
+    )
+    pipe.enable_model_cpu_offload()
+
+    image = pipe(
+        'A cat wearing a vintage astronaut suit, floating inside a spaceship and gazing out the window at Earth.',
+        height=1024,
+        width=1024,
+        guidance_scale=5.0,
+        num_inference_steps=50,
+        generator=torch.Generator("cuda").manual_seed(0),
+    ).images[0]
+    image.save("output.png")
+    ```
+
+
+### 🔍 How It Works
+
+We apply **Huffman coding** to losslessly compress the exponent bits of BFloat16 model weights, which are highly compressible (their 8 bits carry only ~2.6 bits of actual information). To enable fast inference, we implement a highly efficient CUDA kernel that performs on-the-fly weight decompression directly on the GPU.
+
+The result is a model that is **~30% smaller**, delivers **bit-identical outputs**, and achieves performance **comparable to the original** BFloat16 model.
+
+Learn more in our [research paper](https://arxiv.org/abs/2504.11651).
+
+### 📄 Learn More
+
+* **Paper**: [70% Size, 100% Accuracy: Lossless LLM Compression for Efficient GPU Inference via Dynamic-Length Float](https://arxiv.org/abs/2504.11651)
+* **GitHub**: [https://github.com/LeanModels/DFloat11](https://github.com/LeanModels/DFloat11)
+* **HuggingFace**: [https://huggingface.co/DFloat11](https://huggingface.co/DFloat11)
diff --git a/config.json b/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..3d55a530775506301d7a5583bfdd54c864feb98a
--- /dev/null
+++ b/config.json
@@ -0,0 +1,64 @@
+{
+    "dfloat11_config": {
+      "bytes_per_thread": 8,
+      "pattern_dict": {
+        "double_stream_blocks\\.\\d+": [
+            "block.adaLN_modulation.1",
+            "block.attn1.to_q",
+            "block.attn1.to_k",
+            "block.attn1.to_v",
+            "block.attn1.to_out",
+            "block.attn1.to_q_t",
+            "block.attn1.to_k_t",
+            "block.attn1.to_v_t",
+            "block.attn1.to_out_t",
+            "block.ff_i.shared_experts.w1",
+            "block.ff_i.shared_experts.w2",
+            "block.ff_i.shared_experts.w3",
+            "block.ff_i.experts.0.w1",
+            "block.ff_i.experts.0.w2",
+            "block.ff_i.experts.0.w3",
+            "block.ff_i.experts.1.w1",
+            "block.ff_i.experts.1.w2",
+            "block.ff_i.experts.1.w3",
+            "block.ff_i.experts.2.w1",
+            "block.ff_i.experts.2.w2",
+            "block.ff_i.experts.2.w3",
+            "block.ff_i.experts.3.w1",
+            "block.ff_i.experts.3.w2",
+            "block.ff_i.experts.3.w3",
+            "block.ff_t.w1",
+            "block.ff_t.w2",
+            "block.ff_t.w3"
+          ],
+        "single_stream_blocks\\.\\d+": [
+            "block.adaLN_modulation.1",
+            "block.attn1.to_q",
+            "block.attn1.to_k",
+            "block.attn1.to_v",
+            "block.attn1.to_out",
+            "block.ff_i.shared_experts.w1",
+            "block.ff_i.shared_experts.w2",
+            "block.ff_i.shared_experts.w3",
+            "block.ff_i.experts.0.w1",
+            "block.ff_i.experts.0.w2",
+            "block.ff_i.experts.0.w3",
+            "block.ff_i.experts.1.w1",
+            "block.ff_i.experts.1.w2",
+            "block.ff_i.experts.1.w3",
+            "block.ff_i.experts.2.w1",
+            "block.ff_i.experts.2.w2",
+            "block.ff_i.experts.2.w3",
+            "block.ff_i.experts.3.w1",
+            "block.ff_i.experts.3.w2",
+            "block.ff_i.experts.3.w3"
+          ]
+      },
+      "threads_per_block": [
+        512
+      ],
+      "version": "0.2.0"
+    },
+    "model_type": "llama"
+  }
+  
\ No newline at end of file
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