Update README.md

README.md

@@ -13,13 +13,13 @@ library_name: torchao
 pipeline_tag: text-to-3d
 ---
 
-# 🚀 First INT4 Quantized Cube3D — Run on Half the VRAM
+# 🚀 First INT4 Quantized Cube3D - Run on Half the VRAM
 
 ![](https://img.shields.io/badge/VRAM_-14.3_GB_(44%25↓)-brightgreen)
-![](https://img.shields.io/badge/Loading-88%25_faster-blue)
-![](https://img.shields.io/badge/Latency-same-green)
+![](https://img.shields.io/badge/Loading-88%25_Faster-blue)
+![](https://img.shields.io/badge/Latency-Same-green)
 
-Presenting the **first INT4 quantized version** of [Cube3D v0.5](https://huggingface.co/Roblox/cube3d-v0.5), a text-to-3D mesh generative model. Quantized via **RTN W4A16** (group_size=128) using [torchao](https://github.com/pytorch/ao), it cuts peak VRAM from **25.4 GB → 14.3 GB (44%↓)** while maintaining the same inference speed and comparable shape fidelity — enabling 3D shape generation on much smaller, more accessible GPUs.
+Presenting the **first INT4 quantized version** of [Cube3D v0.5](https://huggingface.co/Roblox/cube3d-v0.5), a text-to-3D mesh generative model. Quantized via **RTN W4A16** (group_size=128) using [torchao](https://github.com/pytorch/ao), it cuts peak VRAM from **25.4 GB → 14.3 GB (44%↓)** while maintaining the same inference speed and comparable shape fidelity - enabling 3D shape generation on much smaller, more accessible GPUs.
 
 | | BF16 + Engine | BF16 + EngineFast | **INT4 + EngineFast** |
 |---|:-:|:-:|:-:|
@@ -27,7 +27,8 @@ Presenting the **first INT4 quantized version** of [Cube3D v0.5](https://hugging
 | 📦 Setup time | 19.4 s | 206.9 s | **25.1 s (88%↓)** |
 | ⏱️ Latency | 90.9 s | 15.0 s | **14.2 s** |
 
-> 💡 The 44% VRAM reduction means this model now fits on a single 16 GB GPU (e.g. NVIDIA L4, A10 etc.), bringing high-quality text-to-3D generation to individual researchers and end-user hardware.
+<mark>💡 The 44% VRAM reduction means this model now fits on a single 16 GB GPU (e.g. NVIDIA L4, A10, A2 etc.), bringing high-quality text-to-3D generation to individual researchers and end-user hardware.
+</mark>
 
 ### Original BF16 vs Quantized INT4 Comparisons:
 ##### A. Easy Categories (7)
@@ -38,7 +39,7 @@ Presenting the **first INT4 quantized version** of [Cube3D v0.5](https://hugging
 ![Complex categories](assets/complex.gif)
 
 
-# Cube3D v0.5 — RTN W4A16 INT4 (torchao)
+# Cube3D v0.5 - RTN W4A16 INT4 (torchao)
 
 Post-training quantized version of [Roblox/cube3d-v0.5](https://huggingface.co/Roblox/cube3d-v0.5), a text-to-3D mesh generative model.  
 Quantization method: **RTN W4A16**, group_size=128, via [torchao](https://github.com/pytorch/ao) `int4_weight_only`.
@@ -52,16 +53,15 @@ Quantization method: **RTN W4A16**, group_size=128, via [torchao](https://github
 | `open_model_v0.5.yaml` | tiny | Model architecture config |
 | `quant_config.json` | tiny | Quantization metadata |
 
-The BF16 GPT weights (`shape_gpt.safetensors`) are **not** included here — they live in the parent repo and are only needed to reconstruct the model skeleton for loading.
 
-## Benchmark (NVIDIA A100-SXM4-40GB, 15-categories)
+## New Benchmarking Dataset (15-categories, 170 prompts)
 
 ### Shape Quality (Chamfer Distance, 15 categories, 170 prompts):
 
-Median CD: 67.9 × 10⁻³
+Median Chamfer Distance: 67.9 × 10⁻³
 
 Best categories: `vehicle_land` (41.4), `geometric_primitive` (46.5), `animal_wild` (53.8).  
-Complex categories: `symmetry_topology` (205.8), `abstract_mathematical` (167.9) — high variance: RTN INT4 rounding hurts topologically complex shapes.
+Complex categories: `symmetry_topology` (205.8), `abstract_mathematical` (167.9) - high variance.
 
 
 | Category | Mean | Std | n |
@@ -94,7 +94,7 @@ torchaudio==2.10.0
 torchao==0.10.0
 ```
 
-The `.pt` file is a torchao pickle, torchao enables kernel-supported INT4 inference.
+The .pt file is a torchao pickle, torchao enables kernel-supported INT4 inference.
 
 ## Usage
 Please see the Google Colab tutorial.