Update README.md

README.md

@@ -3,15 +3,72 @@ license: mit
 datasets:
 - ylecun/mnist
 tags:
-- image-generation
-- supralabs
 - harley-ml
+- image
+- digit-to-image
 - mnist
 - small
-- digit-to-image
 - text-to-image
+- supralabs
 ---
 
-This is SupraMNiST-IMG-200k, a model trained to generate an image based on an input number (0-9).
+# **SupraMNiST-IMG-200k**
+
+## Sumary
+
+```
+Task: Number-To-Image
+Dataset: ylecun/mnist
+Total training time: ~8 minutes
+Inputs: Number (0-9) 
+Outputs: 32x32 image
+Params: ~201k
+Framework: PyTorch, diffusers
+Author: SupraLabs
+```
+
+## **Description**
+MNiST-IMG-200k is an ~**200k parameter model** trained to **generate an image** based on an **input number (0-9)**. 
+
+## **Training**
+
+### **Hardware**
+
+MNiST-IMG was trained on Google Colaboratory (NVIDA Tesla T4) for ~8 minutes with a batch size of 64 for 10 epochs.
+
+### **Dataset**
+
+[ylecun/mnist](https://huggingface.co/ylecun/mnist)
+
+### **Training Results**
+
+Loss ended at ~**0.40**.
+
+Note: I can't provide the raw training logs as I loss it somehwere after training. Sorry!
+
+## **Generation Examples**
+
+At **1000** decoding steps:
+
+![1000 Decoding Step Digit Image Generation](images/digit_image_samples_1000s.png)
+
+At **200** decoding steps:
+
+![1000 Decoding Step Generation Image](images/digit_image_samples_200s.png)
+
+# Inference
+
+Use the script in the repo. [inference.py](https://huggingface.co/Harley-ml/MNIST-IMG-390k/blob/main/inference.py)
+
+### Related Models
+
+# Citation
 
-Note: We will finish this model card later.
+```bibtex
+@misc{mnist-img-390k,
+  title     = {MNIST-IMG-390k: a Tiny Diffusion Model for Generating Handwritten Digits},
+  author    = {Paul Courneya; Harley-ml},
+  year      = {2026},
+  url       = {https://huggingface.co/Harley-ml/MNIST-IMG-390k}
+}
+```