Update README.md

README.md

@@ -19,7 +19,7 @@ new_version: SupraLabs/Supra-Mini-v4-2M
 ---
 
 # 🦅 Supra Mini v2 0.1M
-Supra Mini **v2** 0.1M is a very tiny base model trained on 700 million tokens of Fineweb-Edu for 3 epochs as the **second version** of our Supra Mini series.
+Supra Mini **v2** 0.1M is a very, and we mean very small base model trained on 700 million tokens of Fineweb-Edu for 3 epochs as the **second version** of our Supra Mini series.
 
 ## Model Config
 
@@ -35,7 +35,7 @@ Supra Mini **v2** 0.1M is a very tiny base model trained on 700 million tokens o
 - Weight Decay: 0.01
 
 ## Final Loss
-This model reached a final train loss after 3 epochs of **4.413**.
+This model reached a final train loss of **4.413**.
 
 ## Benchmarks
 
@@ -97,6 +97,18 @@ print("-" * 30)
 print("\nOutput:\n" + generate_text(test_prompt))
 ```
 
+## Use cases
+
+1. Educational research
+2. deployment or testing/fine-tuning on edge environments
+3. Or more simply, for fun
+
+## Limitations
+
+1. Cannot reason, chat, or code
+2. Incoherent more often than not
+3. Mostly unfactual
+
 ## Training guide
 We trained Supra Mini v2 0.1M on a single T4 GPU in ~2 hours for 3 epochs.<br>
 The full training code can be found in this repo as `run.sh` (easily run the complete pipeline), `train_tokenizer.py` (train costum BPE tokenizer with vocab size of 2048), `train.py` (train the model) and `inference.py` (test the model).<br>