Update README.md

README.md

@@ -18,7 +18,7 @@ tags:
 ---
 
 # 🦅 Supra Mini v5 8M
-Supra Mini **v5** 8M is a very tiny base model trained on **5 billion** tokens of Fineweb-Edu for 2 epochs as the **fifth version** of our Supra Mini series.
+Supra Mini **v5** 8M is a very small model trained on **5 billion** tokens of Fineweb-Edu for 2 epochs as the **fifth version** of our Supra Mini series. SupraMini-8M shows improvements across all benchmarks because of its larger size and training budget.
 
 ## Model Config
 
@@ -35,7 +35,7 @@ Supra Mini **v5** 8M is a very tiny base model trained on **5 billion** tokens o
 - Trained in bfloat16
 
 ## Final Loss
-This model reached a final train loss after 2 epochs of **4.414**.
+This model reached a final CrossEntropy loss (on the train set) of **4.414**.
 
 ## Benchmarks
 
@@ -69,12 +69,13 @@ So, why does that sound? Because there's something new than a few different thin
 \- "You have no idea where I'm so, but if my son has any thought or understanding of his name, he will be able to understand him by saying something more than one day. This way they can tell us when you've got me at home. That is why I want your child to know which words are most important for them: "If you get this language from another person, then you'll find yourself in the same place as you read it," says Mike McNamara, who was born with an English friend, Jennifer Batharinee, who had been diagnosed with dementia during her lifetime. He said he would learn how to say things such as "a lot of things," and "you don't really need to do anything else." It may seem simple because he didn't feel good before he went out and asked whether he could make sense of it. But he wanted to take advantage of the fact"*
 
 ## Usage
-To use our model, just run this code using HF Transformers to execute the model:
+To use our model, just run this code:
+
 ```python3
 from transformers import pipeline
 import torch
 
-print("[*] Loading Supra Mini v5 8M model from Hugging Face Hub...")
+print("Loading Supra Mini v5 8M model from Hugging Face...")
 pipe = pipeline(
     "text-generation", 
     model="SupraLabs/Supra-Mini-v5-8M",
@@ -101,8 +102,19 @@ print(f"\nPrompt: {test_prompt}")
 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 v5 8M on a single NVIDIA RTX 5060 Ti 16GB in ~11 hours for 2 epochs.<br>
 The full training code can be found in this repo as `train_tokenizer.py` (train costum BPE tokenizer with vocab size of 16384), `train.py` (train the model) and `inference.py` (test the model).<br>
-The model was trained on the first 5 billion tokens of Sample-10BT from Fineweb-Edu using streaming tokenization. 
+The model was trained on the first 5 billion tokens of Sample-10BT from Fineweb-Edu.