Updated README with PyTorch Lightning checkpoint instructions and improved usage examples

README.md

@@ -70,9 +70,8 @@ pip install torch>=2.0.0 transformers>=4.35.0 huggingface-hub>=0.17.0
 ```python
 from huggingface_hub import hf_hub_download
 
-# Download model files
+# Download essential model files
 model_path = hf_hub_download(repo_id="Marks-lab/LOL-EVE", filename="pytorch_model.bin")
-config_path = hf_hub_download(repo_id="Marks-lab/LOL-EVE", filename="config.json")
 tokenizer_path = hf_hub_download(repo_id="Marks-lab/LOL-EVE", filename="tokenizer.json")
 ```
 
@@ -82,19 +81,15 @@ Since this model uses a custom architecture, you'll need to load it using PyTorc
 
 ```python
 import torch
-import json
 
-# Load configuration
-with open(config_path, 'r') as f:
-    config = json.load(f)
-
-# Load model state dict
+# Load model weights
 model_state = torch.load(model_path, map_location='cpu')
 
-print(f"Model type: {config['model_type']}")
-print(f"Layers: {config['num_layers']}")
-print(f"Embedding dimension: {config['num_embd']}")
 print(f"Model parameters: {sum(p.numel() for p in model_state.values()):,}")
+print(f"Model size: ~2.6GB")
+
+# To use the model, you'll need to implement the LOLEVEForCausalLM class
+# and load these weights into your model instance
 ```
 
 ## Testing the Model