Update README.md

README.md

@@ -111,6 +111,7 @@ Please note that the code assumes you have access to the Starcodium/VergilGPT2 m
 ## Installation
 
 Make sure to install the required dependencies by running the following commands:
+(Note these installations were done in google collaboratory, if you are installing them on your local PC take out the '!')
 
 ```python
 !pip install torch
@@ -145,24 +146,6 @@ tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id)
 ```
 
-For loading the original GPT2 model in 4-bit and applying quantization for better results, as well as utilizing bfloat16 compute dtype and nested quantization for memory efficiency during model loading, use the following example:
-
-```python
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
-
-model_id = "gpt2"
-bnb_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_use_double_quant=True,
-    bnb_4bit_quant_type="nf4",
-    bnb_4bit_compute_dtype=torch.bfloat16
-)
-
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model_4bit = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map="auto")
-```
-
 To load the GPT2 model with the allenai/soda dataset, follow this example:
 
 ```python
@@ -192,7 +175,7 @@ dataset = dataset.map(preprocess_dataset)
 
 ## Loading & Training VergilGPT2
 
-To load the original VergilGPT2 model for training, you can use the following example:
+To load the VergilGPT2 model for training, you can use the following example:
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
@@ -201,24 +184,6 @@ tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id)
 ```
 
-For loading the VergilGPT2 model in 4-bit and applying quantization for better results, as well as utilizing bfloat16 compute dtype and nested quantization for memory efficiency during model loading, use the following example:
-
-```python
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
-
-model_id = "VergilGPT2"
-bnb_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_use_double_quant=True,
-    bnb_4bit_quant_type="nf4",
-    bnb_4bit_compute_dtype=torch.bfloat16
-)
-
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model_4bit = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map="auto")
-```
-
 To load the VergilGPT2 model with the allenai/soda dataset, follow this example:
 
 ```python
@@ -249,7 +214,7 @@ dataset = dataset.map(preprocess_dataset)
 train_dataset, val_dataset = train_test_split(dataset['train'], test_size=0.1, shuffle=True)
 ```
 
-It is worth noting that VergilGPT2 is already trained on the allensi/soda dataset so in actual training be sure to change the conversational dialogue.
+It is worth noting that VergilGPT2 is already trained on the allenai/soda dataset so in actual training be sure to change the conversational dialogue.
 
 ## Text Files