Update README.md

README.md

@@ -12,33 +12,25 @@ pipeline_tag: text-generation
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 # ManaGPT-1010
-<img style="float:right; margin:10px; margin-right:30px" src="https://huggingface.co/NeuraXenetica/ManaGPT-1010/resolve/main/ManaGPT_logo_01.png" width="150" height="150">
-This model is a fine-tuned version of GPT-2 that has been trained on a custom dataset of scholarly and popular texts from the field of organizational management that relate to the emerging effects of posthumanizing technologies (e.g., relating to advanced artificial intelligence, social robotics, virtual reality, neuroprosthetics, and cyber-physical systems) on the structure of organizations and human beings’ experience of organizational life.
+<img style="float:right; margin:10px; margin-right:30px" src="https://huggingface.co/NeuraXenetica/ManaGPT-1010/resolve/main/ManaGPT_logo_01.png" width="150" height="150"></img>
+_(Please note that ManaGPT-1010 has been superseded by **[ManaGPT-1020](https://huggingface.co/NeuraXenetica/ManaGPT-1020)**, which has been fine-tuned on a dataset roughly 6.45 times the size of that used to fine-tune ManaGPT-1010.)_
+
+**ManaGPT-1010** is an experimental open-source text-generating AI designed to offer insights on the role of emerging technologies in organizational management.
 
 ## Model description
 
-More information needed
+The model is a fine-tuned version of GPT-2 that has been trained on a custom corpus of scholarly and popular texts from the field of organizational management that relate to ongoing effects of posthumanizing technologies (e.g., relating to advanced artificial intelligence, social robotics, virtual reality, neuroprosthetics, and cyber-physical systems) on the structure of organizations and human beings’ experience of organizational life.
 
 ## Intended uses & limitations
 
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
+This model has been designed for experimental research purposes; it isn’t intended for use in a production setting or in any sensitive or potentially hazardous contexts.
 
-### Training hyperparameters
+## Training procedure and hyperparameters
 
-The following hyperparameters were used during training:
+The model was trained using a Tesla T4 with 16GB of GPU memory. The following hyperparameters were used during training:
 - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'ExponentialDecay', 'config': {'initial_learning_rate': 0.0005, 'decay_steps': 500, 'decay_rate': 0.95, 'staircase': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
 - training_precision: float32
 
-### Training results
-
-
-
 ### Framework versions
 
 - Transformers 4.27.1