Update README.md

README.md

@@ -7,7 +7,7 @@ StellarX is a powerful autoregressive language model designed for various natura
 
 ## Model Details
 
-- **Training Data:** StellarX is trained on a large-scale dataset provided by "redpajama" maintained by the group "togethercumputer." This dataset has been instrumental in shaping StellarX's language capabilities and general-purpose understanding.
+- **Training Data:** StellarX is trained on a large-scale dataset provided by "redpajama" maintained by the group "togethercomputer." This dataset has been instrumental in shaping StellarX's language capabilities and general-purpose understanding.
 - **Model Architecture:** StellarX is built upon the GPT-NeoX architecture, which may, be, inspired by GPT-3 and shares similarities with GPT-J-6B. The architecture incorporates key advancements in transformer-based language models, ensuring high-quality predictions and contextual understanding.
 - **Model Size:** StellarX consists of approximately 4 billion parameters, making it a highly capable language model for a wide range of natural language processing tasks.
 - **Carbon-Friendly and Resource-Efficient:** StellarX has been optimized for carbon efficiency and can be comfortably run on local devices. When loaded in 8 bits, the model requires only about 5GB of storage, making it more accessible and convenient for various applications.
@@ -52,7 +52,7 @@ GPT-NeoX-20B, a sibling model to StellarX, is a 20 billion parameter autoregress
 
 ## Training and Evaluation
 
-StellarX's training dataset comprises a comprehensive collection of English-language texts, covering various domains, thanks to the efforts of "redpajama" dataset by the group "togethercumputer" group.
+StellarX's training dataset comprises a comprehensive collection of English-language texts, covering various domains, thanks to the efforts of "redpajama" dataset by the group "togethercomputer" group.
 
 Evaluation of GPT-NeoX 20B performance has demonstrated its competence across different natural language tasks. Although since this description provides a brief summary, we refer to the GPT-NeoX Paper https://arxiv.org/abs/2204.06745, comparing GPT-NeoX 20B to other models on tasks such as OpenAI's LAMBADA, SciQ, PIQA, TriviaQA, and ARC Challenge.
 
@@ -66,7 +66,7 @@ Furthermore, StellarX is not limited to the English language if trained properly
 
 Lastly, users should be aware of potential biases and limitations inherent in
 
-Special thanks to the group that created the training dataset. The Redpajama dataset, used to train StellarX, thank you togethercumputer.
+Special thanks to the group that created the training dataset. The Redpajama dataset, used to train StellarX, thank you togethercomputer.
 
 ## Community and Support
 
@@ -77,7 +77,7 @@ server and engage in discussions in the #questions channel. It is recommended to
 
 StellarX, a base language model developed by the Dampish, offers impressive language capabilities and flexibility. Trained on an extensive dataset and built upon the GPT-NeoX architecture, StellarX excels in various natural language processing tasks. Its carbon-friendly and resource-efficient design makes it accessible for local device deployment. Researchers and enthusiasts can freely explore StellarX for research purposes and personal use, while commercial users should adhere to the licensing terms.
 
-**Again i am really grateful for the data made by togethercumputers and their willingness to opensource, they inspired this project and sparked the idea in Stellar-models, i am truly really really grateful to them.
+**Again i am really grateful for the data made by togethercomputers and their willingness to opensource, they inspired this project and sparked the idea in Stellar-models, i am truly really really grateful to them.
 -dampish**