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- **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.
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- **V0** Meaning what version it is on, currently version 0, we are currently working on the next more complete version however please dont have it deteer you as the current version is not complete and we seek to improve the model by chaning the training the model in a different way. The next version aims to have a way higher accuracy.
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## Licensing and Usage
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StellarX, developed by the Dampish, is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC-BY-NC-SA-4.0). This license ensures that you can utilize the model for research purposes and personal use without any restrictions, while also promoting the sharing and adaptation of the model under certain conditions.
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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.
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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.
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- **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.
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- **V0** Meaning what version it is on, currently version 0, we are currently working on the next more complete version however please dont have it deteer you as the current version is not complete and we seek to improve the model by chaning the training the model in a different way. The next version aims to have a way higher accuracy.
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## How to Use
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To load StellarX using the Hugging Face Transformers library, you can use the following code snippet:
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("Dampish/StellarX")
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model = AutoModelForCausalLM.from_pretrained("Dampish/StellarX")
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```
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This model is particularly beneficial for those seeking a language model that is powerful, compact, and can be run on local devices without a hefty carbon footprint. Remember, when considering Darius1, it's not just about the impressive numbers—it's about what these numbers represent: powerful performance, optimized resources, and responsible computing.
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**For any queries related to this model, feel free to reach out to "Dampish#3607" on discord.**
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## Licensing and Usage
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StellarX, developed by the Dampish, is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC-BY-NC-SA-4.0). This license ensures that you can utilize the model for research purposes and personal use without any restrictions, while also promoting the sharing and adaptation of the model under certain conditions.
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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.
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**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.
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-dampish**
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