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- unsloth
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- llama
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- gguf
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---
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# Uploaded model
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- **Developed by:** Ken4070TiS
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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- unsloth
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- llama
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- gguf
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datasets:
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- Ken4070TiS/qubit_arXiv
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This model was made by the following step:
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1. Use a web crawler to collect the papers by using arXiv API.
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2. The searching keyword is "qubit AND (IBM OR IQM OR Rigetti)", the time range is 2018 - 2024.
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3. The data was corrected in the JSON with column' Title, Abstract, Authors, arXiv_id, Date, Author_company.
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4. Feed the JSON files to llama-3-8b-bnb-4bit and fine-tune the model by using unsloth
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5. That's it! :)
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# Uploaded model
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- **Developed by:** Ken4070TiS
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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