Ether0:q2_k
Language Models (LLM) Apache-2.0 (Free)
About
Scientific research language model fine-tuned for chemistry, biology, and materials science. Based on Qwen2.5-24B with Q2_K quantization optimized for single-CPU execution, enabling research workflows on standard hardware.
Citation
Narayanan, S.M. et al. Training a Scientific Reasoning Model for Chemistry. arXiv:2506.17238 (2025). DOI:10.48550/arXiv.2506.17238
Frequently Asked Questions
What is Ether0:q2_k?
Ether0:q2_k is a language models (llm) application available in the Paramus App Store. Scientific research language model fine-tuned for chemistry, biology, and materials science. Based on Qwen2.5-24B with Q2_K quantization optimized for single-CPU execution, enabling research workflows on standard hardware.
Is Ether0:q2_k free to use?
Yes. Ether0:q2_k is distributed under the Apache-2.0 (Free) license and is available at no cost through the Paramus App Store.
How do I install Ether0:q2_k?
Ether0:q2_k is installed through Paramus Chemistry OS, an on-premise Windows platform for computational chemistry. Open the Paramus App Store in your local installation and select Ether0:q2_k for one-click deployment.
What type of application is Ether0:q2_k?
Ether0:q2_k belongs to the “Language Models (LLM)” category in the Paramus App Store. It runs on Paramus Chemistry OS and can also be accessed through Paramus Cloud for supported workflows.
What platform does Ether0:q2_k run on?
Ether0:q2_k runs on Paramus Chemistry OS, a Windows-based on-premise platform that provides local compute power for demanding simulations. It requires a Paramus OS installation with appropriate hardware resources.
Can Ether0:q2_k be automated or integrated with AI workflows?
Yes. Ether0:q2_k is available as part of the Paramus ecosystem which supports MCP (Model Context Protocol) tools for AI-driven automation. This enables integration with large language models and automated research pipelines.
How should I cite Ether0:q2_k in publications?
The recommended citation for Ether0:q2_k is: Narayanan, S.M. et al. Training a Scientific Reasoning Model for Chemistry. arXiv:2506.17238 (2025). DOI:10.48550/arXiv.2506.17238