Extra Large Language Models Benchmarking for Medicinal Chemistry
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| Format: | Recurso digital |
| Langue: | anglais |
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Zenodo
2024
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| _version_ | 1866901396126695424 |
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| author | Kawchak, Kevin |
| author_facet | Kawchak, Kevin |
| contents | <p>Purpose: Determine which unmodified Extra Large Language Model has the greatest speed and generation quality answering medicinal chemistry prompts. Plan: Use OpenAI API for GPT 4o, and NVIDIA NIM API for Nemotron 4, Mixtral, and Arctic. To judge generations, use ChatGPT 4o and Llama 3 70B Groq. Experimental: In a Colab notebook for either OpenAI API or NIM API, answer 10 medicinal chemistry prompts. Manually process all generations, and further analyze the first three prompts three times each with both judges.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14968018 |
| institution | Zenodo |
| language | eng |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Extra Large Language Models Benchmarking for Medicinal Chemistry Kawchak, Kevin OpenAI NVIDIA Mistral Drug Synthesis LLM Prompt Engineering <p>Purpose: Determine which unmodified Extra Large Language Model has the greatest speed and generation quality answering medicinal chemistry prompts. Plan: Use OpenAI API for GPT 4o, and NVIDIA NIM API for Nemotron 4, Mixtral, and Arctic. To judge generations, use ChatGPT 4o and Llama 3 70B Groq. Experimental: In a Colab notebook for either OpenAI API or NIM API, answer 10 medicinal chemistry prompts. Manually process all generations, and further analyze the first three prompts three times each with both judges.</p> |
| title | Extra Large Language Models Benchmarking for Medicinal Chemistry |
| topic | OpenAI NVIDIA Mistral Drug Synthesis LLM Prompt Engineering |
| url | https://doi.org/10.5281/zenodo.14968018 |