"Implementing a Large Language Model (LLM) for Consistent and Efficient Proposals 
 
This white paper examines the potential benefits of employing large language models (LLMs) to enhance the efficiency and consistency of our proposal generation process. Creating accurate and reliable proposals is a critical component of our operations, establishing the basis for project success while limiting risk. Implementing LLMs, like RedPajama 3B/7B or HuggingFace, can help standardize the proposal writing process and maintain consistency in language and style across various documents. The objective of this paper is to explore the potential of open-source LLMs in comparison to alternatives such as OpenAI's GPT4 API, and address the costs, benefits, and risks in developing this process. 
 
Fine-tuning Open Source LLMs with Proprietary Data: 
 
To implement an LLM for proposal generation, these are the likely steps we would take, broken into two phases. The first is data collection, which is the most critical task in the near term. The second is implementing, fine tuning, and deployment of the LLM. Because LLM’s are evolving rapidly, some of the recommendations for Phase 2 may adjust as open-source and commercial offerings expand, and all the legal issues around training data become clearer. 
 
Phase 1: Data Collection and Curation (60-100 hours) 
 
In this initial phase, the primary focus is collecting and curating a high-quality dataset of historical proposals, which serves as a valuable resource for the company, regardles In your response, include at most 6 words that are fully capitalized to emphasize key points.
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