🔴 Live MLOps Podcast – Building, Deploying and Monitoring Large Language Models with Jinen Setpal
Sep 6, 2023
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Jinen Setpal, ML Engineer at DagsHub, discusses building, deploying, and monitoring large language models. They explore the DPT chatbot project, evaluation methods, reducing hallucinations, improving inference speed, and monitoring language models in production.
Customizing large language models through prompt engineering and fine-tuning offers flexibility in tailoring responses to specific tasks and domains.
Human annotators play a crucial role in evaluating the performance of large language models, relying on their generalization capacity and the subjective nature of language understanding.
Successful deployment of large language models requires optimizing infrastructure, considering factors like latency, quantization, and specialized hardware, to achieve a balance between accuracy and speed.
Deep dives
Large Language Models and their Impact
Large language models have become more prominent in recent years, with the release of models like Chat GPT. These models have the ability to provide utilitarian solutions and demonstrate emergent behaviors. While they can be exciting, the challenge lies in their closed-source nature, causing limitations for access and usage. However, the deployment process has become more streamlined with cloud hosting services like OpenAI's API, making it easier to integrate large language models into applications.
Customization and Evaluation of Large Language Models
Prompt engineering and fine-tuning are two approaches to customize large language models. Prompt engineering involves crafting specific instructions to improve model responses, while fine-tuning adapts the model to a domain-specific task. The choice between the two depends on factors such as budget and the level of domain adaptation required. Evaluation of large language models largely relies on human annotators due to the model's generalization capacity and the subjective nature of language understanding. While metrics like BLEU are used for some tasks, human evaluators play a critical role in assessing model performance.
Challenges and Considerations in Large Language Model Deployment
The deployment of large language models requires considerations such as latency and optimization. Quantization, which reduces the precision of inference computations, can significantly improve speed without significant loss in accuracy. Hosting models on specialized hardware or utilizing smaller models may also contribute to faster inference. Balancing accuracy and latency in deployment is essential, and optimizing infrastructure and annotation processes can help achieve better performance.
Importance of active learning pipeline for LLMs
Having an active learning pipeline is crucial for LLMs because it allows for a more dynamic training process. Language itself evolves rapidly, and even the API for documentation models continuously changes. An active learning pipeline facilitates the back-and-forth between training, fine-tuning, and human evaluation, leading to better models and reduced hallucinations.
Challenges and techniques for combating hallucinations in LLMs
Hallucinations are a persistent problem in LLMs due to misaligned incentives and the lack of a clear objective function. One approach to addressing hallucinations is through Bayesian neural networks, which create an ensemble of models with confidence levels representative of accuracy. Another method is domain adaptation, which involves adding better data, expanding the language model's definition, and reducing the temperature of inference. Furthermore, breaking down documents into smaller sections can improve context understanding and mitigate hallucinations caused by cropping out relevant information.
In this live episode, I'm speaking with Jinen Setpal, ML Engineer at DagsHub about actually building, deploying, and monitoring large language model applications.
We discuss DPT, a chatbot project that is live in production on the DagsHub Discord server and helps answer support questions and the process and challenges involved in building it. We dive into evaluation methods, ways to reduce hallucinations and much more.
We also answer the audience's great questions.
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