LLM 101- LLM Powered Applications — Part 4

Integrate LLM into your application. Optimization, Deployment, and improvement of the results using chain-of-thought, ReAct, and LangChain.

In the three preceding blog posts, we covered how to fine-tune our models:

LLM 101 — Generative AI with Large Language Models for Very Beginners — Part 1

LLM 101 — Fine-tuning and evaluating large language models — Part 2

LLM 101 — Reinforcement learning from human feedback (RLHF) with large language models — Part 3

In this blog post, we will see how we can integrate the LLM with our applications. Including the following topics:

• Optimizing a model in order to deploy it to production
• How to overcome a few of the wrong responses of LLM, like out-of-date information, wrong mathematic calculation, and lack of specific data
• How chain of thought can improve the results?
• ReAct
• LangChain

Optimize a model and deploy it for inference

Optimizing a large language model and deploying it for inference involves several steps to ensure efficient and cost-effective deployment while maintaining high performance. Here’s a general guide to the process:

Model Selection and Pruning:
- Choose the appropriate variant of your large language model (e.g., GPT-3, BERT) that suits your use case.
- Consider model pruning techniques to reduce its size without significantly sacrificing performance. Pruning involves removing redundant and less critical weights and neurons from the model.

Post-Training Quantization (PTQ):
- Apply quantization to your model, which converts the model’s floating-point weights into lower-precision fixed-point or integer representations. This reduces the memory footprint and speeds up inference.

Knowledge distillation:
- Knowledge distillation is a model compression technique where a smaller neural network (student) is trained to mimic the predictions of a larger, more complex model (teacher) to transfer its knowledge and achieve similar performance with reduced computational resources.

Hardware Acceleration:
- Choose hardware accelerators that are suitable for your deployment environment. GPUs and TPUs are commonly used for deep learning inference.
- Optimize your model to take full advantage of the selected hardware by utilizing frameworks like TensorFlow, PyTorch, or ONNX Runtime.

Framework Optimization:
- Utilize framework-specific optimization tools and libraries to fine-tune your model for inference. For example, TensorFlow’s TensorFlow Lite for mobile deployment.

Containerization:
- Package your optimized model and its dependencies into a container (e.g., Docker) for easier deployment and scalability.

Serving Framework:
- Choose a serving framework that suits your needs, such as TensorFlow Serving, FastAPI, or Flask.
- Implement your model inference code within the serving framework.

Load Balancing and Scaling:
- Deploy your containerized model to a cloud-based infrastructure like AWS, Azure, or Google Cloud.
- Use load balancing to distribute incoming inference requests across multiple instances for scalability.

Monitoring and Logging:
- Implement real-time monitoring and logging to track model performance, resource utilization, and potential issues.

API Endpoint:
- Create an API endpoint that allows external applications to send inference requests to your deployed model.
- Ensure security measures like authentication and authorization are in place.

Testing and Quality Assurance:
- Thoroughly test your deployed model with representative data to ensure it performs as expected.
- Implement automated testing and continuous integration (CI) to catch issues early.

Cost Optimization:
- Monitor your deployment’s resource usage and optimize for cost-effectiveness by adjusting instance types, scaling policies, and auto-scaling configurations.

Version Control:
- Implement version control for your deployed models and APIs to manage updates and rollbacks effectively.

Documentation:
- Document your deployment process, API endpoints, and usage guidelines for other developers and stakeholders.

Security Auditing:
- Conduct security audits to identify and address potential vulnerabilities in your deployment.

Continuous Improvement:
- Continuously monitor and gather feedback on your deployed model’s performance and user satisfaction.
- Make regular updates and improvements as needed.

LLM wrong inference results

Here are some examples in which the inference gives wrong answers:

1. Out-of-Date Information:

• Scenario: When asked about current events or recent developments, LLMs may provide outdated information because their training data only goes up to a certain date.
• Example: Asking about the current President of a country and receiving the name of the previous President.
• Overcome by: Connect your inference to 3rd party tool/dataset to retrieve more updated information.

2. Wrong Mathematical Calculation:

• Scenario: LLMs may occasionally make mathematical errors or misinterpret mathematical expressions.
• Example: Providing an incorrect result for a simple arithmetic problem like 1938/341=5.72 (instead of 5.68).
• Overcome by: Connect your inference to a calculator function for mathematical calculations (e.g., Pyhton eval). Sometimes called Program-aided language models (PAL). Like connecting a Python code interpreter to the LLM.

3. Wrong Answer:

• Scenario: LLMs can generate factually incorrect or nonsensical answers due to their reliance on patterns in the training data.
• Example: Answering a question about a historical event with a fabricated or inaccurate account.
• Overcome by: Connect your inference to 3rd party tool/dataset to retrieve more information that was not part of the training data or for a more trusted information source like Wikipedia.

Providing the LLM application-specific data

An example of a Large Language Model (LLM) accessing application-specific data could involve a virtual personal assistant that can access a user’s calendar and provide personalized information based on that data. Here’s how this scenario might look:

User: “What meetings do I have scheduled for tomorrow?”

LLM Response: “Tomorrow, you have the following meetings scheduled:

1. 10:00 AM — Team Status Meeting
2. 2:30 PM — Customer Presentation
3. 4:00 PM — Doctor’s Appointment”

Reason and plan with a chain of thought

Refers to enhancing the ability of LLM to engage in logical reasoning and sequential planning, similar to how humans think through a series of steps to solve a complex problem.

Reasoning and Planning: Reasoning is the cognitive process of drawing logical inferences or conclusions based on available information, while planning involves devising a sequence of steps or actions to achieve a specific goal.

Chain of Thought: A “chain of thought” in the context of LLMs refers to a structured sequence of logical steps or actions to address a question or problem. It’s akin to a mental roadmap that guides reasoning and planning.

Challenges for LLMs: LLMs, while proficient in natural language understanding and generation, may face challenges when it comes to complex reasoning and planning tasks. They may struggle to perform sequential tasks or think through multi-step problems.

Helping LLMs Reason and Plan:
— Structured Inputs: Providing LLMs with structured inputs that mimic a chain of thought can help. These inputs can include step-by-step instructions, logical constraints, or context-specific cues.
— Sequential Processing: LLMs can be designed to process information sequentially, much like how humans tackle problems one step at a time.
— Knowledge Integration: LLMs can access external knowledge sources or databases to gather relevant information during their reasoning process.
— Feedback Loops: Incorporating feedback loops into the LLM’s learning process allows it to adjust its reasoning and planning strategies based on past successes and failures.

ReAct Framework

In ReAct Prompting, Yao et al. (2022) introduced a framework where Large Language Models (LLMs) generate reasoning traces and task-specific actions simultaneously. This enables the model to develop action plans, update them, and handle exceptions. Additionally, it allows the LLM to interact with external sources, such as knowledge bases, to gather information for more reliable responses. ReAct outperforms various baselines on language and decision-making tasks, enhancing human interpretability and trust in LLMs. The best results are achieved when combining ReAct with the chain-of-thought (CoT) approach, utilizing internal and external knowledge during reasoning.

Further reading:
https://react-lm.github.io/

LangChain

LangChain is a framework for developing applications powered by language models. It enables applications that are:

• Data-aware: Connect a language model to other sources of data
• Agentic: Allows a language model to interact with its environment

The main value props of LangChain are:

1. Components: abstractions for working with language models and a collection of implementations for each abstraction. Components are modular and easy to use, whether you are using the rest of the LangChain framework or not
2. Off-the-shelf chains: a structured assembly of components for accomplishing specific higher-level tasks

Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains or build new ones for more complex applications and nuanced use cases.

Taken from LangChain introduction in Github: https://python.langchain.com/docs/get_started/introduction.html

Summary

This blog post covered some things to consider when integrating LLM into your applications.

Including an overview of the following topics:

• Optimizing a model in order to deploy it to production
• How to overcome a few of the wrong responses of LLM, like out-of-date information, wrong mathematic calculation, and lack of specific data
• How chain of thought can improve the results?
• ReAct
• LangChain

I’m Eylon Steiner, Engineering Manager for Infostrux Solutions. You can follow me on LinkedIn.

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