Skip to content

Commit cb3d56c

Browse files
Andrew MaoAndrew Mao
Andrew Mao
authored and
Andrew Mao
committed
Building an LLM Chat Application
1 parent 0e0b3c0 commit cb3d56c

4 files changed

Lines changed: 290 additions & 3 deletions

File tree

‎_data/navigation.yml‎

Lines changed: 5 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -1,12 +1,14 @@
11
# main links
22
main:
3-
- title: "Quick-Start Guide"
4-
url: https://mmistakes.github.io/minimal-mistakes/docs/quick-start-guide/
3+
- title: "About"
4+
url: /about/
55
# - title: "About"
6-
# url: https://mmistakes.github.io/minimal-mistakes/about/
6+
# url: /about/
77
# - title: "Sample Posts"
88
# url: /year-archive/
99
# - title: "Sample Collections"
1010
# url: /collection-archive/
11+
# - title: "Terms & Privacy Policy"
12+
# url: /terms/
1113
# - title: "Sitemap"
1214
# url: /sitemap/

‎_posts/2024-04-22-llm-architecture-and-training.md‎

Lines changed: 91 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,91 @@
1+
---
2+
layout: single
3+
title: "Notes on LLMs, and being replaced by them"
4+
date: 2024-04-22
5+
categories: AI
6+
tags: [LLM, Architecture, Training, AI]
7+
# header:
8+
# image: /assets/images/llm-header.jpg
9+
# caption: "Photo credit: [**Unsplash**](https://unsplash.com)"
10+
---
11+
12+
<!-- # Notes on LLMs: Architecture and Training Process -->
13+
14+
Large Language Models (LLMs) are transforming the modern world, in some ways exciting and unsettling. I'm writing a series of posts as an experiment to see how much I am replaceable by AI, and where I still have a unique voice. In this post, I'll jot some notes on the architecture of LLMs and their training process.
15+
16+
Note: this post is mostly AI-generated. I'm working on expanding on the basic concepts below in separate posts.
17+
18+
## Architecture Overview
19+
20+
Modern LLMs are primarily based on the Transformer architecture, which was introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017. The key components include:
21+
22+
### 1. Transformer Architecture
23+
- **Self-Attention Mechanism**: Allows the model to weigh the importance of different words in a sequence
24+
- **Multi-Head Attention**: Enables the model to focus on different parts of the sequence simultaneously
25+
- **Feed-Forward Networks**: Process the attended information
26+
- **Layer Normalization**: Helps stabilize training
27+
- **Residual Connections**: Facilitate gradient flow during training
28+
29+
### 2. Model Components
30+
- **Embedding Layer**: Converts input tokens into dense vectors
31+
- **Positional Encoding**: Provides information about the position of tokens in the sequence
32+
- **Decoder/Encoder Blocks**: Process the input through multiple layers of attention and feed-forward networks
33+
34+
## Training Process
35+
36+
The training of LLMs involves several key stages:
37+
38+
### 1. Pre-training
39+
- **Data Collection**: Gathering large amounts of text data from various sources
40+
- **Tokenization**: Converting text into numerical tokens
41+
- **Masked Language Modeling**: Predicting masked tokens in the input sequence
42+
- **Next Token Prediction**: Learning to predict the next token in a sequence
43+
44+
### 2. Fine-tuning
45+
- **Supervised Fine-tuning**: Training on specific tasks with labeled data
46+
- **Reinforcement Learning**: Optimizing model outputs based on human feedback
47+
- **Instruction Tuning**: Adapting the model to follow specific instructions
48+
49+
### 3. Optimization Techniques
50+
- **Gradient Descent**: Updating model parameters to minimize loss
51+
- **Learning Rate Scheduling**: Adjusting the learning rate during training
52+
- **Mixed Precision Training**: Using lower precision to speed up training
53+
- **Distributed Training**: Training across multiple GPUs/TPUs
54+
55+
## Challenges and Considerations
56+
57+
1. **Computational Resources**
58+
- Large models require significant computational power
59+
- Training can take weeks or months on specialized hardware
60+
61+
2. **Data Quality**
62+
- The quality of training data significantly impacts model performance
63+
- Careful filtering and preprocessing are essential
64+
65+
3. **Ethical Considerations**
66+
- Bias in training data
67+
- Potential for misuse
68+
- Environmental impact of training large models
69+
70+
## Future Directions
71+
72+
1. **Efficiency Improvements**
73+
- Model compression techniques
74+
- More efficient architectures
75+
- Better training algorithms
76+
77+
2. **Multimodal Capabilities**
78+
- Integration with vision and audio
79+
- Cross-modal understanding
80+
81+
3. **Specialized Applications**
82+
- Domain-specific fine-tuning
83+
- Customized solutions for specific industries
84+
85+
## Conclusion
86+
87+
Understanding the architecture and training process of LLMs is crucial for both researchers and practitioners in the field of AI. As these models continue to evolve, they present both exciting opportunities and important challenges that need to be addressed.
88+
89+
---
90+
91+
*This post provides a high-level overview of LLM architecture and training. For more detailed information, please refer to the original research papers and technical documentation.*

‎_posts/2025-04-26-llm-app.md‎

Lines changed: 194 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,194 @@
1+
---
2+
layout: single
3+
title: "Building an LLM Chat Application"
4+
description: A guide to building and deploying an AI chat application with Kubernetes
5+
date: 2024-04-22
6+
categories: AI
7+
tags: [LLM, Architecture, Training, AI]
8+
# header:
9+
# image: /assets/images/llm-header.jpg
10+
# caption: "Photo credit: [**Unsplash**](https://unsplash.com)"
11+
---
12+
13+
We walk through building a modern AI chat application that supports both OpenAI and local LLM models, with Kubernetes deployment and GPU acceleration.
14+
15+
## Table of Contents
16+
17+
1. [Project Overview](#project-overview)
18+
2. [Architecture](#architecture)
19+
3. [Development Setup](#development-setup)
20+
4. [Kubernetes Deployment](#kubernetes-deployment)
21+
5. [CI/CD Pipeline](#cicd-pipeline)
22+
6. [Best Practices](#best-practices)
23+
24+
## Project Overview
25+
26+
Our AI chat application is a full-stack solution that demonstrates modern software development practices:
27+
28+
- **Multiple LLM Support**: Integration with OpenAI's GPT models and local models using vLLM
29+
- **Microservices Architecture**: Separate services for frontend, backend, and inference
30+
- **Container Orchestration**: Kubernetes deployment with GPU support
31+
- **CI/CD Pipeline**: Automated testing and deployment using GitHub Actions
32+
33+
## Architecture
34+
35+
### Components
36+
37+
1. **Frontend (Streamlit)**
38+
- Modern chat interface
39+
- Real-time response streaming
40+
- Model selection and configuration
41+
42+
2. **Backend (FastAPI)**
43+
- API gateway
44+
- Request routing
45+
- Model management
46+
47+
3. **Inference Service (vLLM)**
48+
- GPU-accelerated inference
49+
- Model loading and caching
50+
- Efficient resource utilization
51+
52+
### Infrastructure
53+
54+
```mermaid
55+
graph TD
56+
A[User] --> B[Frontend Service]
57+
B --> C[Backend Service]
58+
C --> D[OpenAI API]
59+
C --> E[Inference Service]
60+
E --> F[GPU Resources]
61+
```
62+
63+
## Development Setup
64+
65+
### Prerequisites
66+
67+
- Python 3.10+
68+
- Docker
69+
- Kubernetes cluster
70+
- NVIDIA GPU with drivers
71+
72+
### Local Development
73+
74+
1. **Clone the Repository**
75+
```bash
76+
git clone https://github.com/yourusername/ai-chat.git
77+
cd ai-chat
78+
```
79+
80+
2. **Set Up Environment**
81+
```bash
82+
python -m venv venv
83+
source venv/bin/activate
84+
pip install -r requirements.txt
85+
```
86+
87+
3. **Run Services**
88+
```bash
89+
# Terminal 1 - Backend
90+
cd backend && uvicorn main:app --reload
91+
92+
# Terminal 2 - Frontend
93+
cd frontend && streamlit run app.py
94+
95+
# Terminal 3 - Inference
96+
cd inference && uvicorn main:app --reload
97+
```
98+
99+
## Kubernetes Deployment
100+
101+
### Cluster Setup
102+
103+
1. **Enable GPU Support**
104+
```bash
105+
# Install NVIDIA device plugin
106+
kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/master/nvidia-device-plugin.yml
107+
```
108+
109+
2. **Create Namespace**
110+
```bash
111+
kubectl create namespace ai-chat
112+
```
113+
114+
3. **Apply Configurations**
115+
```bash
116+
kubectl apply -f k8s/configmap.yaml
117+
kubectl apply -f k8s/pvc.yaml
118+
kubectl apply -f k8s/backend-deployment.yaml
119+
kubectl apply -f k8s/frontend-deployment.yaml
120+
kubectl apply -f k8s/inference-deployment.yaml
121+
```
122+
123+
### Resource Management
124+
125+
- GPU allocation through Kubernetes device plugins
126+
- Persistent volume for model storage
127+
- Resource limits and requests for each service
128+
129+
## CI/CD Pipeline
130+
131+
### GitHub Actions Workflow
132+
133+
1. **Build and Test**
134+
- Run unit tests
135+
- Build Docker images
136+
- Push to container registry
137+
138+
2. **Deploy**
139+
- Update Kubernetes manifests
140+
- Apply configurations
141+
- Verify deployment
142+
143+
### Security Considerations
144+
145+
- Secrets management
146+
- Image scanning
147+
- Access control
148+
149+
## Best Practices
150+
151+
### Development
152+
153+
1. **Code Organization**
154+
- Modular architecture
155+
- Clear separation of concerns
156+
- Comprehensive testing
157+
158+
2. **Performance**
159+
- Efficient resource utilization
160+
- Caching strategies
161+
- Load balancing
162+
163+
3. **Security**
164+
- API key management
165+
- Input validation
166+
- Error handling
167+
168+
### Deployment
169+
170+
1. **Monitoring**
171+
- Health checks
172+
- Resource usage
173+
- Error tracking
174+
175+
2. **Scaling**
176+
- Horizontal pod autoscaling
177+
- Resource optimization
178+
- Load distribution
179+
180+
3. **Maintenance**
181+
- Regular updates
182+
- Backup strategies
183+
- Disaster recovery
184+
185+
## Conclusion
186+
187+
This project demonstrates how to build and deploy a modern AI application using best practices in software development and DevOps. The combination of microservices architecture, container orchestration, and GPU acceleration provides a scalable and efficient solution for AI-powered applications.
188+
189+
## Resources
190+
191+
- [vLLM Documentation](https://github.com/vllm-project/vllm)
192+
- [FastAPI Documentation](https://fastapi.tiangolo.com/)
193+
- [Kubernetes Documentation](https://kubernetes.io/docs/)
194+
- [GitHub Actions Documentation](https://docs.github.com/en/actions)

‎assets/images/bio-photo.jpg‎

299 KB
Loading

0 commit comments

Comments
 (0)