Using Various LLM Models
Last updated
Last updated
Author: eunhhyy
Design:
Peer Review : Wooseok Jeong
This is a part of LangChain Open Tutorial
This tutorial provides a comprehensive guide to major Large Language Models (LLMs) in the AI Market.
GPT models by OpenAI are advanced transformer-based language models designed for tasks like text generation, summarization, translation, and Q&A. Offered primarily as a cloud-based API, they let developers use the models without hosting them. While not open-source, GPT provides pre-trained models with fine-tuning capabilities.
GPT-4o Series (Flagship Models)
GPT-4o: High-reliability model with improved speed over Turbo
GPT-4-turbo: Latest model with vision, JSON, and function calling capabilities
GPT-4o-mini: Entry-level model surpassing GPT-3.5 Turbo performance
O1 Series (Reasoning Specialists)
O1: Advanced reasoning model for complex problem-solving
O1-mini: Fast, cost-effective model for specialized tasks
GPT-4o Multimedia Series (Beta)
GPT-4o-realtime: Real-time audio and text processing model
GPT-4o-audio-preview: Specialized audio input/output model
Core Features
Most advanced GPT-4 model with enhanced reliability
Faster processing compared to GPT-4-turbo variant
Extensive 128,000-token context window
16,384-token maximum output capacity
Performance
Superior reliability and consistency in responses
Enhanced reasoning capabilities across diverse tasks
Optimized speed for real-time applications
Balanced efficiency for resource utilization
Use Cases
Complex analysis and problem-solving
Long-form content generation
Detailed technical documentation
Advanced code generation and review
Technical Specifications
Latest GPT architecture optimizations
Improved response accuracy
Built-in safety measures
Enhanced context retention
For more detailed information, please refer to OpenAI's official documentation.
Meta's Llama AI series offers open-source models that allow fine-tuning, distillation, and flexible deployment.
Llama 3.1 (Multilingual)
8B: Light-weight, ultra-fast model for mobile and edge devices
405B: Flagship foundation model for diverse use cases
Llama 3.2 (Lightweight and Multimodal)
1B and 3B: Efficient models for on-device processing
11B and 90B: Multimodal models with high-resolution image reasoning
Llama 3.3 (Multilingual)
70B: Multilingual support with enhanced performance
Safety Features
Incorporates alignment techniques for safe responses
Performance
Comparable to larger models with fewer resources
Efficiency
Optimized for common GPUs, reducing hardware needs
Language Support
Supports eight languages, including English and Spanish
Training
Pre-trained on 15 trillion tokens
Fine-tuned through Supervised Fine-tuning (SFT) and RLHF
Supervised Fine-tuning : Supervised fine-tuning is a process of improving an existing AI model's performance by training it with labeled data. For example, if you want to teach the model text summarization, you provide pairs of 'original text' and 'summarized text' as training data. Through this training with correct answer pairs, the model can enhance its performance on specific tasks.
Reinforcement Learning with Human Feedback (RLHF) : RLHF is a method where AI models learn to generate better responses through human feedback. When the AI generates responses, humans evaluate them, and the model improves based on these evaluations. Just like a student improves their skills through teacher feedback, AI develops to provide more ethical and helpful responses through human feedback.
Use Cases
For more detailed information, please refer to Meta's official documentation.
Claude models by Anthropic are advanced language models with cloud-based APIs for diverse NLP tasks. These models balance performance, safety, and real-time responsiveness.
Claude 3 Series (Flagship Models)
Claude 3 Haiku: Near-instant responsiveness
Claude 3 Sonnet: Balanced intelligence and speed
Claude 3 Opus: Strong performance for complex tasks
Claude 3.5 Series (Enhanced Models)
Claude 3.5 Haiku: Enhanced real-time responses
Claude 3.5 Sonnet: Advanced research and analysis capabilities
Core Features
Handles highly complex tasks such as math and coding
Extensive context window for detailed document processing
Performance
Superior reliability and consistency
Optimized for real-time applications
Use Cases
Long-form content generation
Detailed technical documentation
Advanced code generation and review
For more detailed information, please refer to Anthropic's official documentation.
Google's Gemini models prioritize efficiency and scalability, designed for a wide range of advanced applications.
Gemini 1.5 Flash: Offers a 1 million-token context window
Gemini 1.5 Pro: Offers a 2 million-token context window
Gemini 2.0 Flash (Experimental): Next-generation model with enhanced speed and performance
Core Features
Supports multimodal live APIs for real-time vision and audio streaming applications
Enhanced spatial understanding and native image generation capabilities
Integrated tool usage and improved agent functionalities
Performance
Provides faster speeds and improved performance compared to previous models
Use Cases
Real-time streaming applications
Reasoning tasks for complex problem-solving
Image and text generation
For more detailed information, refer to Google's Gemini documentation.
Mistral AI provides commercial and open-source models for diverse NLP tasks, including specialized solutions.
Commercial Models
Mistral Large 24.11: Multilingual with a 128k context window
Codestral: Coding specialist with 80+ language support
Ministral Series: Lightweight models for low-latency applications
Open Source Models
Mathstral: Mathematics-focused
Codestral Mamba: 256k context for coding tasks
For more detailed information, please refer to Mistral's official documentation.
Alibaba’s Qwen models offer open-source and commercial variants optimized for diverse industries and tasks.
Qwen 2.5: Advanced multilingual model
Qwen-VL: Multimodal text and image capabilities
Qwen-Audio: Specialized in audio transcription and analysis
Qwen-Coder: Optimized for coding tasks
Qwen-Math: Designed for advanced math problem-solving
Leading performance on various benchmarks
Easy deployment with Alibaba Cloud’s platform
Applications in generative AI, such as writing, image generation, and audio analysis
For more detailed information, visit Alibaba Cloud’s official Qwen page.
