filmov
tv
DSPy explained: No more LangChain PROMPT Templates
Показать описание
DSPy explained and coded in simple terms. No more LangChain or LangGraph prompt templates. A self-improving LLM-RM pipeline! Plus automatic prompt engineering via self-optimization by a GRAPH based pipeline representation via DSPy.
Chapter 1: Development of an Intelligent Pipeline for Large Language Models
Focus: Integration and Optimization of Language Models and Data Retrieval Systems.
Pipeline Architecture: The chapter begins with the conceptualization of an intelligent pipeline that integrates a large language model (LLM), a retriever model, and various data models. The pipeline is designed for self-configuration, learning, and optimization.
Graph-Based Representation: Emphasis is placed on using graph theory and mathematical tools for optimizing the pipeline structure. The graph-based approach allows for more efficient data processing and effective communication between different components.
Problem Identification: Challenges in integrating synthetic reasoning and actions within LLMs are addressed. The chapter discusses the need for optimizing prompt structures for diverse applications, highlighting the complexity of creating flexible and efficient models.
Chapter 2: Evaluating and Optimizing Model Performance
Focus: Comparative Analysis of Model Configurations and Optimization Techniques.
Experimental Analysis: This chapter details experiments conducted by Stanford University and other institutions, analyzing various prompt structures and their impact on model performance. It includes an in-depth examination of different models, including LangChain, and their effectiveness in specific contexts.
Optimization Strategies: The text explores strategies for optimizing the intelligent pipeline, including supervised fine-tuning algorithms from Hugging Face and in-context learning for few-shot examples.
Microsoft's Study: A critical review of a study conducted by Microsoft in January 2024 is presented, focusing on the comparison between retrieval augmented generation (RAG) and fine-tuning methods. This section scrutinizes the balance between incorporating external data into LLMs through RAG versus embedding the knowledge directly into the model via fine-tuning.
Chapter 3: Advanced Pipeline Configuration and Self-Optimization
Focus: Advanced Techniques in Pipeline Self-Optimization and Configuration.
Self-Optimizing Framework: This chapter delves into the creation of a self-improving pipeline, which includes automatic prompt generation and optimization. The pipeline is described as being capable of autonomously generating training datasets and deciding the optimal approach (fine-tuning vs. in-context learning) based on specific tasks.
DSPy Integration: Discussion of DSPy, a platform for coding declarative language model calls into self-improving pipelines, with a focus on its utilization in PyTorch.
Comprehensive Optimization: The chapter concludes with an exploration of techniques for structural optimization of the pipeline and internal model optimization. It highlights collaborative efforts from Stanford University, UC Berkeley, Microsoft, Carnegie Mellon University, and Amazon in advancing these technologies.
all rights with authors:
DSPY: COMPILING DECLARATIVE LANGUAGE MODEL CALLS INTO SELF-IMPROVING PIPELINES
by Stanford, UC Berkeley, et al
DSPy Notebooks:
Chapter 1: Development of an Intelligent Pipeline for Large Language Models
Focus: Integration and Optimization of Language Models and Data Retrieval Systems.
Pipeline Architecture: The chapter begins with the conceptualization of an intelligent pipeline that integrates a large language model (LLM), a retriever model, and various data models. The pipeline is designed for self-configuration, learning, and optimization.
Graph-Based Representation: Emphasis is placed on using graph theory and mathematical tools for optimizing the pipeline structure. The graph-based approach allows for more efficient data processing and effective communication between different components.
Problem Identification: Challenges in integrating synthetic reasoning and actions within LLMs are addressed. The chapter discusses the need for optimizing prompt structures for diverse applications, highlighting the complexity of creating flexible and efficient models.
Chapter 2: Evaluating and Optimizing Model Performance
Focus: Comparative Analysis of Model Configurations and Optimization Techniques.
Experimental Analysis: This chapter details experiments conducted by Stanford University and other institutions, analyzing various prompt structures and their impact on model performance. It includes an in-depth examination of different models, including LangChain, and their effectiveness in specific contexts.
Optimization Strategies: The text explores strategies for optimizing the intelligent pipeline, including supervised fine-tuning algorithms from Hugging Face and in-context learning for few-shot examples.
Microsoft's Study: A critical review of a study conducted by Microsoft in January 2024 is presented, focusing on the comparison between retrieval augmented generation (RAG) and fine-tuning methods. This section scrutinizes the balance between incorporating external data into LLMs through RAG versus embedding the knowledge directly into the model via fine-tuning.
Chapter 3: Advanced Pipeline Configuration and Self-Optimization
Focus: Advanced Techniques in Pipeline Self-Optimization and Configuration.
Self-Optimizing Framework: This chapter delves into the creation of a self-improving pipeline, which includes automatic prompt generation and optimization. The pipeline is described as being capable of autonomously generating training datasets and deciding the optimal approach (fine-tuning vs. in-context learning) based on specific tasks.
DSPy Integration: Discussion of DSPy, a platform for coding declarative language model calls into self-improving pipelines, with a focus on its utilization in PyTorch.
Comprehensive Optimization: The chapter concludes with an exploration of techniques for structural optimization of the pipeline and internal model optimization. It highlights collaborative efforts from Stanford University, UC Berkeley, Microsoft, Carnegie Mellon University, and Amazon in advancing these technologies.
all rights with authors:
DSPY: COMPILING DECLARATIVE LANGUAGE MODEL CALLS INTO SELF-IMPROVING PIPELINES
by Stanford, UC Berkeley, et al
DSPy Notebooks:
0:53:22
DSPy explained: No more LangChain PROMPT Templates
0:54:16
DSPy Explained!
0:34:34
Getting started with DSPy tutorial
0:10:21
A brief overview of Stanford's DSPy - explained simply
0:57:55
Optimization of LLM Systems with DSPy and LangChain/LangSmith
1:04:52
DSPy Explained
0:19:35
DsPy crash course - optimize your LLM pipelines with DsPy (Part 1)
0:13:14
Getting started with DSPy: A beginners guide to RAG
0:03:57
LangChain vs DSPy : Key differences between Generative AI packages
0:42:17
Prompt Engineering is Dead; Build LLM Applications with DSPy Framework
0:06:36
What is Retrieval-Augmented Generation (RAG)?
0:25:38
SBTB23: Omar Khattab, DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
0:03:22
Vector databases are so hot right now. WTF are they?
1:01:23
DSPy: Advanced Prompt Engineering?
0:18:55
DSPy: MOST Advanced AI RAG Framework with Auto Reasoning and Prompting
0:06:19
What Is LangChain? - Explained Simply
0:08:32
DSPy tutorial for beginners: Generative AI without prompt engineering
0:16:30
DSPy — Does It Live Up To The Hype?
0:13:46
DSPy + Weaviate for the Next Generation of LLM Apps
0:12:02
Intro to DSPy: Goodbye Prompting, Hello Programming!
0:02:54
DSPy - Programming with LLMs
0:00:34
Here's what happened after Chinese President Xi Jinping's aide arrived late for the BRICS ...
0:31:54
Getting Started with RAG in DSPy!
0:00:16
Testing Stable Diffusion inpainting on video footage #shorts
Комментарии