Key Points

References

Key Concepts

Claude

https://claude.ai/chats

Chat 1 

Use the Jim Mason profile document I uploaded to write a great resume for the Job description for the Program Manager document I uploaded

Claude docs

Claude Usage Guide

MCP provides a standardized way to connect AI models to different data sources and tools. 

https://docs.anthropic.com/en/docs/build-with-claude/pdf-support

caq>> Explain Claude 3.7 Sonnet

Claude 3.7 Sonnet and Claude Code Introduction

Claude 3.7 Sonnet and Claude Code Introduction.pdf.     GD

Claude 3.7 Sonnet and Claude Code _ Anthropic.pdf

Cldude-3v7-System-Card-Feb_2025_Model_Card.pdf.    GD

Responsible AI with Claude 3v7

We’ve conducted extensive testing and evaluation of Claude 3.7 Sonnet, working with external experts to ensure it meets our standards for security, safety, and reliability. Claude 3.7 Sonnet also makes more nuanced distinctions between harmful and benign requests, reducing unnecessary refusals by 45% compared to its predecessor.

caq>> please provide a useful Mermaid context diagram with related discussion of your architecture and a glossary of terms.

separately show in a Mermaid flowchart with swimlanes how I use Claude 3.7 with all features in the chat session life cycle from initial model context, uploaded context documents, images or urls, a series of prompt requests and responses, add RAG customization to fine tune a query and generate a well engineered response set that will fit my needs

I'll create two Mermaid diagrams for you: one showing Claude's architecture conceptually, and another showing the user interaction flowchart. I'll also provide explanations and a glossary of relevant terms.

Understanding Claude 3.7 Sonnet's Architecture

The first diagram provides a conceptual overview of Claude 3.7 Sonnet's architecture. While Anthropic hasn't publicly disclosed the exact details, this represents a likely high-level structure based on what we know about modern large language models.

Key Components:

1. Input Processing
   • User inputs (text, documents, images) are processed through tokenization
   • The tokenizer converts raw text into numerical tokens the model can process
2. Context Window
   • Stores all relevant information for the current conversation
   • Includes system instructions, conversation history, uploaded documents, and current query
3. Attention Mechanisms
   • Multi-head attention allows the model to focus on relevant parts of the context
   • Enables understanding relationships between different parts of the input
4. Extended Reasoning Module
   • New in Claude 3.7 Sonnet
   • Provides enhanced reasoning capabilities for complex queries
   • Available to Pro users as "extended thinking mode"
5. Safety Systems
   • Content filtering to prevent harmful outputs
   • Constitutional AI ensures responses align with Anthropic's values
   • RLHF (Reinforcement Learning from Human Feedback) for alignment

User Interaction Flow with Claude

The second diagram illustrates the complete lifecycle of interacting with Claude 3.7 Sonnet, including:

1. Initialization Phase
   • Model selection and loading
   • Context window preparation
2. Context Enhancement
   • Document uploading and processing
   • Image analysis capabilities
   • URL content extraction
3. Prompt-Response Cycle
   • Initial prompt crafting
   • Response generation
   • Refinement through follow-up interactions
4. RAG Integration (Optional)
   • Document indexing
   • Query embedding generation
   • Relevant information retrieval
   • Context augmentation

Glossary of Terms

• Tokenizer: System that converts text into numerical tokens the model can process
• Context Window: The total amount of information the model can consider at once
• Multi-Head Attention: Mechanism allowing the model to focus on different parts of input simultaneously
• Feed-Forward Networks: Neural network components that transform representations
• Extended Reasoning: Enhanced thinking capabilities for complex problem-solving
• RLHF: Reinforcement Learning from Human Feedback - alignment technique using human preferences
• Constitutional AI: Anthropic's approach to aligning AI systems with human values
• RAG: Retrieval-Augmented Generation - enhancing responses by retrieving relevant information
• Embeddings: Numerical representations of text that capture semantic meaning
• Token Predictor: Component that determines the most likely next token in a sequence
• Artifacts: Self-contained content pieces (code, diagrams, etc.) that can be referenced throughout conversations

Would you like me to elaborate on any specific part of these diagrams or explain any particular aspect of Claude's architecture or usage in more detail?

caq>> regenerate the Mermaid diagram "User Interaction Flowchart with Claude 3.7 Sonnet" correctly without errors

Claude 3.7 Sonnet Architecture - Conceptual Overview

graph TB
subgraph "Claude 3.7 Sonnet Architecture"
Input[/"User Input"/] --> Tokenizer
Tokenizer --> Context["Context Window"]
Context --> Attention["Multi-Head Attention Layers"]
Attention --> FeedForward["Feed-Forward Networks"]
FeedForward --> Reasoning["Extended Reasoning Module"]
Reasoning --> NormLayers["Normalization Layers"]
NormLayers --> OutputProj["Output Projection"]
OutputProj --> TokenPredictor["Token Predictor"]
TokenPredictor --> OutputTokens["Output Tokens"]
OutputTokens --> Detokenizer
Detokenizer --> Response[/"AI Response"/]

subgraph "Safety Systems"
SafetyFilters["Content Filtering"] 
ConstitutionalAI["Constitutional AI"]
RLHF["RLHF Alignment"]
end

Input -.-> SafetyFilters
Context -.-> ConstitutionalAI
OutputTokens -.-> SafetyFilters

subgraph "Training Components"
PreTraining["Pre-training"]
SFT["Supervised Fine-Tuning"]
RLHF
end
end

User Interaction Flowchart with Claude 3.7 Sonnet

flowchart TD
%% User Swimlane
subgraph User[User]
Start([Start]) --> SelectModel["Select Claude 3.7 Sonnet"]
SelectModel --> UploadContext["Upload Context (Optional)"]
UploadContext --> InitialPrompt["Craft Initial Prompt"]
InitialPrompt --> ReceiveResponse["Receive Response"]
ReceiveResponse --> Evaluate{"Evaluate Response"}
Evaluate -->|"Satisfactory"| UseResponse["Use Generated Content"]
Evaluate -->|"Needs Refinement"| Refine["Refine Prompt or Context"]
Refine --> ReceiveResponse
UseResponse --> End([End])
end

%% System Swimlane
subgraph System[Claude System]
LoadModel["Load Claude 3.7 Sonnet Model"]
PrepareContext["Initialize Context Window"]
ProcessContext["Process Uploaded Context"]
EnrichContext["Enrich System Context"]
TokenizeInput["Tokenize User Input"]
AppendContext["Append to Context Window"]
EnableFeatures["Enable Applicable Features"]
GenerateResponse["Generate Response"]
FormatOutput["Format Output"]
ProcessRefinement["Process Refinement"]
end

%% RAG Swimlane
subgraph RAG[RAG Customization]
IndexDocuments["Index Documents"]
GenerateEmbeddings["Generate Query Embeddings"]
RetrieveRelevant["Retrieve Relevant Information"]
AugmentPrompt["Augment Prompt with Context"]
end

%% Connect swimlanes with appropriate flows
SelectModel --> LoadModel
LoadModel --> PrepareContext
UploadContext --> ProcessContext
ProcessContext --> EnrichContext
EnrichContext --> PrepareContext
InitialPrompt --> TokenizeInput
TokenizeInput --> AppendContext
AppendContext --> EnableFeatures
EnableFeatures --> GenerateResponse
GenerateResponse --> FormatOutput
FormatOutput --> ReceiveResponse
Refine --> ProcessRefinement
ProcessRefinement --> AppendContext

%% RAG connections
UploadContext --> IndexDocuments
InitialPrompt --> GenerateEmbeddings
GenerateEmbeddings --> RetrieveRelevant
RetrieveRelevant --> AugmentPrompt
AugmentPrompt --> AppendContext

Claude Capabilities 2025 overview video

100K tokens on query prompts

beta feature > The upgraded Claude 3.5 Sonnet model is capable of interacting with tools that can manipulate a computer desktop environment.

file limit now = 5 files w max 10 MB size

Claude can interact with Anthropic enabled desktop tools - ( beta in 2025 )

https://docs.anthropic.com/en/docs/build-with-claude/computer-use

https://docs.anthropic.com/en/docs/build-with-claude/tool-use

Integrate external tools with Claude in these steps:

Note: Steps 3 and 4 are optional. For some workflows, Claude’s tool use request (step 2) might be all you need, without sending results back to Claude.

Claude analyzes images now

https://docs.anthropic.com/en/docs/build-with-claude/vision

Claude Sheets extensions integrates queries into Sheets

https://docs.anthropic.com/en/docs/build-with-claude/claude-for-sheets

Claude for Sheets enables prompt engineering at scale by enabling you to test prompts across evaluation suites in parallel. Additionally, it excels at office tasks like survey analysis and online data processing.

Visit our prompt engineering example sheet to see this in action.

Get your Anthropic API key

Example adding Claude content to a cell

Parameter arguments come after the initial prompt, like =CLAUDE(prompt, model, params...).

Now type in any cell =CLAUDE("Hi, Claude!", "claude-3-haiku-20240307", "max_tokens", 3)

Any API parameter can be set this way. You can even pass in an API key to be used just for this specific cell, like this: "api_key", "sk-ant-api03-j1W..."

Claude versions

Claude 3.7 Sonnet  Jan 25

Claude 3.5 Sonnet, First of Three 3.5 Releases - Trained in Aug 2024.

Claude AI Architecture - Model Context Protocol for AI Training Data for Chatbots

Anthropic is proposing a new standard for connecting AI assistants to the systems where data resides.

Called the Model Context Protocol, or MCP for short, Anthropic says the standard, which it open sourced today, could help AI models produce better, more relevant responses to queries.

MCP lets models — any models, not just Anthropic’s — draw data from sources like business tools and software to complete tasks, as well as from content repositories and app development environments.

“As AI assistants gain mainstream adoption, the industry has invested heavily in model capabilities, achieving rapid advances in reasoning and quality,” Anthropic wrote in a blog post. “Yet even the most sophisticated models are constrained by their isolation from data — trapped behind information silos and legacy systems. Every new data source requires its own custom implementation, making truly connected systems difficult to scale.”

i>>> MCP live training model needs real-time quality management, validation and governance 

MCP ostensibly solves this problem through a protocol that enables developers to build two-way connections between data sources and AI-powered applications (e.g., chatbots). Developers can expose data through “MCP servers” and build “MCP clients” — for instance, apps and workflows — that connect to those servers on command.

Model Training vs Model Tuning aReport 

Claude Prompting and Styles

Anthropic just dropped Styles, a new feature that lets you save and reuse your preferred writing styles in Claude.

Claude Work Items

Claude project folders
https://claude.ai/projects

Integrating Claude AI into Projects
https://claude.ai/chat/feeefbc9-c025-4fee-b3eb-2cb10ace5a44

Build with Claude
https://docs.anthropic.com/en/home

Solution Engineering Decision Workbook Template
https://claude.ai/chat/5cdd8ef7-99ba-4e90-96bc-20a29f2404af

TTutorial Comprehensive Self-Sovereign Identity Implementation Guide
https://claude.ai/chat/ab8196c5-211e-4e76-8a58-7f553980c357

Top 5 Open Source Decentralized Data Streaming Servers
https://claude.ai/chat/916571bf-cb53-440f-b1bf-9fcb2376a436

Claude Training and Governance

Potential Value Opportunities

Open LLMs - Good list > https://github.com/eugeneyan/open-llms

8 Top Open-Source LLMs for 2024 and Their Uses

Tuning Foundation LLM and other AI models

Snowflake AI ML Academy Training

Potential Challenges

Candidate Solutions

https://www.unite.ai/best-open-source-llms/

LLama 2

https://ai.meta.com/llama/

Meta’s Llama 2 is a groundbreaking addition to their AI model lineup. This isn't just another model; it's designed to fuel a range of state-of-the-art applications. Llama 2's training data is vast and varied, making it a significant advancement over its predecessor. This diversity in training ensures that Llama 2 is not just an incremental improvement but a monumental step towards the future of AI-driven interactions.

The collaboration between Meta and Microsoft has expanded the horizons for Llama 2. The open-source model is now supported on platforms like Azure and Windows, aiming to provide developers and organizations with the tools to create generative AI-driven experiences. This partnership underscores both companies' dedication to making AI more accessible and open to all.

Top Features of LLaMa 2:

• Diverse Training Data: Llama 2's training data is both extensive and varied, ensuring a comprehensive understanding and performance.
• Collaboration with Microsoft: Llama 2 is supported on platforms like Azure and Windows, broadening its application scope.
• Open Availability: Unlike its predecessor, Llama 2 is available for a wider audience, ready for fine-tuning on multiple platforms.
• Safety-Centric Design: Meta has emphasized safety, ensuring that Llama 2 produces accurate and reliable results while minimizing harmful outputs.
• Optimized Versions: Llama 2 comes in two main versions – Llama 2 and Llama 2-Chat, with the latter being specially designed for two-way conversations. These versions range in complexity from 7 billion to 70 billion parameters.
• Enhanced Training: Llama 2 was trained on two million tokens, a significant increase from the original Llama's 1.4 trillion tokens.

BLOOM

BLOOM's debut was a significant step in making generative AI technology more accessible. As an open-source LLM, it boasts 176 billion parameters, making it one of the most formidable in its class. BLOOM has the proficiency to generate coherent and precise text across 46 languages and 13 programming languages.

The project emphasizes transparency, allowing public access to its source code and training data. This openness invites ongoing examination, utilization, and enhancement of the model.

Accessible at no cost through the Hugging Face platform, BLOOM stands as a testament to collaborative innovation in AI.

Top Features of Bloom:

• Multilingual Capabilities: BLOOM is proficient in generating text in 46 languages and 13 programming languages, showcasing its wide linguistic range.
• Open-Source Access: The model's source code and training data are publicly available, promoting transparency and collaborative improvement.
• Autoregressive Text Generation: Designed to continue text from a given prompt, BLOOM excels in extending and completing text sequences.
• Massive Parameter Count: With 176 billion parameters, BLOOM stands as one of the most powerful open-source LLMs in existence.
• Global Collaboration: Developed through a year-long project with contributions from volunteers across more than 70 countries and Hugging Face researchers.
• Free Accessibility: Users can access and utilize BLOOM for free through the Hugging Face ecosystem, enhancing its democratization in the field of AI.
• Industrial-Scale Training: The model was trained on vast amounts of text data using significant computational resources, ensuring robust performance.

MPT-7B

MosaicML Pretrained Transformer, is a GPT-style, decoder-only transformer model. This model boasts several enhancements, including performance-optimized layer implementations and architectural changes that ensure greater training stability.

A standout feature of MPT-7B is its training on an extensive dataset comprising 1 trillion tokens of text and code. This rigorous training was executed on the MosaicML platform over a span of 9.5 days.

The open-source nature of MPT-7B positions it as a valuable tool for commercial applications. It holds the potential to significantly impact predictive analytics and the decision-making processes of businesses and organizations.

In the rapidly evolving world of artificial intelligence (AI), Large Language Models (LLMs) have emerged as a cornerstone, driving innovations and reshaping the way we interact with technology.

As these models become increasingly sophisticated, there's a growing emphasis on democratizing access to them. Open-source models, in particular, are playing a pivotal role in this democratization, offering researchers, developers, and enthusiasts alike the opportunity to delve deep into their intricacies, fine-tune them for specific tasks, or even build upon their foundations.

In this blog, we'll explore some of the top open-source LLMs that are making waves in the AI community, each bringing its unique strengths and capabilities to the table.

1. Llama 2

Meta’s Llama 2 is a groundbreaking addition to their AI model lineup. This isn't just another model; it's designed to fuel a range of state-of-the-art applications. Llama 2's training data is vast and varied, making it a significant advancement over its predecessor. This diversity in training ensures that Llama 2 is not just an incremental improvement but a monumental step towards the future of AI-driven interactions.

The collaboration between Meta and Microsoft has expanded the horizons for Llama 2. The open-source model is now supported on platforms like Azure and Windows, aiming to provide developers and organizations with the tools to create generative AI-driven experiences. This partnership underscores both companies' dedication to making AI more accessible and open to all.

Llama 2 is not just a successor to the original Llama model; it represents a paradigm shift in the chatbot arena. While the first Llama model was revolutionary in generating text and code, its availability was limited to prevent misuse. Llama 2, on the other hand, is set to reach a wider audience. It's optimized for platforms like AWS, Azure, and Hugging Face's AI model hosting platform. Moreover, with Meta's collaboration with Microsoft, Llama 2 is poised to make its mark not only on Windows but also on devices powered by Qualcomm's Snapdragon system-on-chip.

Safety is at the heart of Llama 2's design. Recognizing the challenges faced by earlier large language models like GPT, which sometimes produced misleading or harmful content, Meta has taken extensive measures to ensure Llama 2's reliability. The model has undergone rigorous training to minimize ‘hallucinations', misinformation, and biases.

Top Features of LLaMa 2:

• Diverse Training Data: Llama 2's training data is both extensive and varied, ensuring a comprehensive understanding and performance.
• Collaboration with Microsoft: Llama 2 is supported on platforms like Azure and Windows, broadening its application scope.
• Open Availability: Unlike its predecessor, Llama 2 is available for a wider audience, ready for fine-tuning on multiple platforms.
• Safety-Centric Design: Meta has emphasized safety, ensuring that Llama 2 produces accurate and reliable results while minimizing harmful outputs.
• Optimized Versions: Llama 2 comes in two main versions – Llama 2 and Llama 2-Chat, with the latter being specially designed for two-way conversations. These versions range in complexity from 7 billion to 70 billion parameters.
• Enhanced Training: Llama 2 was trained on two million tokens, a significant increase from the original Llama's 1.4 trillion tokens.

2. Bloom

In 2022, after a global collaborative effort involving volunteers from over 70 countries and experts from Hugging Face, the BLOOM project was unveiled. This large language model (LLM), created through a year-long initiative, is designed for autoregressive text generation, capable of extending a given text prompt. It was trained on a massive corpus of text data utilizing substantial computational power.

BLOOM's debut was a significant step in making generative AI technology more accessible. As an open-source LLM, it boasts 176 billion parameters, making it one of the most formidable in its class. BLOOM has the proficiency to generate coherent and precise text across 46 languages and 13 programming languages.

The project emphasizes transparency, allowing public access to its source code and training data. This openness invites ongoing examination, utilization, and enhancement of the model.

Accessible at no cost through the Hugging Face platform, BLOOM stands as a testament to collaborative innovation in AI.

Top Features of Bloom:

• Multilingual Capabilities: BLOOM is proficient in generating text in 46 languages and 13 programming languages, showcasing its wide linguistic range.
• Open-Source Access: The model's source code and training data are publicly available, promoting transparency and collaborative improvement.
• Autoregressive Text Generation: Designed to continue text from a given prompt, BLOOM excels in extending and completing text sequences.
• Massive Parameter Count: With 176 billion parameters, BLOOM stands as one of the most powerful open-source LLMs in existence.
• Global Collaboration: Developed through a year-long project with contributions from volunteers across more than 70 countries and Hugging Face researchers.
• Free Accessibility: Users can access and utilize BLOOM for free through the Hugging Face ecosystem, enhancing its democratization in the field of AI.
• Industrial-Scale Training: The model was trained on vast amounts of text data using significant computational resources, ensuring robust performance.

3. MPT-7B

MosaicML Foundations has made a significant contribution to this space with the introduction of MPT-7B, their latest open-source LLM. MPT-7B, an acronym for MosaicML Pretrained Transformer, is a GPT-style, decoder-only transformer model. This model boasts several enhancements, including performance-optimized layer implementations and architectural changes that ensure greater training stability.

A standout feature of MPT-7B is its training on an extensive dataset comprising 1 trillion tokens of text and code. This rigorous training was executed on the MosaicML platform over a span of 9.5 days.

The open-source nature of MPT-7B positions it as a valuable tool for commercial applications. It holds the potential to significantly impact predictive analytics and the decision-making processes of businesses and organizations.

In addition to the base model, MosaicML Foundations is also releasing specialized models tailored for specific tasks, such as MPT-7B-Instruct for short-form instruction following, MPT-7B-Chat for dialogue generation, and MPT-7B-StoryWriter-65k+ for long-form story creation.

The development journey of MPT-7B was comprehensive, with the MosaicML team managing all stages from data preparation to deployment within a few weeks. The data was sourced from diverse repositories, and the team utilized tools like EleutherAI’s GPT-NeoX and the 20B tokenizer to ensure a varied and comprehensive training mix.

Key Features Overview of MPT-7B:

• Commercial Licensing: MPT-7B is licensed for commercial use, making it a valuable asset for businesses.
• Extensive Training Data: The model boasts training on a vast dataset of 1 trillion tokens.
• Long Input Handling: MPT-7B is designed to process extremely lengthy inputs without compromise.
• Speed and Efficiency: The model is optimized for swift training and inference, ensuring timely results.
• Open-Source Code: MPT-7B comes with efficient open-source training code, promoting transparency and ease of use.
• Comparative Excellence: MPT-7B has demonstrated superiority over other open-source models in the 7B-20B range, with its quality matching that of LLaMA-7B.

Falcon-40B

Falcon-40B, is a foundational LLM equipped with 40 billion parameters and has been trained on an impressive one trillion tokens. It operates as an autoregressive decoder-only model, which essentially means it predicts the subsequent token in a sequence based on the preceding tokens. This architecture is reminiscent of the GPT model. Notably, Falcon's architecture has demonstrated superior performance to GPT-3, achieving this feat with only 75% of the training compute budget and requiring significantly less compute during inference.

Key Features Overview of Falcon LLM:

• Extensive Parameters: Falcon-40B is equipped with 40 billion parameters, ensuring comprehensive learning and performance.
• Autoregressive Decoder-Only Model: This architecture allows Falcon to predict subsequent tokens based on preceding ones, similar to the GPT model.
• Superior Performance: Falcon outperforms GPT-3 while utilizing only 75% of the training compute budget.
• High-Quality Data Pipeline: TII's data pipeline ensures the extraction of high-quality content from the web, crucial for the model's training.
• Variety of Models: In addition to Falcon-40B, TII offers Falcon-7B and specialized models like Falcon-40B-Instruct and Falcon-7B-Instruct.
• Open-Source Availability: Falcon LLM has been open-sourced, promoting accessibility and inclusivity in the AI domain.

Vicuna-13B

Impressively, Vicuna-13B outperforms other notable models such as LLaMA and Stanford Alpaca in over 90% of cases. The entire training process for Vicuna-13B was executed at a cost of approximately $300. For those interested in exploring its capabilities, the code, weights, and an online demo have been made publicly available for non-commercial purposes.

The Vicuna-13B model has been fine-tuned with 70K user-shared ChatGPT conversations, enabling it to generate more detailed and well-structured responses. The quality of these responses is comparable to ChatGPT.

Initial findings suggest that GPT-4 can produce consistent ranks and detailed assessments when comparing chatbot responses. Preliminary evaluations based on GPT-4 show that Vicuna achieves 90% capability of models like Bard/ChatGPT.

Key Features Overview of Vicuna-13B:

• Open-Source Nature: Vicuna-13B is available for public access, promoting transparency and community involvement.
• Extensive Training Data: The model has been trained on 70K user-shared conversations, ensuring a comprehensive understanding of diverse interactions.
• Competitive Performance: Vicuna-13B's performance is on par with industry leaders like ChatGPT and Google Bard.
• Cost-Effective Training: The entire training process for Vicuna-13B was executed at a low cost of around $300.
• Fine-Tuning on LLaMA: The model has been fine-tuned on LLaMA, ensuring enhanced performance and response quality.
• Online Demo Availability: An interactive online demo is available for users to test and experience the capabilities of Vicuna-13B.

Step-by-step guide for Example

sample code block

Recommended Next Steps

Related articles