Models on AI Knowledge Base

AI Capabilities and Limitations

Mon, 01 Jan 0001 00:00:00 +0000

A concise introduction to what AI can and cannot do — the companion course to the AI Fluency Framework. Explains the machine properties (how AI actually works) that the 4D Framework competencies respond to. Essential for product managers and business leaders who need to understand AI capabilities at a conceptual level before making product decisions. Pairs with the AI Landscape for Product Leaders learning path.

AI for Everyone

Mon, 01 Jan 0001 00:00:00 +0000

Andrew Ng’s definitive non-technical introduction to AI — the single best starting point for anyone who wants to understand what AI can and cannot do without writing a line of code. Covers AI terminology, what machine learning is, what data it needs, and how to spot opportunities for AI in your organization. Over 4 million enrollments make this the most popular AI course ever created. Take this before any other course if you’re new to AI.

Fundamentals of Deep Learning

Mon, 01 Jan 0001 00:00:00 +0000

NVIDIA’s flagship deep learning workshop — 8 hours of hands-on training with GPU cloud servers included. Covers neural network fundamentals, CNNs, data augmentation, and deployment. The unique selling point is access to NVIDIA’s cloud GPU infrastructure during the course, so you train real models without setting up your own hardware. The most hardware-focused deep learning course available, taught from the perspective of the company that makes the chips.

Machine Learning Specialization

Mon, 01 Jan 0001 00:00:00 +0000

Andrew Ng’s updated Machine Learning course with Stanford — the modern successor to the original Coursera ML course that launched the online AI education movement. Three courses covering supervised learning, advanced algorithms, and unsupervised learning using Python and TensorFlow. The most rigorous free ML foundation available, with the Stanford brand adding credential weight. If you want one certification that signals ML competence, this is it.

Prompt Engineering Across Providers

Mon, 01 Jan 0001 00:00:00 +0000

Master prompt engineering by studying best practices from Anthropic, OpenAI, and Mistral. Then see how DSPy challenges the entire paradigm by replacing prompts with programs.

Comparing approaches across providers gives you deeper intuition than studying any single provider’s guide.

What Product Managers Need to Know About LLMs

Mon, 01 Jan 0001 00:00:00 +0000

A comprehensive non-technical guide to LLM capabilities, limitations, and practical applications for product managers building AI-powered features.

AWS Certified AI Practitioner

Mon, 01 Jan 0001 00:00:00 +0000

AWS’s foundational AI certification — validates understanding of AI/ML concepts, generative AI, and responsible AI within the AWS ecosystem. The training materials are free; only the exam costs $150. This is the most accessible cloud AI certification available and carries weight in enterprises that use AWS. Good for PMs and technical leaders who want a formal credential demonstrating AI literacy.

Claude with Amazon Bedrock

Mon, 01 Jan 0001 00:00:00 +0000

Learn to deploy Claude through AWS Bedrock — covers provisioned throughput, fine-tuning, and enterprise deployment patterns. Essential for teams evaluating how to access Claude in an enterprise AWS environment. This is the deployment-focused complement to the API-focused courses: the API course teaches you how to call Claude, this course teaches you how to run Claude in your cloud infrastructure.

Fine-Tuning Large Language Models

Mon, 01 Jan 0001 00:00:00 +0000

One-hour introduction to when and how to fine-tune LLMs — covers the decision framework (prompt engineering vs. fine-tuning vs. RAG), data preparation, training process, and evaluation. The fastest way to understand whether fine-tuning is right for your use case before committing engineering resources. Pairs with the Fine-Tuning Across Providers learning path for provider-specific implementation details.

How 100 Enterprise CIOs Are Building and Buying Gen AI in 2025

Mon, 01 Jan 0001 00:00:00 +0000

Updated enterprise AI survey showing 81% of enterprises now use 3+ model families, with data on procurement patterns, multi-model optimization, and Anthropic’s growing enterprise penetration.

Introduction to AI

Mon, 01 Jan 0001 00:00:00 +0000

Created by the University of Helsinki, this is the most accessible and comprehensive non-technical AI course available. With over 2 million signups from 170+ countries and a 4.8/5 rating, it covers AI concepts, machine learning, neural networks, and societal implications through interactive exercises. The 30-hour estimate is generous — most complete it in 15-20 hours. Unlike Andrew Ng’s course which is video-based, this is primarily text-based with embedded exercises, making it excellent for self-directed learners who prefer reading to watching.

OpenAI API Essentials

Mon, 01 Jan 0001 00:00:00 +0000

Learn the OpenAI API from first request to advanced features. Covers Chat Completions, function calling, structured outputs, streaming, vision, reasoning models, and embeddings.

Claude API Essentials

Mon, 01 Jan 0001 00:00:00 +0000

Learn the Anthropic API from first principles. Covers the Messages API, tool use, streaming, structured outputs, extended thinking, and cost optimization.

Claude with Google Cloud's Vertex AI

Mon, 01 Jan 0001 00:00:00 +0000

Learn to deploy Claude through Google Cloud’s Vertex AI — covers model garden access, API integration, and enterprise deployment patterns. The Google Cloud counterpart to the Bedrock course. If your organization uses GCP, this is the path to accessing Claude within your existing cloud infrastructure. Useful for the AI Vendor & Platform Evaluation learning path’s deployment topology assessment.

Generative AI Glossary for Business Leaders

Mon, 01 Jan 0001 00:00:00 +0000

Plain-language glossary of essential generative AI terms designed for everyone in a company regardless of technical background.

Generative AI's Act Two

Mon, 01 Jan 0001 00:00:00 +0000

Analysis of generative AI’s transition from technology-driven novelty to customer-focused value creation, with updated market maps and vendor landscape organized by use case.

Google Cloud ML Engineer Learning Path

Mon, 01 Jan 0001 00:00:00 +0000

Google Cloud’s professional ML certification path — the most technically rigorous cloud AI certification available. Covers ML model development, MLOps, deployment, and monitoring on Google Cloud. Includes hands-on labs with Vertex AI, BigQuery ML, and TensorFlow. The learning path is free; certification exam is $200. Requires significant ML experience — this is not a beginner credential.

Introduction to Generative AI

Mon, 01 Jan 0001 00:00:00 +0000

A 30-minute micro-course that explains what generative AI is, how it works, and how it differs from traditional machine learning. Google’s concise format makes this the fastest on-ramp to understanding LLMs — take this if you need a quick conceptual foundation before diving into provider-specific content. Covers transformer architecture at a conceptual level without code.

Generative AI Explained

Mon, 01 Jan 0001 00:00:00 +0000

NVIDIA’s no-code introduction to generative AI, designed for anyone regardless of technical background. Covers what generative AI is, how large language models work at a conceptual level, and the applications transforming industries. Shorter and more focused than Elements of AI or AI for Everyone — take this for NVIDIA’s GPU-centric perspective on why generative AI requires different infrastructure than traditional software.

Reinforcement Learning Course

Mon, 01 Jan 0001 00:00:00 +0000

The most accessible free reinforcement learning course available — covers Q-learning, deep Q-networks, policy gradient methods, PPO, and RLHF (reinforcement learning from human feedback). RLHF is the technique that makes ChatGPT and Claude behave helpfully — understanding it gives you insight into how modern LLMs are trained and aligned. Unique coverage that no other free platform provides at this depth.

Deep Learning Specialization

Mon, 01 Jan 0001 00:00:00 +0000

Andrew Ng’s legendary 5-course deep learning specialization — the gold standard for understanding neural networks from the ground up. Covers neural network foundations, hyperparameter tuning, CNNs, sequence models, and attention mechanisms. This is the theoretical foundation that makes everything else in this knowledge base make sense. Not a short commitment (60+ hours), but if you want to truly understand how LLMs work under the hood, this is the investment.

Generative AI Foundations

Mon, 01 Jan 0001 00:00:00 +0000

AWS’s free introduction to generative AI — covers foundation models, training approaches, and how generative AI fits into the AWS ecosystem. Provides the AWS perspective on AI infrastructure, which complements the Anthropic and Google viewpoints in this knowledge base. Useful for teams evaluating Bedrock as their AI deployment platform.

Practical Deep Learning for Coders

Mon, 01 Jan 0001 00:00:00 +0000

Jeremy Howard’s legendary top-down deep learning course — you build working models in lesson 1 and learn theory as needed. This course has produced alumni at Google Brain, OpenAI, Adobe, and Tesla. The teaching philosophy is radical: start with practical results, then go deeper. Covers computer vision, NLP, tabular data, and deployment using PyTorch, fastai, and Hugging Face Transformers. If you learn by doing rather than by theory, this is the best deep learning course available anywhere.

Building AI

Mon, 01 Jan 0001 00:00:00 +0000

The practical follow-up to Introduction to AI — bridges conceptual understanding to hands-on AI development. Covers machine learning algorithms, neural networks, and AI project planning with optional Python exercises. Designed for people who completed the intro course and want to go deeper without committing to a full computer science curriculum. The Python exercises are optional, making it accessible to non-developers who want to understand the mechanics.

Intro to Deep Learning

Mon, 01 Jan 0001 00:00:00 +0000

Kaggle’s hands-on deep learning micro-course — 4 hours covering neural networks, stochastic gradient descent, overfitting, dropout, and batch normalization. Shorter and more practical than Coursera specializations, with browser-based notebooks that run immediately. The fastest path from ‘I understand basic ML’ to ‘I can build neural networks’ — take this before investing in longer courses.

Intro to Machine Learning

Mon, 01 Jan 0001 00:00:00 +0000

The fastest hands-on introduction to machine learning — 3 hours from zero to building your first model. Kaggle’s browser-based notebooks mean no setup required, and the 30 free GPU hours per week let you experiment immediately. Covers decision trees, model validation, underfitting/overfitting, and random forests with real datasets. Take this if you want to get your hands dirty with ML code in an afternoon, not a semester.

Azure AI Fundamentals (AI-900)

Mon, 01 Jan 0001 00:00:00 +0000

Microsoft’s structured learning path for the AI-900 certification — covers AI workloads, machine learning principles, computer vision, NLP, and generative AI on Azure. The most enterprise-oriented AI fundamentals course available, designed for professionals who need to understand AI in the context of cloud services and business applications. Each module has knowledge checks and the path directly prepares you for the AI-900 certification exam.

Build with Claude — Overview

Mon, 01 Jan 0001 00:00:00 +0000

Central hub for understanding how to build applications with the Claude API.

Introduction to the Anthropic Platform

Mon, 01 Jan 0001 00:00:00 +0000

Overview of the Anthropic API platform, Claude models, and what you can build.

Vision & Multimodal AI

Mon, 01 Jan 0001 00:00:00 +0000

Build applications that understand and generate images, documents, and audio. This path covers vision capabilities across 5 providers, document processing, image generation, multimodal embeddings, and audio — the complete multimodal toolkit.

The key cross-provider insight: each provider has different vision strengths. OpenAI offers the broadest multimodal coverage (vision + generation + audio), Anthropic excels at document understanding, Cohere provides multimodal embeddings for search, and Mistral/Together AI offer cost-effective open-source alternatives. Choosing the right provider per modality can dramatically improve both quality and cost.

NLP Course

Mon, 01 Jan 0001 00:00:00 +0000

Hugging Face’s comprehensive NLP course — covers tokenizers, transformers, fine-tuning pretrained models, and the Hugging Face ecosystem (Datasets, Tokenizers, Transformers, Accelerate). The definitive open-source-first approach to NLP: everything runs on Hugging Face infrastructure with free GPU access. If you’re building with open models rather than proprietary APIs, start here.

LLM Course

Mon, 01 Jan 0001 00:00:00 +0000

Hugging Face’s newer LLM-specific course covering the full stack: using LLMs, building LLM applications, fine-tuning, and deploying. More focused on the modern LLM workflow than the NLP course. Includes RAG, quantization, and model evaluation. Take this for an open-source-first perspective on the same topics covered by the provider-specific learning paths in this knowledge base.

Cost Optimization

Mon, 01 Jan 0001 00:00:00 +0000

Minimize AI application costs without sacrificing quality. This path covers the complete cost optimization toolkit: model selection, token counting, prompt caching, and batch processing — comparing approaches across Anthropic and OpenAI.

The key insight: cost optimization is not about using cheaper models everywhere. It’s about matching the right model to each task, caching repeated content, batching non-urgent work, and measuring token usage to eliminate waste. A well-optimized pipeline using GPT-4o-mini + caching can cost less than a naive GPT-3.5 implementation.

Fine-Tuning Across Providers

Mon, 01 Jan 0001 00:00:00 +0000

Master fine-tuning across multiple providers — from data preparation to training to deployment. This advanced path covers OpenAI, Mistral, and Together AI’s fine-tuning workflows, with W&B for experiment tracking.

Fine-tuning is a powerful but expensive technique. This path emphasizes the decision framework (when to fine-tune vs alternatives), practical data preparation, and cross-provider comparison of capabilities, costs, and workflows. LoRA and reinforcement fine-tuning expand the toolkit beyond basic supervised fine-tuning.

AI Landscape for Product Leaders

Mon, 01 Jan 0001 00:00:00 +0000

A non-technical introduction to the AI provider landscape for product managers, executives, and business leaders. This path builds your understanding of what AI models can do, how they’re priced, and how to think about provider selection — without requiring any coding knowledge.

By the end of this path, you’ll be able to: evaluate AI provider options, understand cost structures for budgeting, and frame build-vs-buy decisions for your organization. You’ll have the vocabulary and mental models to discuss AI capabilities with your engineering team and present AI strategy to leadership.

Working With Messages

Mon, 01 Jan 0001 00:00:00 +0000

The Messages API is the core primitive — understand message roles, content blocks, and conversation structure.

AI Vendor & Platform Evaluation

Mon, 01 Jan 0001 00:00:00 +0000

A systematic framework for evaluating AI providers, platforms, and build-vs-buy decisions. This path equips technical leaders with the analytical tools to compare vendors across capability, cost, deployment, and strategic dimensions — going beyond feature matrices to assess lock-in risk, deployment flexibility, and long-term platform strategy.

By the end of this path, you’ll be able to: build comparative cost models across providers, structure vendor evaluation around the accuracy-latency-cost tradeoff triangle, assess deployment topology requirements, and frame build-vs-buy recommendations with supporting market data.

Extended Thinking

Mon, 01 Jan 0001 00:00:00 +0000

Enable Claude’s chain-of-thought reasoning for complex problems that benefit from step-by-step analysis.

01 Intro Basics

Mon, 01 Jan 0001 00:00:00 +0000

Learn the basics of fine-tuning LLMs with Mistral AI’s API and open-source tools for optimized performance

02 Prepare Dataset

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to prepare datasets for fine-tuning models across various use cases, from tone to coding and RAG

A Data Analyst Agent Built with Cohere and Langchain

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how to build a data-analysis system out of Cohere’s models.

A2A vs MCP

Mon, 01 Jan 0001 00:00:00 +0000

Understanding how A2A complements the Model Context Protocol

A2A vs MCP

Mon, 01 Jan 0001 00:00:00 +0000

Detailed comparison with Model Context Protocol

Access the W&B MCP Server

Mon, 01 Jan 0001 00:00:00 +0000

Connect your IDE or LLM application to W&B’s Model Context Protocol (MCP) server to provide your agent with access to your W&B workspace, data, and W&B’s documentation.

Add labels to runs with tags

Mon, 01 Jan 0001 00:00:00 +0000

Add W&B (wandb) to your code

Mon, 01 Jan 0001 00:00:00 +0000

Add W&B to your Python code script or Jupyter Notebook.

Add W&B to a Python library

Mon, 01 Jan 0001 00:00:00 +0000

Advanced Document Parsing For Enterprises

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how to use Cohere’s models to build a document-parsing agent.

Agent definitions

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to define an agent’s instructions, model, tools, and local context in the OpenAI Agents SDK.

Agent Workflows

Mon, 01 Jan 0001 00:00:00 +0000

Orchestrating together multiple language model calls to solve complex tasks.

Agentic RAG for PDFs with mixed data

Mon, 01 Jan 0001 00:00:00 +0000

This page describes building a powerful, multi-step chatbot with Cohere’s models.

Agents & Conversations

Mon, 01 Jan 0001 00:00:00 +0000

Agents & Conversations API: Create, manage agents with tools, and handle interactive conversations with persistent history

Agents Function Calling

Mon, 01 Jan 0001 00:00:00 +0000

Agents use tools and function calling to perform tasks, with built-in and customizable options

Agents Handoffs

Mon, 01 Jan 0001 00:00:00 +0000

Agents Handoffs enable seamless task delegation and workflow automation between multiple agents with diverse tools and capabilities

Agents Introduction

Mon, 01 Jan 0001 00:00:00 +0000

Introduction to Mistral’s agent system — autonomous task execution with tools, state persistence, connectors (code interpreter, web search), and multi-agent collaboration.

AI Evaluations UI

Mon, 01 Jan 0001 00:00:00 +0000

Guide to using the AI Evaluations UI for model assessment

AI Models for ADK agents

Mon, 01 Jan 0001 00:00:00 +0000

Amazon Bedrock

Mon, 01 Jan 0001 00:00:00 +0000

Ambassador

Mon, 01 Jan 0001 00:00:00 +0000

Join Mistral AI’s Ambassador Program to advocate, create content, and gain exclusive benefits for AI enthusiasts

An Overview of Cohere's Models

Mon, 01 Jan 0001 00:00:00 +0000

Cohere has a variety of models that cover many different use cases. If you need more customization, you can train a model to tune it to your specific use case.

An Overview of Cohere's Rerank Model

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how Cohere’s Rerank models work.

An Overview of The Cohere Platform

Mon, 01 Jan 0001 00:00:00 +0000

Cohere offers world-class Large Language Models (LLMs) like Command, Rerank, and Embed. These help developers and enterprises build LLM-powered applications.

An Overview of the Developer Playground

Mon, 01 Jan 0001 00:00:00 +0000

The Cohere Playground is a powerful visual interface for testing Cohere’s generation and embedding language models without coding.

Analysis of Form 10-K/10-Q Using Cohere and RAG

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how to use Cohere’s large language models to build an agent able to analyze financial forms like a 10-K or a 10-Q.

Annotate collections

Mon, 01 Jan 0001 00:00:00 +0000

Annotations

Mon, 01 Jan 0001 00:00:00 +0000

Mistral Document AI API extracts structured data from documents using custom JSON annotations for bboxes and full documents

Anthropic

Mon, 01 Jan 0001 00:00:00 +0000

Antitrust Policy

Mon, 01 Jan 0001 00:00:00 +0000

MCP Project Antitrust Policy for participants and contributors

Apigee AI Gateway

Mon, 01 Jan 0001 00:00:00 +0000

Apply patch

Mon, 01 Jan 0001 00:00:00 +0000

Allow models to propose structured diffs that your integration applies.

Architecture

Mon, 01 Jan 0001 00:00:00 +0000

Architecture overview

Mon, 01 Jan 0001 00:00:00 +0000

The most important mental model here is the three-layer architecture: Host (the application), Client (the protocol handler), and Server (the capability provider). A common mistake is conflating the host and the client – the host is the user-facing application (like Claude Desktop), while the client is an internal protocol component that maintains a 1:1 connection with a single server. Understanding this separation is essential before reading the specification or building anything.

Artifact data privacy and compliance

Mon, 01 Jan 0001 00:00:00 +0000

Learn where W&B files are stored by default. Explore how to save, store sensitive information.

Audio & Transcription

Mon, 01 Jan 0001 00:00:00 +0000

Audio & Transcription: Voxtral models enable chat and transcription via audio input with various file-passing methods

Authorization

Mon, 01 Jan 0001 00:00:00 +0000

The MCP authorization specification defines the OAuth 2.0-based flow that remote MCP servers use to authenticate clients, and it is the formal basis for everything the higher-level registry authentication guide simplifies. Focus on the required OAuth grant types, token scoping rules, and the metadata discovery mechanism that clients use to find a server’s authorization endpoint. A key gotcha is that the spec mandates specific OAuth metadata fields that many generic OAuth libraries do not populate by default, so you may need custom configuration even when using well-known auth frameworks. This is dense specification text best read alongside a working implementation for reference.

Authorization Extensions

Mon, 01 Jan 0001 00:00:00 +0000

Supplementary authorization mechanisms for the Model Context Protocol

Automation events and scopes

Mon, 01 Jan 0001 00:00:00 +0000

Available models

Mon, 01 Jan 0001 00:00:00 +0000

See the models you can train with Serverless RL

Available Models

Mon, 01 Jan 0001 00:00:00 +0000

Browse the foundation models available through W&B Inference

AWS Bedrock

Mon, 01 Jan 0001 00:00:00 +0000

Deploy and query Mistral AI models on AWS Bedrock with fully managed, serverless endpoints

AWS Private Deployment Guide (EC2 and EKS)

Mon, 01 Jan 0001 00:00:00 +0000

Deploying Cohere models in AWS via EC2 or EKS for enhanced security, compliance, and control.

Aya Expanse

Mon, 01 Jan 0001 00:00:00 +0000

Understand Cohere Labs highly performant multilingual Aya models, which aim to bring many more languages into generative AI.

Aya Family of Models

Mon, 01 Jan 0001 00:00:00 +0000

Understand Cohere Labs groundbreaking multilingual Aya models, which aim to bring many more languages into generative AI.

Aya Vision

Mon, 01 Jan 0001 00:00:00 +0000

Understand Cohere Labs groundbreaking multilingual model Aya Vision, a state-of-the-art multimodal language model excelling at multiple tasks.

Azure AI

Mon, 01 Jan 0001 00:00:00 +0000

Deploy and query Mistral AI models on Azure AI via serverless MaaS or GPU-based endpoints

Azure OpenAI Fine-Tuning

Mon, 01 Jan 0001 00:00:00 +0000

How to Fine-Tune Azure OpenAI models using W&B.

Bar plots

Mon, 01 Jan 0001 00:00:00 +0000

Visualize metrics, customize axes, and compare categorical data as bars.

Baseten

Mon, 01 Jan 0001 00:00:00 +0000

Basic OCR

Mon, 01 Jan 0001 00:00:00 +0000

Extract text and structured content from PDFs and images with Mistral’s Document AI OCR processor

Basic RAG

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to build a basic RAG system by combining retrieval and generation for AI-powered knowledge-based responses

Basic Semantic Search with Cohere Models

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how to do basic semantic search with Cohere’s models.

Batch Inference

Mon, 01 Jan 0001 00:00:00 +0000

Process multiple API requests in batches with customizable models, endpoints, and metadata

Bedrock

Mon, 01 Jan 0001 00:00:00 +0000

Bedrock

Mon, 01 Jan 0001 00:00:00 +0000

Track and monitor Amazon Bedrock LLM calls with Weave, capturing foundation model interactions, converse API usage, and multi-provider model deployments through AWS’s unified API for comprehensive observability.

BEMA for Reference Model

Mon, 01 Jan 0001 00:00:00 +0000

Bienvenue to Mistral AI Documentation

Mon, 01 Jan 0001 00:00:00 +0000

Mistral AI offers open-source and commercial LLMs, APIs, and tools for developers and enterprises to build AI-powered applications

Build an evaluation

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to build an evaluation pipeline with Weave Models and Evaluations

Build an MCP App

Mon, 01 Jan 0001 00:00:00 +0000

Getting started guide for building interactive UI applications with MCP Apps

Build an MCP client

Mon, 01 Jan 0001 00:00:00 +0000

Get started building your own client that can integrate with all MCP servers.

Build an MCP server

Mon, 01 Jan 0001 00:00:00 +0000

Get started building your own server to use in Claude for Desktop and other clients.

Build an Onboarding Assistant with Cohere!

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how to build an onboarding assistant with Cohere’s large language models.

Build with Agent Skills

Mon, 01 Jan 0001 00:00:00 +0000

Use agent skills to guide AI coding assistants through MCP server design and implementation

Build with Fireworks AI

Mon, 01 Jan 0001 00:00:00 +0000

Fast inference and fine-tuning for open source models

Building RAG models with Cohere

Mon, 01 Jan 0001 00:00:00 +0000

This page walks through building a retrieval-augmented generation model with Cohere.

Cancellation

Mon, 01 Jan 0001 00:00:00 +0000

Cerebras

Mon, 01 Jan 0001 00:00:00 +0000

Chat

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to query our open-source chat models.

Checkpoints and Resume

Mon, 01 Jan 0001 00:00:00 +0000

Save training progress, resume from failures, and promote checkpoints to deployable models — driven by the recipe.

Choosing A Model

Mon, 01 Jan 0001 00:00:00 +0000

The essential decision guide for anyone evaluating which Claude model to use. Anthropic organizes its models into tiers — Haiku for speed and cost efficiency, Sonnet for the best balance of capability and price, and Opus for maximum intelligence on complex tasks. For product leaders, the key insight is that model selection is a business decision, not just a technical one: choosing Haiku over Opus can reduce costs by 10-20x while still handling most routine tasks. Compare this tiered approach with OpenAI’s model lineup (GPT-4o, GPT-4o mini, o1) and Mistral’s range to understand how the industry structures the speed-cost-quality tradeoff.

Chroma BM25

Mon, 01 Jan 0001 00:00:00 +0000

Chroma Cloud Qwen

Mon, 01 Jan 0001 00:00:00 +0000

Chroma Cloud Splade

Mon, 01 Jan 0001 00:00:00 +0000

Citation Formatting

Mon, 01 Jan 0001 00:00:00 +0000

Learn practical citation formatting patterns that help models generate reliable citations.

Citations and References

Mon, 01 Jan 0001 00:00:00 +0000

Citations and references enable models to ground responses with sources, ideal for RAG and agentic applications

Classifier Factory

Mon, 01 Jan 0001 00:00:00 +0000

Create and fine-tune custom classification models for intent detection, moderation, sentiment analysis, and more using Mistral’s Classifier Factory

Claude

Mon, 01 Jan 0001 00:00:00 +0000

Claude Code

Mon, 01 Jan 0001 00:00:00 +0000

Use Claude Code with Fireworks AI models

CLI command reference

Mon, 01 Jan 0001 00:00:00 +0000

Clone and export reports

Mon, 01 Jan 0001 00:00:00 +0000

Export a W&B Report as a PDF or LaTeX.

Cloud

Mon, 01 Jan 0001 00:00:00 +0000

Access Mistral AI models via Azure, AWS, Google Cloud, Snowflake, IBM, and Outscale using cloud credits

Cloudflare Workers AI

Mon, 01 Jan 0001 00:00:00 +0000

Code Embeddings

Mon, 01 Jan 0001 00:00:00 +0000

Code embeddings enable retrieval, clustering, and analytics for code databases and coding assistants using Mistral AI’s API

Code generation

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to use OpenAI Codex models to generate code and build coding agents.

Code Interpreter

Mon, 01 Jan 0001 00:00:00 +0000

Code Interpreter enables safe, on-demand code execution for data analysis, graphing, and more in isolated containers

Code Interpreter

Mon, 01 Jan 0001 00:00:00 +0000

Allow models to write and run Python to solve problems.

Coding

Mon, 01 Jan 0001 00:00:00 +0000

Mistral AI offers Codestral for code generation & FIM, and Devstral for agentic tool use in software development, with integrations for IDEs and frameworks

Cohere

Mon, 01 Jan 0001 00:00:00 +0000

Cohere

Mon, 01 Jan 0001 00:00:00 +0000

Cohere Chat on LangChain (Integration Guide)

Mon, 01 Jan 0001 00:00:00 +0000

Integrate Cohere with LangChain to build applications using Cohere’s models and LangChain tools.

Cohere Embed on LangChain (Integration Guide)

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how to work with Cohere’s embeddings models and LangChain.

Cohere Models on Amazon Bedrock

Mon, 01 Jan 0001 00:00:00 +0000

This document provides a guide for using Cohere’s models on Amazon Bedrock.

Cohere on Amazon Web Services (AWS)

Mon, 01 Jan 0001 00:00:00 +0000

Access Cohere’s language models on AWS with customization options through Amazon SageMaker and Amazon Bedrock.

Cohere on Oracle Cloud Infrastructure (OCI)

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how to work with Cohere models on Oracle Cloud Infrastructure (OCI)

Cohere on the Microsoft Azure Platform

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how to work with Cohere models on Microsoft Azure.

Cohere Rerank on LangChain (Integration Guide)

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how to integrate Cohere’s ReRank models with LangChain.

Cohere Text Generation Tutorial

Mon, 01 Jan 0001 00:00:00 +0000

This page walks through how Cohere’s generation models work and how to use them.

Cohere's Command A Reasoning Model

Mon, 01 Jan 0001 00:00:00 +0000

Command A Reasoning excels in tool use, agentic workflows, and complex problem-solving. It has 111 billion parameters and a 256k context length.

Cohere's Command A Translate Model

Mon, 01 Jan 0001 00:00:00 +0000

Command A Translate is a state of the art model performant in 23 languages. It has a context length of 16K tokens and 111B parameters.

Cohere's Command A Vision Model

Mon, 01 Jan 0001 00:00:00 +0000

Command A Vision is a powerful visual language model capable of interacting with image inputs. This document contains information about its capabilities.

Cohere's Command R Model

Mon, 01 Jan 0001 00:00:00 +0000

Command R is a conversational model that excels in language tasks and supports multiple languages, making it ideal for coding use cases.

Cohere's Command R+ Model

Mon, 01 Jan 0001 00:00:00 +0000

Command R+ is Cohere’s optimized for conversational interaction and long-context tasks, best suited for complex RAG workflows and multi-step tool use.

Cohere's Command R7B Model

Mon, 01 Jan 0001 00:00:00 +0000

Command R7B is the smallest, fastest, and final model in our R family of enterprise-focused large language models. It excels at RAG, tool use, and agents.

Cohere's Embed Models (Details and Application)

Mon, 01 Jan 0001 00:00:00 +0000

Explore Embed models for text classification and embedding generation in English and multiple languages, with details on dimensions and endpoints.

Cohere's Rerank Model (Details and Application)

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how Cohere’s Rerank models work and how to use them.

Collaborate on reports

Mon, 01 Jan 0001 00:00:00 +0000

Collaborate and share W&B Reports with peers, co-workers, and your team.

Command R and Command R+ Model Card

Mon, 01 Jan 0001 00:00:00 +0000

This doc provides guidelines for using Cohere generation models ethically and constructively.

Compare and rank models

Mon, 01 Jan 0001 00:00:00 +0000

Compare and rank different model versions based on evaluation metrics

Compare model performance using the Evaluation Playground

Mon, 01 Jan 0001 00:00:00 +0000

Compare and evaluate model performance without code using Weave’s interactive playground, running evaluations with custom datasets and LLM judges to test system prompts, models, and scoring criteria in a visual interface.

Compare models provided by VertexAI on RAG-based Q&A task using Ragas metrics

Mon, 01 Jan 0001 00:00:00 +0000

Compare run metrics

Mon, 01 Jan 0001 00:00:00 +0000

Compare metrics across multiple runs

Compare runs across projects

Mon, 01 Jan 0001 00:00:00 +0000

Compare runs from two different projects with cross-project reports.

Compare traces and other logged information

Mon, 01 Jan 0001 00:00:00 +0000

Visually compare and diff code, traces, prompts, models, and configurations

Completion

Mon, 01 Jan 0001 00:00:00 +0000

Configuration

Mon, 01 Jan 0001 00:00:00 +0000

Tune model selection, retry logic, rate limits, and runner policies for production workloads.

Configure experiments

Mon, 01 Jan 0001 00:00:00 +0000

Use a dictionary-like object to save your experiment configuration

Configure prompt settings

Mon, 01 Jan 0001 00:00:00 +0000

Configure registry access

Mon, 01 Jan 0001 00:00:00 +0000

Connect Claude Code to tools via MCP

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to connect Claude Code to your tools with the Model Context Protocol.

Connect to a custom model

Mon, 01 Jan 0001 00:00:00 +0000

Connect to an OpenAI compliant model provider/proxy

Mon, 01 Jan 0001 00:00:00 +0000

Connect to any LLM

Mon, 01 Jan 0001 00:00:00 +0000

Comprehensive guide on integrating CrewAI with various Large Language Models (LLMs) using LiteLLM, including supported providers and configuration options.

Connect to local MCP servers

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to extend Claude Desktop with local MCP servers to enable file system access and other powerful integrations

Connect to remote MCP Servers

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to connect Claude to remote MCP servers and extend its capabilities with internet-hosted tools and data sources

Connectors and MCP servers

Mon, 01 Jan 0001 00:00:00 +0000

Use remote MCP servers and OpenAI-maintained connectors for popular services to give models new capabilities.

Connectors Overview

Mon, 01 Jan 0001 00:00:00 +0000

Connectors enable Agents and users to access tools like websearch, code interpreter, image generation, and document library on demand

Console logs

Mon, 01 Jan 0001 00:00:00 +0000

Contribute

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to contribute to Mistral AI through docs, code, community, and the Ambassador Program

Contributing to MCP

Mon, 01 Jan 0001 00:00:00 +0000

How to contribute to the Model Context Protocol project

Contributor Communication

Mon, 01 Jan 0001 00:00:00 +0000

Communication strategy and framework for the Model Context Protocol community

Contributor Ladder

Mon, 01 Jan 0001 00:00:00 +0000

Roles, responsibilities, and advancement criteria for MCP contributors, from first contribution to Core Maintainer

Conversation Simulator Model Callback

Mon, 01 Jan 0001 00:00:00 +0000

Conversation state

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to manage conversation state during a model interaction with the OpenAI API.

Cost optimization

Mon, 01 Jan 0001 00:00:00 +0000

Lower your OpenAI model costs by trying our tools and strategies.

Counting tokens

Mon, 01 Jan 0001 00:00:00 +0000

Count input tokens precisely for text, images, files, and tools using the Responses API — essential for cost management and context window planning.

Create a collection

Mon, 01 Jan 0001 00:00:00 +0000

Create a registry

Mon, 01 Jan 0001 00:00:00 +0000

Create a report

Mon, 01 Jan 0001 00:00:00 +0000

Create a W&B Report with the W&B App or programmatically.

Create a Slack automation

Mon, 01 Jan 0001 00:00:00 +0000

Create a webhook automation

Mon, 01 Jan 0001 00:00:00 +0000

Create an artifact

Mon, 01 Jan 0001 00:00:00 +0000

Create and log a W&B Artifact. Learn how to add one or more files or a URI reference to an Artifact.

Create an artifact alias

Mon, 01 Jan 0001 00:00:00 +0000

Create custom aliases for W&B Artifacts.

Create an artifact version

Mon, 01 Jan 0001 00:00:00 +0000

Create a new artifact version from a single run or from a distributed process.

Create an experiment

Mon, 01 Jan 0001 00:00:00 +0000

Create a W&B Experiment.

Create and track plots from experiments

Mon, 01 Jan 0001 00:00:00 +0000

Create and track plots from machine learning experiments.

Create Model

Mon, 01 Jan 0001 00:00:00 +0000

Create a new model.

Custom Model Cost

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to use custom model cost with W&B Weave

Custom model providers

Mon, 01 Jan 0001 00:00:00 +0000

Configure any LangChain-compatible model provider for the Deep Agents CLI

Custom model providers

Mon, 01 Jan 0001 00:00:00 +0000

Configure any LangChain-compatible model provider for the Deep Agents CLI

Custom models

Mon, 01 Jan 0001 00:00:00 +0000

Custom Models

Mon, 01 Jan 0001 00:00:00 +0000

Upload, verify, and deploy your own models from Hugging Face or elsewhere

Custom Structured Output

Mon, 01 Jan 0001 00:00:00 +0000

Define and enforce JSON output formats using Pydantic or Zod schemas with Mistral AI

Customise models

Mon, 01 Jan 0001 00:00:00 +0000

Customize log axes

Mon, 01 Jan 0001 00:00:00 +0000

Customize run colors

Mon, 01 Jan 0001 00:00:00 +0000

Customizing Prompts

Mon, 01 Jan 0001 00:00:00 +0000

Dive deeper into low-level prompt customization for CrewAI, enabling super custom and complex use cases for different models and languages.

Data modeling

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to structure records for efficient data retrieval and management in Pinecone.

Debug model vs. Pinecone recall issues

Mon, 01 Jan 0001 00:00:00 +0000

Debugging

Mon, 01 Jan 0001 00:00:00 +0000

A comprehensive guide to debugging Model Context Protocol (MCP) integrations

Dedicated Inference

Mon, 01 Jan 0001 00:00:00 +0000

Deploy models on your own custom endpoints for improved reliability at scale

Dedicated Models

Mon, 01 Jan 0001 00:00:00 +0000

Deep research

Mon, 01 Jan 0001 00:00:00 +0000

Use deep research models for autonomous multi-step research tasks — the model browses the web, synthesizes information, and produces comprehensive reports.

DeepSeek R1 Quickstart

Mon, 01 Jan 0001 00:00:00 +0000

How to get the most out of reasoning models like DeepSeek-R1.

Delete an artifact

Mon, 01 Jan 0001 00:00:00 +0000

Delete artifacts interactively with the App UI or programmatically with the W&B Python SDK.

Delete Model

Mon, 01 Jan 0001 00:00:00 +0000

Delete a model, all its checkpoints, artifacts, and the associated W&B run.

Delete Model Checkpoints

Mon, 01 Jan 0001 00:00:00 +0000

Delete specific checkpoints for a model.

Delete registry

Mon, 01 Jan 0001 00:00:00 +0000

Delete runs

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to delete runs from a W&B project using the W&B App.

Deploy Finetuned Command Models from AWS Marketplace

Mon, 01 Jan 0001 00:00:00 +0000

This document provides a guide for bringing your own finetuned models to Amazon SageMaker.

Deploy with Cerebrium

Mon, 01 Jan 0001 00:00:00 +0000

Deploy AI apps effortlessly with Cerebrium’s serverless GPU infrastructure and auto-scaling." (99 characters)

Deploy with Cloudflare Workers AI

Mon, 01 Jan 0001 00:00:00 +0000

Deploy AI models on Cloudflare’s global network with Workers AI for serverless GPU-powered LLMs

Deploy with SkyPilot

Mon, 01 Jan 0001 00:00:00 +0000

Deploy AI models on any cloud with SkyPilot for cost savings, high GPU availability, and managed execution

Deploy your finetuned model on AWS Marketplace

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to deploy your finetuned model on AWS Marketplace.

Deploying a Fine-tuned Model

Mon, 01 Jan 0001 00:00:00 +0000

Once your fine-tune job completes, you should see your new model in your models dashboard.

Deploying Fine Tuned Models

Mon, 01 Jan 0001 00:00:00 +0000

Deploy one or multiple LoRA models fine tuned on Fireworks using live merge or multi-LoRA

Deploying Models in Private Environments

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to pull and test Cohere’s container images using a license with Docker and Kubernetes.

Deployment Options - Overview

Mon, 01 Jan 0001 00:00:00 +0000

This page provides an overview of the available options for deploying Cohere’s models.

Deployments Quickstart

Mon, 01 Jan 0001 00:00:00 +0000

Deploy models on dedicated GPUs in minutes

Design Principles

Mon, 01 Jan 0001 00:00:00 +0000

The core design principles that guide the development of the Model Context Protocol.

Direct Model Requests

Mon, 01 Jan 0001 00:00:00 +0000

Direct preference optimization

Mon, 01 Jan 0001 00:00:00 +0000

Fine-tune models for subjective decision-making by comparing model outputs.

Document AI

Mon, 01 Jan 0001 00:00:00 +0000

Mistral Document AI offers enterprise-grade OCR, structured data extraction, and multilingual support for fast, accurate document processing

Document Library

Mon, 01 Jan 0001 00:00:00 +0000

Document Library enhances agents with uploaded documents via Mistral Cloud’s built-in RAG tool

Document QnA

Mon, 01 Jan 0001 00:00:00 +0000

Document QnA combines OCR and AI to enable natural language queries on document content for insights and extraction

Download an artifact from a registry

Mon, 01 Jan 0001 00:00:00 +0000

Download and use artifacts

Mon, 01 Jan 0001 00:00:00 +0000

Download and use Artifacts from multiple projects.

download the validation and reformat script

Mon, 01 Jan 0001 00:00:00 +0000

Download the reformat_data.py script to validate and reformat datasets for Mistral API fine-tuning

Edit a report

Mon, 01 Jan 0001 00:00:00 +0000

Edit a report interactively with the App UI or programmatically with the W&B SDK.

Elicitation

Mon, 01 Jan 0001 00:00:00 +0000

Embed a report

Mon, 01 Jan 0001 00:00:00 +0000

Embed W&B reports directly into Notion or with an HTML IFrame element.

Embed objects

Mon, 01 Jan 0001 00:00:00 +0000

W&B’s Embedding Projector allows users to plot multi-dimensional embeddings on a 2D plane using common dimension reduction algorithms like PCA, UMAP, and t-SNE.

Embeddings & Reranking

Mon, 01 Jan 0001 00:00:00 +0000

Generate embeddings and rerank results for semantic search

Embeddings Overview

Mon, 01 Jan 0001 00:00:00 +0000

Mistral’s Embeddings API for text and code vector representations — supporting retrieval, clustering, and classification with open-weight models.

Enterprise-Managed Authorization

Mon, 01 Jan 0001 00:00:00 +0000

Centralized access control for MCP in enterprise environments via identity providers

Environment variables

Mon, 01 Jan 0001 00:00:00 +0000

Set W&B environment variables.

Evaluate a hosted API model

Mon, 01 Jan 0001 00:00:00 +0000

Evaluate a hosted API model using infrastructure managed by CoreWeave

Evaluate a model checkpoint

Mon, 01 Jan 0001 00:00:00 +0000

Evaluate a VLLM-compatible model checkpoint using infrastructure managed by CoreWeave

Evaluate external models

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to run evals on non-OpenAI models, using the OpenAI platform.

Evaluate using local scorers

Mon, 01 Jan 0001 00:00:00 +0000

Small language models that run locally to evaluate AI system safety and quality

Evaluating Text Summarization Models

Mon, 01 Jan 0001 00:00:00 +0000

This page discusses how to evaluate a model’s text summarization.

Evaluation

Mon, 01 Jan 0001 00:00:00 +0000

Guide to evaluating LLMs for specific tasks with metrics, human, and LLM-based methods

Evaluation benchmark catalog

Mon, 01 Jan 0001 00:00:00 +0000

Browse the evaluation benchmarks available through LLM Evaluation Jobs

Evaluations with Vertex AI models

Mon, 01 Jan 0001 00:00:00 +0000

Example Clients

Mon, 01 Jan 0001 00:00:00 +0000

A list of applications that support MCP integrations

Example reports

Mon, 01 Jan 0001 00:00:00 +0000

Reports gallery

Example Servers

Mon, 01 Jan 0001 00:00:00 +0000

A list of example servers and implementations

Example tables

Mon, 01 Jan 0001 00:00:00 +0000

Examples of W&B Tables

Experiments limits and performance

Mon, 01 Jan 0001 00:00:00 +0000

Keep your pages in W&B faster and more responsive by logging within these suggested bounds.

Explore artifact lineage graphs

Mon, 01 Jan 0001 00:00:00 +0000

Traverse direct acyclic W&B Artifact graphs.

Export table data

Mon, 01 Jan 0001 00:00:00 +0000

How to export data from tables.

Extension Support Matrix

Mon, 01 Jan 0001 00:00:00 +0000

Which MCP clients implement which official extensions

Extensions Overview

Mon, 01 Jan 0001 00:00:00 +0000

Optional extensions to the Model Context Protocol

Filter runs

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to filter runs in the Runs table using the expression editor.

Financial CSV Agent with Langchain

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how to use Cohere’s models to build an agent able to work with CSV data.

Financial CSV Agent with Native Multi-Step Cohere API

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how to use Cohere’s models and its native API to build an agent able to work with CSV data.

Find and customize a run's ID or name

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to find a run’s unique identifier and run name, how to create a custom run ID, and how to customize a run’s name.

Find registry items

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to search for registries, collections, and artifact versions in the W&B Registry using the global search bar or queries.

Fine-tuning

Mon, 01 Jan 0001 00:00:00 +0000

Fine-tuning models incurs a $2 monthly storage fee per model; see pricing for details

Fine-tuning best practices

Mon, 01 Jan 0001 00:00:00 +0000

Practical tips for getting fine-tuning right — dataset quality vs quantity, avoiding overfitting, hyperparameter selection, and debugging poor results.

Fine-tuning BYOM

Mon, 01 Jan 0001 00:00:00 +0000

Bring Your Own Model: Fine-tune Custom Models from the Hugging Face Hub

Fine-tuning Guide

Mon, 01 Jan 0001 00:00:00 +0000

Learn the basics and best practices of fine-tuning large language models.

Finetuning Cohere Models on AWS Sagemaker

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to finetune one of Cohere’s models on AWS Sagemaker.

Fireworks Agent Overview

Mon, 01 Jan 0001 00:00:00 +0000

Describe what you want, approve the plan and cost, get a deployed fine-tuned model.

Fireworks Agent: Classification

Mon, 01 Jan 0001 00:00:00 +0000

Benchmark base models, fine-tune on labeled data, and pick the best classifier — automatically.

Fireworks Agent: Preference Learning (DPO/ORPO)

Mon, 01 Jan 0001 00:00:00 +0000

Run preference fine-tuning end-to-end with optional base-model sweep, automatic pair generation, and pairwise evaluation.

Fork a run

Mon, 01 Jan 0001 00:00:00 +0000

Explore different parameters or models from a specific point in an experiment without impacting the original run.

Frequently Asked Questions

Mon, 01 Jan 0001 00:00:00 +0000

This FAQ addresses the practical edge cases that the other registry guides do not cover in depth, including questions about naming conflicts, transfer of ownership, and delisting policies. Read this after you have gone through the main registry overview and authentication guides, since many answers assume familiarity with those workflows. The sections on troubleshooting publishing failures are particularly valuable if you encounter cryptic errors during your first submission attempt.

Frequently Asked Questions About Cohere

Mon, 01 Jan 0001 00:00:00 +0000

Cohere is a powerful platform for using Large Language Models (LLMs). This page covers FAQs related to functionality, pricing, troubleshooting, and more.

Fueling Generative Content with Keyword Research

Mon, 01 Jan 0001 00:00:00 +0000

This page contains a basic workflow for using Cohere’s models to come up with keyword content ideas.

Function calling

Mon, 01 Jan 0001 00:00:00 +0000

Function calling with Mistral models — integrate external tools for dynamic, data-driven responses using JSON Schema tool definitions.

Function calling

Mon, 01 Jan 0001 00:00:00 +0000

Learn how function calling enables large language models to connect to external data and systems.

Function Calling Fine-tuning

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to fine-tune models with function calling capabilities using Together AI.

Gemini

Mon, 01 Jan 0001 00:00:00 +0000

Gemma

Mon, 01 Jan 0001 00:00:00 +0000

Generate dense embeddings

Mon, 01 Jan 0001 00:00:00 +0000

Generate dense vector embeddings for the given texts using the specified model. Provide either ‘instructions’ or both ’task’ and ’target’ alongside ’texts’.

Generate sparse embeddings

Mon, 01 Jan 0001 00:00:00 +0000

Generate sparse vector embeddings for the given texts using the specified model. Provide either ‘instructions’ or both ’task’ and ’target’ alongside ’texts’. Set ‘fetch_labels’ to true to include token labels in the response.

get data from hugging face

Mon, 01 Jan 0001 00:00:00 +0000

FAQ on data validation, size limits, job creation, and fine-tuning details for Mistral API and mistral-finetune

Get Started with W&B Models

Mon, 01 Jan 0001 00:00:00 +0000

GitHub Copilot

Mon, 01 Jan 0001 00:00:00 +0000

Use Fireworks AI models in GitHub Copilot Chat via a custom endpoint

Glossary

Mon, 01 Jan 0001 00:00:00 +0000

Glossary of key AI and LLM terms, including LLMs, text generation, tokens, MoE, RAG, fine-tuning, function calling, embeddings, and temperature

Going Live with a Cohere Model

Mon, 01 Jan 0001 00:00:00 +0000

Learn to upgrade from a Trial to a Production key; understand the limitations and benefits of each and go live with Cohere.

Google

Mon, 01 Jan 0001 00:00:00 +0000

Google

Mon, 01 Jan 0001 00:00:00 +0000

Use Weave to trace and log Google GenAI model calls

Google Gemini

Mon, 01 Jan 0001 00:00:00 +0000

Governance and Stewardship

Mon, 01 Jan 0001 00:00:00 +0000

Learn about the Model Context Protocol’s governance structure and how to participate in the community

Groq

Mon, 01 Jan 0001 00:00:00 +0000

Groq

Mon, 01 Jan 0001 00:00:00 +0000

Track and monitor Groq’s ultra-fast LPU™ inference with Weave, capturing model calls, performance metrics, and function chains for high-speed LLM applications using Groq’s specialized hardware acceleration.

Grounded Summarization Using Command R

Mon, 01 Jan 0001 00:00:00 +0000

This page contains a basic tutorial on how to do grounded summarization with Cohere’s models.

Group Charter Template

Mon, 01 Jan 0001 00:00:00 +0000

Template for MCP Working Group and Interest Group charters.

Guides Using Custom Embedding Models

Mon, 01 Jan 0001 00:00:00 +0000

How do Structured Outputs Work?

Mon, 01 Jan 0001 00:00:00 +0000

Get Cohere models to produce structured outputs in JSON or tool-call format using response_format and JSON Schema constraints.

How Does Cohere's Pricing Work?

Mon, 01 Jan 0001 00:00:00 +0000

This page details Cohere’s pricing model. Our models can be accessed directly through our API, allowing for the creation of scalable production workloads.

How to Authenticate When Publishing to the Official MCP Registry

Mon, 01 Jan 0001 00:00:00 +0000

Authentication is the first barrier you will hit when trying to publish an MCP server to the official registry, and getting it wrong means silent failures with unhelpful error messages. Focus on how GitHub-based identity verification works and the relationship between your GitHub account and registry publishing permissions. If you are building CI/CD pipelines for MCP server releases, read this before the GitHub Actions guide so you understand the credential flow that automation depends on.

How to Automate Publishing with GitHub Actions

Mon, 01 Jan 0001 00:00:00 +0000

Automating MCP server publishing through GitHub Actions eliminates manual release steps and ensures every tagged release reaches the registry consistently. The guide walks through workflow file configuration, secret management for registry credentials, and trigger conditions for version-based publishing. A common pitfall is misconfiguring the authentication token scope, which produces permissions errors that surface only at publish time rather than during the workflow setup. Read the authentication guide first to understand the credential requirements before wiring up the action.

How to Get Predictable Outputs with Cohere Models

Mon, 01 Jan 0001 00:00:00 +0000

Strategies for decoding text, and the parameters that impact the randomness and predictability of a language model’s output.

How to handle model rate limits

Mon, 01 Jan 0001 00:00:00 +0000

How To Improve Search With Rerankers

Mon, 01 Jan 0001 00:00:00 +0000

Learn how you can improve semantic search quality with reranker models!

How to use Cline with DeepSeek V3 to build faster

Mon, 01 Jan 0001 00:00:00 +0000

Use Cline (an AI coding agent) with DeepSeek V3 (a powerful open source model) to code faster.

How to use OpenCode with Together AI to build faster

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to combine OpenCode, a powerful terminal-based AI coding agent, with Together AI models like DeepSeek V3 to supercharge your development workflow.

How to use Qwen Code with Together AI for enhanced development workflow

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to configure Qwen Code, a powerful AI-powered command-line workflow tool, with Together AI models to supercharge your coding workflow with advanced code understanding and automation.

Hugging Face

Mon, 01 Jan 0001 00:00:00 +0000

Hugging Face

Mon, 01 Jan 0001 00:00:00 +0000

Hugging Face

Mon, 01 Jan 0001 00:00:00 +0000

Hugging Face Accelerate

Mon, 01 Jan 0001 00:00:00 +0000

Training and inference at scale made simple, efficient and adaptable

Hugging Face Diffusers

Mon, 01 Jan 0001 00:00:00 +0000

Hugging Face Server

Mon, 01 Jan 0001 00:00:00 +0000

Hugging Face Simple Transformers

Mon, 01 Jan 0001 00:00:00 +0000

How to integrate W&B with the Transformers library by Hugging Face.

Hugging Face Transformers

Mon, 01 Jan 0001 00:00:00 +0000

Hydra

Mon, 01 Jan 0001 00:00:00 +0000

How to integrate W&B with Hydra.

IBM watsonx.ai

Mon, 01 Jan 0001 00:00:00 +0000

Mistral AI’s Large model on IBM watsonx.ai: SaaS & on-premise deployment with setup, API access, and usage guides

Image generation

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to generate or edit images with the OpenAI API and image generation models.

Image Generation

Mon, 01 Jan 0001 00:00:00 +0000

Built-in tool for agents to generate images on demand with detailed output handling and download options

Image Generation with Flux2

Mon, 01 Jan 0001 00:00:00 +0000

Deploy a Flux2 image generation model on Together’s managed GPU infrastructure using Dedicated Containers.

Import and export data

Mon, 01 Jan 0001 00:00:00 +0000

Import data from MLFlow, export or update data that you have saved to W&B

Initialize a sweep

Mon, 01 Jan 0001 00:00:00 +0000

Initialize a W&B Sweep

Initialize runs

Mon, 01 Jan 0001 00:00:00 +0000

Instructor

Mon, 01 Jan 0001 00:00:00 +0000

Instructor

Mon, 01 Jan 0001 00:00:00 +0000

Trace and evaluate structured data extraction from LLMs with Weave’s Instructor integration, capturing Pydantic model validation, retry logic, and JSON schema enforcement for reliable structured output workflows.

Integrating Embedding Models with Other Tools

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to integrate Cohere embeddings with open-source vector search engines for enhanced applications.

Integrations

Mon, 01 Jan 0001 00:00:00 +0000

Use Together AI models through partner integrations.

Introduction to Text Generation at Cohere

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how a large language model generates textual output.

Jina AI

Mon, 01 Jan 0001 00:00:00 +0000

JSON mode

Mon, 01 Jan 0001 00:00:00 +0000

Enable JSON mode by setting response_format to type json_object in API requests

Keras

Mon, 01 Jan 0001 00:00:00 +0000

Key Changes

Mon, 01 Jan 0001 00:00:00 +0000

Keyboard shortcuts

Mon, 01 Jan 0001 00:00:00 +0000

Learn about the keyboard shortcuts available in W&B.

Kimi K2 family

Mon, 01 Jan 0001 00:00:00 +0000

Using Kimi K2 family models in agentic and tool-calling workflows on Fireworks.

Kimi K2 QuickStart

Mon, 01 Jan 0001 00:00:00 +0000

How to get the most out of models like Kimi K2.

Kimi K2 Thinking QuickStart

Mon, 01 Jan 0001 00:00:00 +0000

How to get the most out of reasoning models like Kimi K2 Thinking.

Kimi K2.5 Quickstart

Mon, 01 Jan 0001 00:00:00 +0000

How to get the most out of Kimi’s new K2.5 model.

La Plateforme

Mon, 01 Jan 0001 00:00:00 +0000

Mistral AI’s La Plateforme offers pay-as-you-go API access to its latest models with flexible deployment options

LangChain overview

Mon, 01 Jan 0001 00:00:00 +0000

LangChain is an open source framework with a pre-built agent architecture and integrations for any model or tool — so you can build agents that adapt as fast as the ecosystem evolves

LangChain overview

Mon, 01 Jan 0001 00:00:00 +0000

LangChain is an open source framework with a pre-built agent architecture and integrations for any model or tool — so you can build agents that adapt as fast as the ecosystem evolves

LangDB Integration

Mon, 01 Jan 0001 00:00:00 +0000

Govern, secure, and optimize your CrewAI workflows with LangDB AI Gateway—access 350+ models, automatic routing, cost optimization, and full observability.

LangSmith shared responsibility model

Mon, 01 Jan 0001 00:00:00 +0000

Overview of how LangChain and customers share security responsibilities for the LangSmith platform.

Language Models

Mon, 01 Jan 0001 00:00:00 +0000

Learn How Cohere's Rerank Models Work

Mon, 01 Jan 0001 00:00:00 +0000

This page contains a basic tutorial on how Cohere’s ReRank models work and how to use them.

Learn more about sweeps

Mon, 01 Jan 0001 00:00:00 +0000

Collection of useful sources for Sweeps.

Lifecycle

Mon, 01 Jan 0001 00:00:00 +0000

The lifecycle specification defines the three phases every MCP connection goes through: initialization, operation, and shutdown. Focus especially on the initialization handshake where client and server exchange capabilities – getting this wrong is the most common source of connection failures. Note that the protocol version negotiation happens here too, so mismatched versions between client and server will fail fast at this stage rather than producing mysterious errors later.

Line plot reference

Mon, 01 Jan 0001 00:00:00 +0000

Lineage graphs and audit history

Mon, 01 Jan 0001 00:00:00 +0000

Use lineage graphs to visualize a linked artifact’s history and audit a collection’s history.

Link an artifact version to a collection

Mon, 01 Jan 0001 00:00:00 +0000

List Model Checkpoints

Mon, 01 Jan 0001 00:00:00 +0000

List Models

Mon, 01 Jan 0001 00:00:00 +0000

List Models

Mon, 01 Jan 0001 00:00:00 +0000

Get all available models and their IDs

LiteLLM

Mon, 01 Jan 0001 00:00:00 +0000

LiteLLM integration

Mon, 01 Jan 0001 00:00:00 +0000

Configure LiteLLM as a provider to fan out across multiple model backends.

LiteRT-LM

Mon, 01 Jan 0001 00:00:00 +0000

Llama 4 Quickstart

Mon, 01 Jan 0001 00:00:00 +0000

How to get the most out of the new Llama 4 models.

LlamaIndex and Cohere's Models

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to use Cohere and LlamaIndex together to generate responses based on data.

LLM Call Hooks

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to use LLM call hooks to intercept, modify, and control language model interactions in CrewAI

LLM gateway configuration

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to configure Claude Code to work with LLM gateway solutions. Covers gateway requirements, authentication configuration, model selection, and provider-specific endpoint setup.

LLMs

Mon, 01 Jan 0001 00:00:00 +0000

A comprehensive guide to configuring and using Large Language Models (LLMs) in your CrewAI projects

Log

Mon, 01 Jan 0001 00:00:00 +0000

Log trajectories and calculate metrics.

Log distributed training experiments

Mon, 01 Jan 0001 00:00:00 +0000

Use W&B to log distributed training experiments with multiple GPUs.

Log media and objects

Mon, 01 Jan 0001 00:00:00 +0000

Log rich media, from 3D point clouds and molecules to HTML and histograms

Log models

Mon, 01 Jan 0001 00:00:00 +0000

Log summary metrics

Mon, 01 Jan 0001 00:00:00 +0000

Log tables

Mon, 01 Jan 0001 00:00:00 +0000

Log tables

Mon, 01 Jan 0001 00:00:00 +0000

Log tables with W&B.

Logging

Mon, 01 Jan 0001 00:00:00 +0000

LoRA Fine-Tuning and Inference

Mon, 01 Jan 0001 00:00:00 +0000

Fine-tune and run inference for a model with LoRA adapters

Manage algorithms locally

Mon, 01 Jan 0001 00:00:00 +0000

Search and stop algorithms locally instead of using the W&B cloud-hosted service.

Manage artifact data retention

Mon, 01 Jan 0001 00:00:00 +0000

Time to live policies (TTL)

Manage artifact storage and memory allocation

Mon, 01 Jan 0001 00:00:00 +0000

Manage storage, memory allocation of W&B Artifacts.

Manage costs effectively

Mon, 01 Jan 0001 00:00:00 +0000

Track token usage, set team spend limits, and reduce Claude Code costs with context management, model selection, extended thinking settings, and preprocessing hooks.

Manage model configurations

Mon, 01 Jan 0001 00:00:00 +0000

Manage model configurations and control their availability across LangSmith features.

Manage sweeps

Mon, 01 Jan 0001 00:00:00 +0000

Pause, resume, and cancel a W&B Sweep with the CLI.

Manage workspace, section, and panel settings

Mon, 01 Jan 0001 00:00:00 +0000

Managed Fine-Tuning Overview

Mon, 01 Jan 0001 00:00:00 +0000

Fine-tune models with Fireworks-managed infrastructure — no custom code required.

Master Reranking with Cohere Models

Mon, 01 Jan 0001 00:00:00 +0000

This page contains a tutorial on using Cohere’s ReRank models.

MCP

Mon, 01 Jan 0001 00:00:00 +0000

MCP is an open standard protocol for seamless AI model integration with data sources and tools

MCP Apps

Mon, 01 Jan 0001 00:00:00 +0000

Interactive UI applications that render inside MCP hosts like Claude Desktop

MCP Inspector

Mon, 01 Jan 0001 00:00:00 +0000

In-depth guide to using the MCP Inspector for testing and debugging Model Context Protocol servers

MCP Registry Aggregators

Mon, 01 Jan 0001 00:00:00 +0000

MCP Registry Supported Package Types

Mon, 01 Jan 0001 00:00:00 +0000

The MCP registry supports multiple package types and choosing the wrong one constrains how clients can discover and install your server. Understanding the distinction between npm packages, Docker images, and other supported formats is critical before you publish, because changing package type after initial publication creates migration headaches for existing consumers. Focus on the compatibility matrix between package types and host applications, since not every MCP client supports every format equally well.

MCP Tools

Mon, 01 Jan 0001 00:00:00 +0000

Load additional tools from MCP (Model Context Protocol) servers

Media panels

Mon, 01 Jan 0001 00:00:00 +0000

Meeting minutes

Mon, 01 Jan 0001 00:00:00 +0000

Create an automated meeting minutes generator with Whisper and GPT-4.

Microsoft Foundry

Mon, 01 Jan 0001 00:00:00 +0000

Deploy frontier open models inside your Azure subscription, billed through Azure.

Migration Guide

Mon, 01 Jan 0001 00:00:00 +0000

Model migrations between Claude generations often introduce subtle behavioral changes that break existing prompts, even when the new model is strictly more capable. This guide documents the specific differences you should test for when upgrading, including changes to output formatting, tool-use patterns, and response length tendencies. Focus on the recommended testing strategies before doing a production cutover, since regressions frequently appear in edge cases rather than typical inputs. If you rely heavily on structured output or function calling, test those paths first as schema adherence can shift between model versions.

Mistral

Mon, 01 Jan 0001 00:00:00 +0000

Mistral

Mon, 01 Jan 0001 00:00:00 +0000

Mistral

Mon, 01 Jan 0001 00:00:00 +0000

MistralAI

Mon, 01 Jan 0001 00:00:00 +0000

Track and monitor MistralAI model calls with Weave’s automatic tracing, capturing chat completions, function calling, and model interactions for open-weight and commercial Mistral models.

Mixed adapter types

Mon, 01 Jan 0001 00:00:00 +0000

Model configuration

Mon, 01 Jan 0001 00:00:00 +0000

Learn about the Claude Code model configuration, including model aliases like opusplan.

Model Context Protocol

Mon, 01 Jan 0001 00:00:00 +0000

Connect MCP servers so agents can request external data or actions through standardized tool APIs.

Model Context Protocol (MCP)

Mon, 01 Jan 0001 00:00:00 +0000

Model Context Protocol (MCP)

Mon, 01 Jan 0001 00:00:00 +0000

Model Context Protocol (MCP)

Mon, 01 Jan 0001 00:00:00 +0000

LangChain’s MCP integration allows you to expose MCP servers as LangChain tools, bridging two of the most important ecosystems in the AI tooling space. This is particularly valuable if you have existing MCP servers and want to use them within LangChain agents or LangGraph workflows without rewriting tool definitions. Focus on how MCP tool schemas map to LangChain’s tool interface and what metadata is preserved or lost in translation. Be aware that MCP’s streaming and sampling capabilities may not map perfectly to LangChain’s tool abstraction, so test complex MCP servers thoroughly after integration.

Model Context Protocol (MCP) and Weave

Mon, 01 Jan 0001 00:00:00 +0000

Trace activity between your MCP client and MCP server with Weave

Model customization

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to customize LLMs for your application with system prompts, fine-tuning, and moderation layers

Model Deprecations

Mon, 01 Jan 0001 00:00:00 +0000

Model Gallery

Mon, 01 Jan 0001 00:00:00 +0000

Pinecone integrations enable you to build and deploy AI applications faster and more efficiently. Integrate Pinecone with your favorite frameworks, data sources, and infrastructure providers.

Model Lifecycle

Mon, 01 Jan 0001 00:00:00 +0000

Learn about W&B Inference model lifecycle and retirement

Model merging

Mon, 01 Jan 0001 00:00:00 +0000

Model optimization

Mon, 01 Jan 0001 00:00:00 +0000

Ensure quality model outputs with evals and fine-tuning in the OpenAI platform.

Model providers

Mon, 01 Jan 0001 00:00:00 +0000

Model selection

Mon, 01 Jan 0001 00:00:00 +0000

Guide to selecting Mistral models based on performance, cost, and use case complexity.’ (99 characters)

Model selection

Mon, 01 Jan 0001 00:00:00 +0000

How to choose the right OpenAI model by balancing accuracy, latency, and cost — the fundamental tradeoff triangle for every AI application.

Model Vault

Mon, 01 Jan 0001 00:00:00 +0000

This document provides a guide for using Cohere’s new Model Vault functionality.

Model weights

Mon, 01 Jan 0001 00:00:00 +0000

Open-source pre-trained and instruction-tuned models with various licenses, download links, and usage guidelines

Models

Mon, 01 Jan 0001 00:00:00 +0000

Models

Mon, 01 Jan 0001 00:00:00 +0000

Models

Mon, 01 Jan 0001 00:00:00 +0000

Models

Mon, 01 Jan 0001 00:00:00 +0000

Models

Mon, 01 Jan 0001 00:00:00 +0000

Models

Mon, 01 Jan 0001 00:00:00 +0000

Models and providers

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to choose models, set defaults, and think about providers and transport in the OpenAI Agents SDK.

Models Benchmarks

Mon, 01 Jan 0001 00:00:00 +0000

Mistral’s benchmarked models excel in reasoning, multilingual tasks, coding, and multimodal capabilities, outperforming competitors in key benchmarks

Models Overview

Mon, 01 Jan 0001 00:00:00 +0000

Mistral offers open and premier models for various tasks, including text, code, audio, and multimodal processing

Moderation

Mon, 01 Jan 0001 00:00:00 +0000

Mistral’s moderation API detects harmful content across multiple categories using AI-powered classification for text and conversations

Morph

Mon, 01 Jan 0001 00:00:00 +0000

Move a run to a different project or team

Mon, 01 Jan 0001 00:00:00 +0000

Move runs between projects or teams using the W&B App.

Nomic

Mon, 01 Jan 0001 00:00:00 +0000

null

Mon, 01 Jan 0001 00:00:00 +0000

OAuth Client Credentials

Mon, 01 Jan 0001 00:00:00 +0000

Machine-to-machine authentication for MCP using the OAuth 2.0 client credentials flow

Observability

Mon, 01 Jan 0001 00:00:00 +0000

Observability for LLMs ensures visibility, debugging, and performance optimization across prototyping, testing, and production

Official MCP Registry Terms of Service

Mon, 01 Jan 0001 00:00:00 +0000

Ollama

Mon, 01 Jan 0001 00:00:00 +0000

Ollama

Mon, 01 Jan 0001 00:00:00 +0000

OpenAI

Mon, 01 Jan 0001 00:00:00 +0000

OpenAI

Mon, 01 Jan 0001 00:00:00 +0000

OpenAI API

Mon, 01 Jan 0001 00:00:00 +0000

How to use W&B with the OpenAI API.

OpenAI Compatibility

Mon, 01 Jan 0001 00:00:00 +0000

Together’s API is compatible with OpenAI’s libraries, making it easy to try out our open-source models on existing applications.

OpenAI Fine-Tuning

Mon, 01 Jan 0001 00:00:00 +0000

How to Fine-Tune OpenAI models using W&B.

OpenAI GPT-OSS Quickstart

Mon, 01 Jan 0001 00:00:00 +0000

Get started with OpenAI’s GPT-OSS, open-source reasoning model duo.

OpenAI Gym

Mon, 01 Jan 0001 00:00:00 +0000

How to integrate W&B with OpenAI Gym.

OpenAI models in Amazon Bedrock

Mon, 01 Jan 0001 00:00:00 +0000

Learn how Amazon Bedrock availability differs from the OpenAI API, including supported capabilities, AWS-managed controls, and pricing considerations.

OpenCLIP

Mon, 01 Jan 0001 00:00:00 +0000

OpenRouter

Mon, 01 Jan 0001 00:00:00 +0000

Organize runs

Mon, 01 Jan 0001 00:00:00 +0000

Organize your runs into groups and other properties.

Organize versions with tags

Mon, 01 Jan 0001 00:00:00 +0000

Use tags to organize collections or artifact versions within collections. You can add, remove, edit tags with the Python SDK or W&B App UI.

Other resources

Mon, 01 Jan 0001 00:00:00 +0000

Explore Mistral AI Cookbook for code examples, community contributions, and third-party tool integrations

Outlines

Mon, 01 Jan 0001 00:00:00 +0000

Outscale

Mon, 01 Jan 0001 00:00:00 +0000

Deploy and query Mistral AI models on Outscale via managed VMs and REST APIs

Overview

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Mon, 01 Jan 0001 00:00:00 +0000

This is the essential reference for understanding the Claude model family and should be one of the first pages you read before building anything with the Anthropic API. Focus on the capability differences between model tiers (Haiku, Sonnet, Opus) as this directly impacts your cost, latency, and quality tradeoffs in production. Pay attention to context window sizes and maximum output token limits, since these constraints will shape your prompt design and chunking strategies. When comparing with OpenAI’s model lineup, note that Anthropic’s naming convention signals capability tier rather than generation number.

Overview

Mon, 01 Jan 0001 00:00:00 +0000

Train models using reinforcement learning in minutes

Overview

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Mon, 01 Jan 0001 00:00:00 +0000

Welcome to Together AI’s docs! Together makes it easy to run, finetune, and train open source AI models with transparency and privacy.

Overview

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to create configuration files for sweeps.

Pagination

Mon, 01 Jan 0001 00:00:00 +0000

Parallel coordinates

Mon, 01 Jan 0001 00:00:00 +0000

Compare results across machine learning experiments

Parallelize agents

Mon, 01 Jan 0001 00:00:00 +0000

Parallelize W&B Sweep agents on multi-core or multi-GPU machine.

Parameter importance

Mon, 01 Jan 0001 00:00:00 +0000

Visualize the relationships between your model’s hyperparameters and output metrics

Parameter Tuning

Mon, 01 Jan 0001 00:00:00 +0000

Learn how training parameters affect model behavior and outcomes

PEFT configurations and models

Mon, 01 Jan 0001 00:00:00 +0000

Perplexity

Mon, 01 Jan 0001 00:00:00 +0000

Pin and compare runs

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to use pinned and baseline runs to keep track of important runs and efficiently evaluate model experiments.

Ping

Mon, 01 Jan 0001 00:00:00 +0000

Playground

Mon, 01 Jan 0001 00:00:00 +0000

Guide to using Together AI’s web playground for interactive AI model inference across chat, image, video, audio, and transcribe models.

Point aggregation

Mon, 01 Jan 0001 00:00:00 +0000

Predicted outputs

Mon, 01 Jan 0001 00:00:00 +0000

Optimize response time by predefining predictable content for faster, efficient AI outputs." (99 characters)

Predicted Outputs

Mon, 01 Jan 0001 00:00:00 +0000

Understand how to reduce latency for model responses where much of the response is known ahead of time.

Prefix

Mon, 01 Jan 0001 00:00:00 +0000

Prefixes enhance model responses by improving language adherence, saving tokens, enabling roleplay, and strengthening safeguards

Pricing

Mon, 01 Jan 0001 00:00:00 +0000

Check the pricing page for detailed API cost information

Private Deployment – Setting Up

Mon, 01 Jan 0001 00:00:00 +0000

This page describes the setup required for private deployments of Cohere’s models.

Private Deployment Overview

Mon, 01 Jan 0001 00:00:00 +0000

This page provides an overview of private deployments of Cohere’s models.

Private Deployment Usage

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how to use Cohere’s SDK to access privately deployed Cohere models.

Progress

Mon, 01 Jan 0001 00:00:00 +0000

Projects

Mon, 01 Jan 0001 00:00:00 +0000

Compare versions of your model, explore results in a scratch workspace, and export findings to a report to save notes and visualizations

Prompt engineering

Mon, 01 Jan 0001 00:00:00 +0000

Learn strategies and tactics for better results using large language models in the OpenAI API.

Prompting capabilities

Mon, 01 Jan 0001 00:00:00 +0000

Learn effective prompting techniques for classification, summarization, personalization, and evaluation with Mistral models

Prompts

Mon, 01 Jan 0001 00:00:00 +0000

Prompts are the least commonly used of the three MCP primitives, but they fill an important niche: reusable, parameterized templates that help users invoke common workflows. Think of them as saved recipes that combine a specific prompt structure with dynamic arguments. Unlike tools and resources, prompts are user-initiated – the user explicitly selects a prompt from a menu rather than the model discovering it automatically. This makes them ideal for standardizing repetitive interactions like “summarize this codebase” or “review this PR.”

Publishing Remote Servers

Mon, 01 Jan 0001 00:00:00 +0000

Remote servers represent a significant architectural shift from the local stdio-based MCP servers most developers start with, enabling cloud-hosted tools accessible over HTTP with persistent sessions. This guide covers the specific requirements for publishing remote servers to the registry, including endpoint URL configuration and health check expectations. Watch for the distinction between the server’s transport URL and its registry metadata URL, as confusing the two is a frequent source of registration failures. Read this after the general registry overview if you plan to deploy MCP servers as hosted services rather than local processes.

Pydantic Model

Mon, 01 Jan 0001 00:00:00 +0000

PyTorch

Mon, 01 Jan 0001 00:00:00 +0000

PyTorch Geometric

Mon, 01 Jan 0001 00:00:00 +0000

PyTorch Ignite

Mon, 01 Jan 0001 00:00:00 +0000

How to integrate W&B with PyTorch Ignite.

PyTorch Lightning

Mon, 01 Jan 0001 00:00:00 +0000

Pytorch torchtune

Mon, 01 Jan 0001 00:00:00 +0000

Quantization

Mon, 01 Jan 0001 00:00:00 +0000

Reduce model precision to improve performance and lower costs

Quickstart

Mon, 01 Jan 0001 00:00:00 +0000

Quickstart guide for setting up a Mistral AI account, configuring billing, and using the API for models and embeddings

Quickstart: Flux Kontext

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to use Flux’s new in-context image generation models

Quickstart: FLUX.2

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to use FLUX.2, the next generation image model with advanced prompting capabilities

Quickstart: How to do OCR

Mon, 01 Jan 0001 00:00:00 +0000

A step by step guide on how to do OCR with Together AI’s vision models with structured outputs

Quickstart: How to Use OpenClaw with Together AI

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to pair OpenClaw, a powerful autonomous agent, with frontier OSS models on Together AI like Kimi K2.5 and GLM 4.7.

Quickstart: Publish an MCP Server to the MCP Registry

Mon, 01 Jan 0001 00:00:00 +0000

Publishing your MCP server to the registry makes it discoverable by any MCP client, which is a critical step for adoption beyond your own projects. Focus on the metadata requirements (name, description, capabilities) because poorly described servers are effectively invisible in the registry. Note that the registry currently requires GitHub-based authentication and a specific package manifest format — read through the validation errors carefully if your first publish attempt fails, as the error messages are quite specific. Come to this doc after you have a working server from the build-server tutorial, since you need a functional server before publishing makes sense.

Quickstart: Using Hugging Face Inference With Together

Mon, 01 Jan 0001 00:00:00 +0000

This guide will walk you through how to use Together models with Hugging Face Inference.

Quickstart: Using Mastra with Together AI

Mon, 01 Jan 0001 00:00:00 +0000

This guide will walk you through how to use Together models with Mastra.

Quickstart: Using Vercel AI SDK With Together AI

Mon, 01 Jan 0001 00:00:00 +0000

This guide will walk you through how to use Together models with the Vercel AI SDK.

Quickstart: Wan 2.7 T2V

Mon, 01 Jan 0001 00:00:00 +0000

Generate videos from text prompts with optional audio input using the Wan 2.7 T2V model.

Rate limit and usage tiers

Mon, 01 Jan 0001 00:00:00 +0000

Learn about Mistral’s API rate limits, usage tiers, and how to upgrade for higher capacity.’ (99 characters)

Reasoning

Mon, 01 Jan 0001 00:00:00 +0000

How to use reasoning with Fireworks models

Reasoning

Mon, 01 Jan 0001 00:00:00 +0000

Reasoning models generate logical chains of thought to solve problems, improving accuracy with extra compute time." (99 characters)

Reasoning

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to use reasoning models that think step-by-step before answering.

Reasoning best practices

Mon, 01 Jan 0001 00:00:00 +0000

Best practices for using o-series reasoning models (o1, o3-mini) vs GPT models — covering use cases, model selection, and prompting strategies specific to reasoning models.

Reasoning Capabilities

Mon, 01 Jan 0001 00:00:00 +0000

Reasoning models excel at tool use, agentic workflows, and complex problem-solving. This page provides a general overview of Cohere’s reasoning capalities.

Reasoning Fine-tuning

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to fine-tune reasoning models with chain-of-thought data using Together AI.

Reasoning models

Mon, 01 Jan 0001 00:00:00 +0000

Explore the capabilities of OpenAI’s o1 series for complex reasoning and problem-solving. Learn about their features and how they compare to GPT-4o models.

Reasoning Models Guide

Mon, 01 Jan 0001 00:00:00 +0000

How reasoning models like DeepSeek-R1 work.

Reasoning tokens

Mon, 01 Jan 0001 00:00:00 +0000

Display model thinking and reasoning processes in collapsible blocks

Reasoning tokens

Mon, 01 Jan 0001 00:00:00 +0000

Display model thinking and reasoning processes in collapsible blocks

Recommended Models

Mon, 01 Jan 0001 00:00:00 +0000

Find the right models for your use case

Reference an artifact version with aliases

Mon, 01 Jan 0001 00:00:00 +0000

Reference overview

Mon, 01 Jan 0001 00:00:00 +0000

Generated documentation for W&B APIs

Regions

Mon, 01 Jan 0001 00:00:00 +0000

Fireworks runs a global fleet of hardware on which you can deploy your models.

Reinforcement fine-tuning

Mon, 01 Jan 0001 00:00:00 +0000

Train models using reward-based signals for expert-level domain performance — going beyond supervised fine-tuning when you can grade quality but can’t easily provide gold outputs.

Reinforcement fine-tuning use cases

Mon, 01 Jan 0001 00:00:00 +0000

Practical use cases and best practices for reinforcement fine-tuning (RFT) — when graded rewards outperform simple input/output pairs for training signal.

Reproduce experiments

Mon, 01 Jan 0001 00:00:00 +0000

Reranking - Cohere on Azure AI Foundry

Mon, 01 Jan 0001 00:00:00 +0000

A guide for performing reranking with Cohere’s Reranking models on Azure AI Foundry (API v2).

Reranking - quickstart

Mon, 01 Jan 0001 00:00:00 +0000

A quickstart guide for performing reranking with Cohere’s Reranking models (v2 API).

Resources

Mon, 01 Jan 0001 00:00:00 +0000

Resources are how MCP servers expose data to the model without requiring the model to “call” anything – think of them as files or documents the application can pull into context. The key distinction from tools is that resources are application-controlled (the host decides when to read them), while tools are model-controlled (the model decides when to invoke them). Pay attention to the URI-based addressing scheme, which lets clients discover and subscribe to resource updates. If your use case is primarily about providing context rather than performing actions, resources are usually the better primitive.

Resume a run

Mon, 01 Jan 0001 00:00:00 +0000

Resume a paused or exited W&B Run

Retrieval augmented generation (RAG) - Cohere on Azure AI Foundry

Mon, 01 Jan 0001 00:00:00 +0000

A guide for performing retrieval augmented generation (RAG) with Cohere’s Command models on Azure AI Foundry (API v2).

Retrieval augmented generation (RAG) - quickstart

Mon, 01 Jan 0001 00:00:00 +0000

A quickstart guide for performing retrieval augmented generation (RAG) with Cohere’s Command models (v2 API).

Reward Modeling

Mon, 01 Jan 0001 00:00:00 +0000

Rewind a run

Mon, 01 Jan 0001 00:00:00 +0000

Rewind a run to correct or modify its history without losing original data.

Roadmap

Mon, 01 Jan 0001 00:00:00 +0000

Our plans for evolving Model Context Protocol

Roboflow

Mon, 01 Jan 0001 00:00:00 +0000

Roots

Mon, 01 Jan 0001 00:00:00 +0000

Run states

Mon, 01 Jan 0001 00:00:00 +0000

Learn about the different states a W&B run can have.

Safety Modes

Mon, 01 Jan 0001 00:00:00 +0000

The safety modes documentation describes how to use default and strict modes in order to exercise additional control over model output.

Sampling

Mon, 01 Jan 0001 00:00:00 +0000

Sampling is the mechanism that allows an MCP server to request LLM completions through the client, effectively enabling servers to leverage AI capabilities without embedding their own model access. This is one of the most powerful and least intuitive parts of the MCP specification because it inverts the typical client-server relationship. Pay careful attention to the human-in-the-loop requirements, since the spec mandates that clients must obtain user approval before fulfilling sampling requests, which has significant UX implications. If you are building agentic MCP servers that need to reason or generate text, understanding this capability is essential.

Sampling

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to adjust LLM sampling parameters like Temperature, Top P, and penalties for better output control

Save and diff code

Mon, 01 Jan 0001 00:00:00 +0000

Scatter plots

Mon, 01 Jan 0001 00:00:00 +0000

Schema Reference

Mon, 01 Jan 0001 00:00:00 +0000

SDK Clients

Mon, 01 Jan 0001 00:00:00 +0000

Official Python & TypeScript SDKs and community clients for Mistral AI

SDK Tiering System

Mon, 01 Jan 0001 00:00:00 +0000

Feature completeness, protocol support, and maintenance commitment levels for Model Context Protocol SDKs

SDKs

Mon, 01 Jan 0001 00:00:00 +0000

Official SDKs for building with Model Context Protocol

Search runs

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to search for specific runs by name or ID in your project’s Runs table or Workspace.

Secure Training (BYOB)

Mon, 01 Jan 0001 00:00:00 +0000

Fine-tune models while keeping sensitive data and components under your control

Security Best Practices

Mon, 01 Jan 0001 00:00:00 +0000

Security considerations, attack vectors, and best practices for MCP implementations

Self-deployment

Mon, 01 Jan 0001 00:00:00 +0000

Deploy Mistral AI models on your infrastructure using vLLM, TensorRT-LLM, TGI, or tools like SkyPilot and Cerebrium

Semantic run plot legends

Mon, 01 Jan 0001 00:00:00 +0000

Create semantic legends for charts

Semantic search - Cohere on Azure AI Foundry

Mon, 01 Jan 0001 00:00:00 +0000

A guide for performing text semantic search with Cohere’s Embed models on Azure AI Foundry (API v2).

Semantic search - quickstart

Mon, 01 Jan 0001 00:00:00 +0000

A quickstart guide for performing text semantic search with Cohere’s Embed models (v2 API).

Semantic Search with Cohere Models

Mon, 01 Jan 0001 00:00:00 +0000

This is a tutorial describing how to leverage Cohere’s models for semantic search.

Send an alert

Mon, 01 Jan 0001 00:00:00 +0000

Send alerts, triggered from your Python code, to your Slack or email

Sentence Transformer

Mon, 01 Jan 0001 00:00:00 +0000

SEP Guidelines

Mon, 01 Jan 0001 00:00:00 +0000

Specification Enhancement Proposal (SEP) guidelines for proposing changes to the Model Context Protocol

SEP-1024: MCP Client Security Requirements for Local Server Installation

Mon, 01 Jan 0001 00:00:00 +0000

MCP Client Security Requirements for Local Server Installation

SEP-1024: MCP Client Security Requirements for Local Server Installation

Mon, 01 Jan 0001 00:00:00 +0000

MCP Client Security Requirements for Local Server Installation

SEP-1034: Support default values for all primitive types in elicitation schemas

Mon, 01 Jan 0001 00:00:00 +0000

Support default values for all primitive types in elicitation schemas

SEP-1034: Support default values for all primitive types in elicitation schemas

Mon, 01 Jan 0001 00:00:00 +0000

Support default values for all primitive types in elicitation schemas

SEP-1036: URL Mode Elicitation for secure out-of-band interactions

Mon, 01 Jan 0001 00:00:00 +0000

URL Mode Elicitation for secure out-of-band interactions

SEP-1036: URL Mode Elicitation for secure out-of-band interactions

Mon, 01 Jan 0001 00:00:00 +0000

URL Mode Elicitation for secure out-of-band interactions

SEP-1046: Support OAuth client credentials flow in authorization

Mon, 01 Jan 0001 00:00:00 +0000

Support OAuth client credentials flow in authorization

SEP-1046: Support OAuth client credentials flow in authorization

Mon, 01 Jan 0001 00:00:00 +0000

Support OAuth client credentials flow in authorization

SEP-1302: Formalize Working Groups and Interest Groups in MCP Governance

Mon, 01 Jan 0001 00:00:00 +0000

Formalize Working Groups and Interest Groups in MCP Governance

SEP-1302: Formalize Working Groups and Interest Groups in MCP Governance

Mon, 01 Jan 0001 00:00:00 +0000

Formalize Working Groups and Interest Groups in MCP Governance

SEP-1303: Input Validation Errors as Tool Execution Errors

Mon, 01 Jan 0001 00:00:00 +0000

Input Validation Errors as Tool Execution Errors

SEP-1303: Input Validation Errors as Tool Execution Errors

Mon, 01 Jan 0001 00:00:00 +0000

Input Validation Errors as Tool Execution Errors

SEP-1319: Decouple Request Payload from RPC Methods Definition

Mon, 01 Jan 0001 00:00:00 +0000

Decouple Request Payload from RPC Methods Definition

SEP-1319: Decouple Request Payload from RPC Methods Definition

Mon, 01 Jan 0001 00:00:00 +0000

Decouple Request Payload from RPC Methods Definition

SEP-1330: Elicitation Enum Schema Improvements and Standards Compliance

Mon, 01 Jan 0001 00:00:00 +0000

Elicitation Enum Schema Improvements and Standards Compliance

SEP-1330: Elicitation Enum Schema Improvements and Standards Compliance

Mon, 01 Jan 0001 00:00:00 +0000

Elicitation Enum Schema Improvements and Standards Compliance

SEP-1577: Sampling With Tools

Mon, 01 Jan 0001 00:00:00 +0000

Sampling With Tools

SEP-1577: Sampling With Tools

Mon, 01 Jan 0001 00:00:00 +0000

Sampling With Tools

SEP-1613: Establish JSON Schema 2020-12 as Default Dialect for MCP

Mon, 01 Jan 0001 00:00:00 +0000

Establish JSON Schema 2020-12 as Default Dialect for MCP

SEP-1613: Establish JSON Schema 2020-12 as Default Dialect for MCP

Mon, 01 Jan 0001 00:00:00 +0000

Establish JSON Schema 2020-12 as Default Dialect for MCP

SEP-1686: Tasks

Mon, 01 Jan 0001 00:00:00 +0000

Tasks

SEP-1686: Tasks

Mon, 01 Jan 0001 00:00:00 +0000

Tasks

SEP-1699: Support SSE polling via server-side disconnect

Mon, 01 Jan 0001 00:00:00 +0000

Support SSE polling via server-side disconnect

SEP-1699: Support SSE polling via server-side disconnect

Mon, 01 Jan 0001 00:00:00 +0000

Support SSE polling via server-side disconnect

SEP-1730: SDKs Tiering System

Mon, 01 Jan 0001 00:00:00 +0000

SDKs Tiering System

SEP-1730: SDKs Tiering System

Mon, 01 Jan 0001 00:00:00 +0000

SDKs Tiering System

SEP-1850: PR-Based SEP Workflow

Mon, 01 Jan 0001 00:00:00 +0000

PR-Based SEP Workflow

SEP-1850: PR-Based SEP Workflow

Mon, 01 Jan 0001 00:00:00 +0000

PR-Based SEP Workflow

SEP-1865: MCP Apps - Interactive User Interfaces for MCP

Mon, 01 Jan 0001 00:00:00 +0000

MCP Apps - Interactive User Interfaces for MCP

SEP-1865: MCP Apps - Interactive User Interfaces for MCP

Mon, 01 Jan 0001 00:00:00 +0000

MCP Apps - Interactive User Interfaces for MCP

SEP-2085: Governance Succession and Amendment Procedures

Mon, 01 Jan 0001 00:00:00 +0000

Governance Succession and Amendment Procedures

SEP-2085: Governance Succession and Amendment Procedures

Mon, 01 Jan 0001 00:00:00 +0000

Governance Succession and Amendment Procedures

SEP-2133: Extensions

Mon, 01 Jan 0001 00:00:00 +0000

Extensions

SEP-2133: Extensions

Mon, 01 Jan 0001 00:00:00 +0000

Extensions

SEP-2148: MCP Contributor Ladder

Mon, 01 Jan 0001 00:00:00 +0000

MCP Contributor Ladder

SEP-2149: MCP Group Governance and Charter Template

Mon, 01 Jan 0001 00:00:00 +0000

MCP Group Governance and Charter Template

SEP-2207: OIDC-Flavored Refresh Token Guidance

Mon, 01 Jan 0001 00:00:00 +0000

OIDC-Flavored Refresh Token Guidance

SEP-2260: Require Server requests to be associated with a Client request.

Mon, 01 Jan 0001 00:00:00 +0000

Require Server requests to be associated with a Client request.

SEP-414: Document OpenTelemetry Trace Context Propagation Conventions

Mon, 01 Jan 0001 00:00:00 +0000

Document OpenTelemetry Trace Context Propagation Conventions

SEP-414: Document OpenTelemetry Trace Context Propagation Conventions

Mon, 01 Jan 0001 00:00:00 +0000

Document OpenTelemetry Trace Context Propagation Conventions

SEP-932: Model Context Protocol Governance

Mon, 01 Jan 0001 00:00:00 +0000

Model Context Protocol Governance

SEP-932: Model Context Protocol Governance

Mon, 01 Jan 0001 00:00:00 +0000

Model Context Protocol Governance

SEP-973: Expose additional metadata for Implementations, Resources, Tools and Prompts

Mon, 01 Jan 0001 00:00:00 +0000

Expose additional metadata for Implementations, Resources, Tools and Prompts

SEP-973: Expose additional metadata for Implementations, Resources, Tools and Prompts

Mon, 01 Jan 0001 00:00:00 +0000

Expose additional metadata for Implementations, Resources, Tools and Prompts

SEP-985: Align OAuth 2.0 Protected Resource Metadata with RFC 9728

Mon, 01 Jan 0001 00:00:00 +0000

Align OAuth 2.0 Protected Resource Metadata with RFC 9728

SEP-985: Align OAuth 2.0 Protected Resource Metadata with RFC 9728

Mon, 01 Jan 0001 00:00:00 +0000

Align OAuth 2.0 Protected Resource Metadata with RFC 9728

SEP-986: Specify Format for Tool Names

Mon, 01 Jan 0001 00:00:00 +0000

Specify Format for Tool Names

SEP-986: Specify Format for Tool Names

Mon, 01 Jan 0001 00:00:00 +0000

Specify Format for Tool Names

SEP-990: Enable enterprise IdP policy controls during MCP OAuth flows

Mon, 01 Jan 0001 00:00:00 +0000

Enable enterprise IdP policy controls during MCP OAuth flows

SEP-990: Enable enterprise IdP policy controls during MCP OAuth flows

Mon, 01 Jan 0001 00:00:00 +0000

Enable enterprise IdP policy controls during MCP OAuth flows

SEP-991: Enable URL-based Client Registration using OAuth Client ID Metadata Documents

Mon, 01 Jan 0001 00:00:00 +0000

Enable URL-based Client Registration using OAuth Client ID Metadata Documents

SEP-991: Enable URL-based Client Registration using OAuth Client ID Metadata Documents

Mon, 01 Jan 0001 00:00:00 +0000

Enable URL-based Client Registration using OAuth Client ID Metadata Documents

SEP-994: Shared Communication Practices/Guidelines

Mon, 01 Jan 0001 00:00:00 +0000

Shared Communication Practices/Guidelines

SEP-994: Shared Communication Practices/Guidelines

Mon, 01 Jan 0001 00:00:00 +0000

Shared Communication Practices/Guidelines

Server Card Charter

Mon, 01 Jan 0001 00:00:00 +0000

Charter for the MCP Server Card Working Group.

Serverless Models

Mon, 01 Jan 0001 00:00:00 +0000

Serverless Overview

Mon, 01 Jan 0001 00:00:00 +0000

How Serverless inference works on Fireworks: serving paths, billing, request/response headers, prompt caching, model lifecycle, and when to choose Serverless over On-demand

Serverless Pricing

Mon, 01 Jan 0001 00:00:00 +0000

Per-token serverless pricing for text, vision, and embedding models, including Priority and Fast serving paths

Short-Term Memory Handling for Agents

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how to manage short-term memory in an agent built with Cohere models.

Signal handling and sweep runs

Mon, 01 Jan 0001 00:00:00 +0000

Learn how W&B Sweeps handle UNIX signals, exit codes, and preemption in sweep runs.

Single-Turn Training Quickstart

Mon, 01 Jan 0001 00:00:00 +0000

Train a model to be an expert at answering GSM8K math questions

Smolagents

Mon, 01 Jan 0001 00:00:00 +0000

Track and analyze Smolagents agentic applications with Weave’s automatic tracing, capturing tool calls, model inferences, and multi-step workflows across OpenAI, Hugging Face, and Anthropic LLM providers.

Smooth line plots

Mon, 01 Jan 0001 00:00:00 +0000

In line plots, use smoothing to see trends in noisy data.

Snowflake Cortex

Mon, 01 Jan 0001 00:00:00 +0000

Access Mistral AI models on Snowflake Cortex as serverless, fully managed endpoints for SQL & Python

Specification

Mon, 01 Jan 0001 00:00:00 +0000

Specification Enhancement Proposals (SEPs)

Mon, 01 Jan 0001 00:00:00 +0000

Index of all MCP Specification Enhancement Proposals

Specification Enhancement Proposals (SEPs)

Mon, 01 Jan 0001 00:00:00 +0000

Index of all MCP Specification Enhancement Proposals

Speculative Decoding

Mon, 01 Jan 0001 00:00:00 +0000

Speed up generation with draft models and n-gram speculation

Start a sweep agent

Mon, 01 Jan 0001 00:00:00 +0000

Start or stop a W&B Sweep Agent on one or more machines.

Stop runs

Mon, 01 Jan 0001 00:00:00 +0000

Stop runs programmatically using the W&B Python SDK or manually from the W&B App.

Streaming API responses

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to stream model responses from the OpenAI API using server-sent events.

Structured model outputs

Mon, 01 Jan 0001 00:00:00 +0000

Understand how to ensure model responses follow specific JSON Schema you define.

Structured Output

Mon, 01 Jan 0001 00:00:00 +0000

Learn to generate structured outputs like JSON for LLM agents and pipelines, with custom and flexible formatting options

Supervised Fine Tuning - Text

Mon, 01 Jan 0001 00:00:00 +0000

Supervised Fine Tuning - Vision

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to fine-tune vision-language models on Fireworks AI with image and text datasets

Supervised fine-tuning

Mon, 01 Jan 0001 00:00:00 +0000

Fine-tune OpenAI models with input/output pairs — covering data preparation, training configuration, validation, and deployment of customized models.

Support

Mon, 01 Jan 0001 00:00:00 +0000

Support: Models

Mon, 01 Jan 0001 00:00:00 +0000

Supported Models

Mon, 01 Jan 0001 00:00:00 +0000

Supported models for Evaluations

Supported Models

Mon, 01 Jan 0001 00:00:00 +0000

A list of all the models available for fine-tuning.

Sweep configuration options

Mon, 01 Jan 0001 00:00:00 +0000

Sweeps troubleshooting

Mon, 01 Jan 0001 00:00:00 +0000

Troubleshoot common W&B Sweep issues.

Sweeps UI

Mon, 01 Jan 0001 00:00:00 +0000

Describes the different components of the Sweeps UI.

Tasks

Mon, 01 Jan 0001 00:00:00 +0000

TensorFlow

Mon, 01 Jan 0001 00:00:00 +0000

TensorRT

Mon, 01 Jan 0001 00:00:00 +0000

Guide to building and deploying TensorRT-LLM engines with Triton inference server

Text & Vision Fine-tuning

Mon, 01 Jan 0001 00:00:00 +0000

Fine-tune Mistral’s text and vision models with custom datasets in JSONL format for domain-specific or conversational improvements

Text and Chat Completions

Mon, 01 Jan 0001 00:00:00 +0000

Mistral models enable chat and text completions with customizable prompts, roles, and streaming options

Text Classification Using Embeddings

Mon, 01 Jan 0001 00:00:00 +0000

This page discusses the creation of a text classification model using word vector embeddings.

Text Embeddings

Mon, 01 Jan 0001 00:00:00 +0000

Generate and use text embeddings with Mistral AI’s API for NLP tasks like similarity, classification, and retrieval

Text generation - Cohere on Azure AI Foundry

Mon, 01 Jan 0001 00:00:00 +0000

A guide for performing text generation with Cohere’s Command models on Azure AI Foundry (API v2).

Text generation - quickstart

Mon, 01 Jan 0001 00:00:00 +0000

A quickstart guide for performing text generation with Cohere’s Command models (v2 API).

Text Generation Inference

Mon, 01 Jan 0001 00:00:00 +0000

TGI is a toolkit for deploying and serving LLMs with high-performance text generation features like quantization and OpenAI-like API support

Text Models

Mon, 01 Jan 0001 00:00:00 +0000

Query, track and manage inference for text models

Text2Vec

Mon, 01 Jan 0001 00:00:00 +0000

The MCP Registry

Mon, 01 Jan 0001 00:00:00 +0000

This is the starting point for understanding the MCP registry, the centralized discovery layer that lets host applications find and install MCP servers by name. The registry is what turns MCP from a protocol into an ecosystem, so grasping its design goals and trust model is important before publishing or consuming servers. Read this overview first, then move to authentication and package types for the operational details. If you are evaluating whether to publish your MCP server publicly or keep it private, this page covers the discoverability and governance tradeoffs.

The MCP Registry Moderation Policy

Mon, 01 Jan 0001 00:00:00 +0000

Together AI

Mon, 01 Jan 0001 00:00:00 +0000

Together AI

Mon, 01 Jan 0001 00:00:00 +0000

Track and evaluate Together AI’s open source LLMs using Weave’s OpenAI SDK compatibility for seamless integration with model calls, fine-tuning workflows, and hosted models.

Together's IAM Model

Mon, 01 Jan 0001 00:00:00 +0000

Tokenization

Mon, 01 Jan 0001 00:00:00 +0000

Learn about Mistral AI’s tokenization process, including subword tokenization, control tokens, and Python implementation for LLMs

Tool Calling

Mon, 01 Jan 0001 00:00:00 +0000

Connect models to external tools and APIs

Tool use & agents - Cohere on Azure AI Foundry

Mon, 01 Jan 0001 00:00:00 +0000

A guide for using tool use and building agents with Cohere’s Command models on Azure AI Foundry (API v2).

Tool use & agents - quickstart

Mon, 01 Jan 0001 00:00:00 +0000

A quickstart guide for using tool use and building agents with Cohere’s Command models (v2 API).

Tools

Mon, 01 Jan 0001 00:00:00 +0000

Tools are the most commonly used MCP primitive – they let the model invoke server-side functions with structured inputs and receive results. Focus on the JSON Schema-based input validation, which is how the model knows what arguments a tool accepts. A critical detail is that tool calls are model-initiated but require human approval in most host implementations, so design your tool descriptions to be clear enough that users understand what they are approving. Keep tool names concise and descriptions precise, as the model relies heavily on them for deciding when and how to call each tool.

Topic Modeling System for AI Papers

Mon, 01 Jan 0001 00:00:00 +0000

This page discusses how to create a topic-modeling system for papers focused on AI papers.

Trace grading

Mon, 01 Jan 0001 00:00:00 +0000

Use trace grading to create datasets, configure graders, and track evaluation runs for your models.

Trace Mistral applications

Mon, 01 Jan 0001 00:00:00 +0000

Track application versions with models

Mon, 01 Jan 0001 00:00:00 +0000

Track versions of your application with structured models that combine data and code.

Track CSV files with experiments

Mon, 01 Jan 0001 00:00:00 +0000

Importing and logging data into W&B

Track external files

Mon, 01 Jan 0001 00:00:00 +0000

Track files saved in an external bucket, HTTP file server, or an NFS share.

Track Jupyter notebooks

Mon, 01 Jan 0001 00:00:00 +0000

Use W&B with Jupyter to get interactive visualizations without leaving your notebook.

Training Shapes

Mon, 01 Jan 0001 00:00:00 +0000

Pre-configured GPU and model training profiles that simplify distributed training setup.

Transports

Mon, 01 Jan 0001 00:00:00 +0000

MCP defines two transport mechanisms: stdio (standard input/output) and HTTP with Server-Sent Events (SSE). For local development and CLI-based servers, stdio is simpler and the right default choice. HTTP+SSE is needed when the server runs remotely or must handle multiple clients. A common gotcha is that the Streamable HTTP transport replaced the older SSE-only transport in the 2025-11-25 spec revision, so be careful not to follow outdated examples that use the previous approach.

Triggers and Events Charter

Mon, 01 Jan 0001 00:00:00 +0000

Charter for the MCP Triggers and Events Working Group.

Tutorial: App versioning

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to use Weave Model to track and version your application and its parameters

Tutorial: Create sweep job from project

Mon, 01 Jan 0001 00:00:00 +0000

Tutorial on how to create sweep jobs from a pre-existing W&B project.

Tutorial: Create, track, and use a dataset artifact

Mon, 01 Jan 0001 00:00:00 +0000

Create, track, and use a dataset artifact with W&B.

Tutorial: Define, initialize, and run a sweep

Mon, 01 Jan 0001 00:00:00 +0000

Sweeps quickstart shows how to define, initialize, and run a sweep. There are four main steps

Tutorial: Log tables, visualize and query data

Mon, 01 Jan 0001 00:00:00 +0000

Explore how to use W&B Tables with this 5 minute Quickstart.

Tutorial: Use custom charts

Mon, 01 Jan 0001 00:00:00 +0000

Tutorial of using the custom charts feature in the W&B UI

UI Guide

Mon, 01 Jan 0001 00:00:00 +0000

Access W&B Inference models through the web interface

Understanding Authorization in MCP

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to implement secure authorization for MCP servers using OAuth 2.1 to protect sensitive resources and operations

Understanding MCP clients

Mon, 01 Jan 0001 00:00:00 +0000

Most developers will build servers rather than clients, but understanding the client side is critical for grasping the full protocol flow. The key insight is that the client acts as a gatekeeper: it decides which server capabilities to expose to the model and enforces security boundaries. Pay attention to how capability negotiation works during initialization, since both sides must agree on what features they support before any real work happens.

Understanding MCP servers

Mon, 01 Jan 0001 00:00:00 +0000

This page explains the three core primitives that MCP servers expose: Tools, Resources, and Prompts. Focus on understanding when to use each one – Tools let the model take actions, Resources provide read-only data the model can pull in, and Prompts are reusable templates for common interactions. A frequent mistake is implementing everything as a tool when a resource would be more appropriate and give the host application more control over how data is presented to the model.

Unit testing

Mon, 01 Jan 0001 00:00:00 +0000

Test agent logic without API calls using fake chat models and in-memory persistence.

Unit testing

Mon, 01 Jan 0001 00:00:00 +0000

Test agent logic without API calls using fake chat models and in-memory persistence.

Update an artifact

Mon, 01 Jan 0001 00:00:00 +0000

Update an existing Artifact inside and outside of a W&B Run.

Upgrading to GPT-5.4

Mon, 01 Jan 0001 00:00:00 +0000

Guidance for upgrading OpenAI API model strings and directly related prompts to GPT-5.4.

Upgrading to GPT-5.5

Mon, 01 Jan 0001 00:00:00 +0000

Guidance for upgrading OpenAI API model strings and directly related prompts to GPT-5.5.

Upload a Model

Mon, 01 Jan 0001 00:00:00 +0000

Run inference on your custom or fine-tuned models

Upload via REST API

Mon, 01 Jan 0001 00:00:00 +0000

Programmatically upload custom models using the Fireworks REST API

Usage Limits & Analytics

Mon, 01 Jan 0001 00:00:00 +0000

Understanding account tiers, rate limits, model access, and cost analytics on Together AI.

Usage Policy

Mon, 01 Jan 0001 00:00:00 +0000

Developers must outline and get approval for their use case to access the Cohere API, understanding the models and limitations. They should refer to model cards for detailed information and document potential harms of their application. Certain use cases, such as violence, hate speech, fraud, and privacy violations, are strictly prohibited.

Use an Assistant MCP server

Mon, 01 Jan 0001 00:00:00 +0000

Connect AI agents to Pinecone Assistant via Model Context Protocol.

Use environment variables for model providers

Mon, 01 Jan 0001 00:00:00 +0000

Use model after training

Mon, 01 Jan 0001 00:00:00 +0000

Use Serverless LoRA Inference

Mon, 01 Jan 0001 00:00:00 +0000

Bring your own custom LoRA for serving fine-tuned models on W&B Inference.

Use W&B Skills

Mon, 01 Jan 0001 00:00:00 +0000

Install W&B Skills to teach your coding agent how to train models, build agents, and analyze experiments using W&B’s AI development platform.

Use Weave with W&B Models

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to use use weave with w&b models with W&B Weave

Use Weave with W&B training runs

Mon, 01 Jan 0001 00:00:00 +0000

Integrate Weave traces with W&B training runs to gain deep visibility into model behavior during training, capturing function execution details and diagnostics alongside traditional ML metrics in customizable workspace dashboards.

Use your trained models

Mon, 01 Jan 0001 00:00:00 +0000

Make inference requests to the models you’ve trained

Using Cohere models via the OpenAI SDK

Mon, 01 Jan 0001 00:00:00 +0000

The document serves as a guide for Cohere’s Compatibility API, which allows developers to seamlessly use Cohere’s models using OpenAI’s SDK.

Using Cohere's Models to Work with Image Inputs

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how a Cohere large language model works with image inputs. It covers passing images with the API, limitations, and best practices.

Using GPT-5.2

Mon, 01 Jan 0001 00:00:00 +0000

Learn about how to use and migrate to GPT-5.2 and the GPT-5 model family, the latest models in the OpenAI API.

Using realtime models

Mon, 01 Jan 0001 00:00:00 +0000

Prompting strategies and usage patterns for OpenAI’s realtime voice and multimodal models via WebSocket connections.

Verifiers

Mon, 01 Jan 0001 00:00:00 +0000

Track and debug Verifiers RL environments and LLM agent training with Weave, capturing multi-round conversations, evaluation rollouts, and model performance metrics for comprehensive observability of reinforcement learning workflows.

Versioning

Mon, 01 Jan 0001 00:00:00 +0000

Versioning Published MCP Servers

Mon, 01 Jan 0001 00:00:00 +0000

Getting versioning right is essential for MCP server publishers because clients may pin to specific versions, and breaking changes without a major version bump will silently break downstream integrations. Pay close attention to how semantic versioning interacts with registry resolution rules and what happens when you publish a version that a client cannot upgrade to automatically. If you maintain servers consumed by multiple host applications, read this alongside the package types guide to understand how versioning constraints differ across package formats.

Vertex AI

Mon, 01 Jan 0001 00:00:00 +0000

Deploy and query Mistral AI models on Google Cloud Vertex AI as serverless endpoints

Vertex AI hosted

Mon, 01 Jan 0001 00:00:00 +0000

Video & Audio Inputs

Mon, 01 Jan 0001 00:00:00 +0000

Query multimodal models to process video and audio content directly

Video Generation with Wan 2.1

Mon, 01 Jan 0001 00:00:00 +0000

Deploy a multi-GPU video generation model on Together’s managed GPU infrastructure using Dedicated Containers.

View a specific run in a project

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to view a specific logged run and its properties using the W&B App or the LEET terminal UI.

View an automation's history

Mon, 01 Jan 0001 00:00:00 +0000

View experiments results

Mon, 01 Jan 0001 00:00:00 +0000

A playground for exploring run data with interactive visualizations

View runs in a project

Mon, 01 Jan 0001 00:00:00 +0000

Details about customizing how runs are displayed in your project’s runs table

Vision

Mon, 01 Jan 0001 00:00:00 +0000

Multimodal AI models analyze images and text for insights, supporting use cases like OCR, chart understanding, and receipt transcription

Vision fine-tuning

Mon, 01 Jan 0001 00:00:00 +0000

Fine-tune models for better image understanding.

Vision Inputs

Mon, 01 Jan 0001 00:00:00 +0000

Fine-tune vision-language models (VLMs) with the Training API using multimodal chat data containing images and text.

Vision LLMs

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to use the vision models supported by Together AI.

Vision Models

Mon, 01 Jan 0001 00:00:00 +0000

Query vision-language models to analyze images and visual content

Vision-Language Fine-tuning

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to fine-tune Vision-Language Models (VLMs) on image+text data using Together AI.

Visualize and analyze tables

Mon, 01 Jan 0001 00:00:00 +0000

Visualize and analyze W&B Tables.

Visualize CoreWeave infrastructure alerts

Mon, 01 Jan 0001 00:00:00 +0000

Visualize sweep results

Mon, 01 Jan 0001 00:00:00 +0000

Visualize the results of your W&B Sweeps with the W&B App UI.

vLLM

Mon, 01 Jan 0001 00:00:00 +0000

vLLM

Mon, 01 Jan 0001 00:00:00 +0000

vLLM is an open-source LLM inference engine optimized for deploying Mistral models on-premise

VoyageAI

Mon, 01 Jan 0001 00:00:00 +0000

W&B Mobile App (iOS)

Mon, 01 Jan 0001 00:00:00 +0000

Track training runs, view line plots, and explore your W&B Models projects from your iPhone or iPad.

W&B Models

Mon, 01 Jan 0001 00:00:00 +0000

W&B Quickstart

Mon, 01 Jan 0001 00:00:00 +0000

W&B Quickstart

Warm Start from Fine-Tuned Models

Mon, 01 Jan 0001 00:00:00 +0000

Continue training from a previously fine-tuned model with RFT

Websearch

Mon, 01 Jan 0001 00:00:00 +0000

Websearch enables models to browse the web for real-time, up-to-date information and access specific websites

What is the Model Context Protocol (MCP)?

Mon, 01 Jan 0001 00:00:00 +0000

This is the single best starting point for understanding MCP. Focus on the core value proposition: MCP standardizes how AI applications connect to external data and tools, replacing fragile one-off integrations with a shared protocol. Pay attention to the analogy with USB-C — MCP aims to be a universal connector between LLMs and the systems they need to interact with. The original page also covers building MCP Apps — interactive apps that run inside AI clients — via the MCP Apps overview. Read this before diving into architecture or specification pages.

Whats New Claude 4 6

Mon, 01 Jan 0001 00:00:00 +0000

Which model should I use?

Mon, 01 Jan 0001 00:00:00 +0000

Find the best open models for your use case or migrate from closed source models like Claude, GPT, and Gemini

Working and Interest Groups

Mon, 01 Jan 0001 00:00:00 +0000

Learn about the two forms of collaborative groups within the Model Context Protocol’s governance structure - Working Groups and Interest Groups.

Working with evals

Mon, 01 Jan 0001 00:00:00 +0000

Build, run, and iterate on evaluations to systematically test and improve AI model outputs — OpenAI’s practical guide to eval-driven development.

Workspace setup

Mon, 01 Jan 0001 00:00:00 +0000

Add required workspace secrets for models and tools used by Agent Builder.

Workspace setup

Mon, 01 Jan 0001 00:00:00 +0000

Add required workspace secrets for models and tools used by Fleet.

Workspaces

Mon, 01 Jan 0001 00:00:00 +0000

La Plateforme workspaces enable team collaboration, access control, and shared fine-tuned models.’ (99 characters)

xAI

Mon, 01 Jan 0001 00:00:00 +0000

XGBoost

Mon, 01 Jan 0001 00:00:00 +0000

Track your trees with W&B.

YOLOv5

Mon, 01 Jan 0001 00:00:00 +0000

YOLOX

Mon, 01 Jan 0001 00:00:00 +0000

How to integrate W&B with YOLOX.