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All matches for “fine-tuning LLM”, grouped by content type.

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Experiment Design for A/B LLM - Advanced

In-depth guide for designing A/B tests specifically for large language models.

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Technical Workshop Lesson Plan

An organized lesson plan template for conducting technical workshops on LLMs and their applications.

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Experiment Design for A/B LLM

A structured approach to designing experiments for A/B testing in language models.

Dataset Card Draft for LLM Training (Advanced)

An advanced template for creating detailed dataset cards focusing on comprehensive metadata for LLM training datasets.

A/B Testing Experiment Design

A structured template to design A/B tests for LLM applications, ensuring consistency in experiment setup.

Dataset Card Draft for LLM Training

Specific guidelines for creating dataset cards for LLM training datasets.

Dataset Card Draft

A standardized template for documenting dataset characteristics, usage, and limitations for LLM training.

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Glossary

LLM evaluation

LLM evaluation measures whether a model or AI workflow is accurate, useful, safe, reliable, and cost-effective for a target task.

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Indic LLM

An Indic LLM is a language model optimized for Indian languages, scripts, romanized text, code-mixing, and India-specific cultural or domain context.

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fine-tuning

The process of adjusting a pre-trained model on a new, often smaller dataset to improve performance on a specific task.

Large language model

A large language model, or LLM, is a neural text model trained on large corpora to predict, generate, transform, and reason over language and code.

Tutorials

Observability: Traces for LLM + Tool Spans

Implementing observability practices to trace interactions between large language models (LLMs) and external tools. Prerequisites include knowledge of observability tools and LLM architectures.

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Metadata Filters and ACL-Aware Retrieval in Legal Document Management

This tutorial outlines the implementation of metadata filters and Access Control List (ACL)-aware retrieval systems in legal document management applications. Prerequisites include knowledge of legal data structures and basic programming skills.

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Enhancing Observability with Traces for LLM and Tool Spans in Data Pipelines

This tutorial focuses on enhancing observability in data pipelines that utilize large language models (LLMs) by implementing tracing for both LLM and tool spans. Prerequisites include familiarity with observability concepts and experience with LLMs.

Tools

LangSmith

LangSmith is LangChain's framework-agnostic platform for tracing, debugging, evaluating, prompt testing, and deployment workflows for AI agents and LLM applications.

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Ollama

Local model runtime for running and serving open LLMs on developer machines and private infrastructure, with simple pull/run workflows and API access.

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LlamaIndex

Data framework for LLM applications focused on ingestion pipelines, indexing, retrieval, and query orchestration over private and enterprise content sources.

LangChain

Application framework for orchestrating LLM workflows, tool calling, retrieval, and agents across multiple providers in Python and TypeScript ecosystems.

OpenAI Playground

Provider of widely used frontier model APIs for text, vision, and audio, with strong developer tooling and broad ecosystem adoption across production AI applications.

Models

Llama 3.1 405B Instruct

Meta’s largest open-weights instruct checkpoint in the Llama 3.1 family, aimed at strong reasoning and coding quality with a permissive license for research and customization. It is typically served on dedicated GPU clusters or via partners (cloud inference, on-prem) rather than a single vendor API.

Strong match

Claude 3.5 Sonnet

Anthropic’s balanced Sonnet-tier model tuned for long-context reasoning, careful instruction following, and strong performance on coding and analysis workloads. It is a common enterprise choice on the Anthropic API and on AWS Bedrock when teams need large context for RAG and document review.

Strong match

GPT-4o

OpenAI’s flagship multimodal chat model for production assistants: native image and audio inputs, strong tool and JSON-mode behavior, and low-latency routing on the Chat Completions API. Teams use it for vision-heavy workflows, agent loops with parallel tools, and structured extraction where schema adherence matters.

DeepSeek-V3

DeepSeek-V3 is a large-scale language model family noted for strong coding and math performance under open or research-friendly terms (verify the exact license for your deployment). Teams adopt it for cost-sensitive research, self-hosted inference, or comparison against frontier APIs.