LLM Adoption - Labellerr AI

LLM Adoption

A collection of 7 posts

Qwen3 Coder: The Agentic LLM-Coder Reshaping Software Development

Qwen3 Coder: Agentic LLM-Coder For Software Development

In 2025, open-source coding LLMs like Qwen3-Coder offer sophisticated multi-language support, agentic task handling, long context windows, and state-of-the-art code generation for local use—empowering developers to build, debug, translate, and optimize code securely on their own har

GLM-4.5 by Zhipu AI: Model for Coding, Reasoning, and Vision

GLM-4.5 delivers state-of-the-art open-source capabilities across language, code, and multimodal vision. Combining a 355B-parameter Mixture-of-Experts architecture, dual-mode reasoning, and native tool use, it sets new standards for coding, agentic, and multilingual tasks.

Evaluating LLMs

Opik Is Changing How You Evaluate LLMs — Find Out How

Opik by Comet automates LLM evaluation, detects errors and hallucination. It tracks decisions, flags mistakes, and removes manual testing.

Is Your Reasoning Model Any Better? Check with ARC AGI v2!

Is Your AI Smart Enough? Test It with ARC AGI v2!

ARC AGI V2 tests AI reasoning with abstract tasks that go beyond memorization. It evaluates how well models recognize patterns, solve problems, and generalize knowledge.

Evaluating Large Language Models: A Comprehensive Guide

Evaluating Large Language Models: A Comprehensive Guide

Evaluating large language models (LLMs) requires multidimensional strategies to assess coherence, accuracy, and fluency. Explore key benchmarks, metrics, and methods to ensure LLM reliability, transparency, and performance in real-world applications.

Llama 2 Vs GPT-3.5 Vs GPT-4: What, When & How To Chose

Llama 2 Vs GPT-3.5 Vs GPT-4: What, When & How To Chose

Llama 2, GPT-3.5, and GPT-4 offer unique AI capabilities: Llama 2 is cost-effective and adaptable, GPT-3.5 handles complex queries for larger businesses, and GPT-4 excels in high-level tasks needing minimal human input.

Challenges in LLM Adoption for Enterprises: Introducing LangKit for Secure LLM Adoption

Security Challenges in LLM Adoption for Enterprises And How To Solve Them

In 2023, generative AI gained significant attention, but enterprises are facing challenges in identifying suitable use cases for this technology. Large Language Models (LLMs) have inherent limitations, such as generating irrelevant or off-topic content and being vulnerable to prompt injection, making them a source of concern for businesses. Figure: LLMs