#model-performance
#model-performance

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Unsloth Tutorials Aim to Make it Easier to Compare and Fine-tune LLMs

Qwen3-Coder-480B-A35B delivers SOTA advancements in agentic coding and code tasks, matching or outperforming Claude Sonnet-4, GPT-4.1, and Kimi K2. The 480B model achieves a 61.8% on Aider Polygot and supports a 256K token context, extendable to 1M tokens.

Artificial intelligence

fromHackernoon

4 years ago

Mixture-of-Agents (MoA): Improving LLM Quality through Multi-Agent Collaboration | HackerNoon

The Mixture-of-Agents framework enhances large language model performance through collaboration among specialized models, achieving superior results without massive scaling.

#model-performance#model-performance

Unsloth Tutorials Aim to Make it Easier to Compare and Fine-tune LLMs

Mixture-of-Agents (MoA): Improving LLM Quality through Multi-Agent Collaboration | HackerNoon

Sam Altman addresses 'bumpy' GPT-5 rollout, bringing 4o back, and the 'chart crime' | TechCrunch

OpenAI launches o3 and o4-mini

Sam Altman addresses 'bumpy' GPT-5 rollout, bringing 4o back, and the 'chart crime' | TechCrunch

OpenAI launches o3 and o4-mini

The Link Between Concept Frequency and AI Performance, Seen Through Images and Words | HackerNoon

Two Indispensable Tools for Measuring the Quality of AI Systems

The Link Between Concept Frequency and AI Performance, Seen Through Images and Words | HackerNoon

Two Indispensable Tools for Measuring the Quality of AI Systems

How Concept Frequency Affects AI Image Accuracy | HackerNoon

How Dataset Diversity Impacts AI Model Performance | HackerNoon

QDyLoRA in Action: Method, Benchmarks, and Why It Outperforms QLoRA | HackerNoon

Contextualizing SUTRA: Advancements in Multilingual & Efficient LLMs | HackerNoon

AI Still Can't Explain a Joke-or a Metaphor-Like a Human Can | HackerNoon

Vector Institute aims to clear up confusion about AI model performance

AI Still Can't Explain a Joke-or a Metaphor-Like a Human Can | HackerNoon

Vector Institute aims to clear up confusion about AI model performance

Mistral AI Releases Magistral, Its First Reasoning-Focused Language Model

Meta's chief AI scientist says scaling AI won't make it smarter

Open AI's new models hallucinate more than the old ones

Reconstruction Evaluations Across Varying Amounts of Training Data: Mindeye2 | HackerNoon

Mistral AI Releases Magistral, Its First Reasoning-Focused Language Model

Meta's chief AI scientist says scaling AI won't make it smarter

Open AI's new models hallucinate more than the old ones

Reconstruction Evaluations Across Varying Amounts of Training Data: Mindeye2 | HackerNoon

DeepSeek may have used Google's Gemini to train its latest model | TechCrunch

What Makes Code LLMs Accurate? | HackerNoon

Do Smaller, Full-Precision Models Outperform Quantized Code Models? | HackerNoon

Why 4-Bit Quantization Is the Sweet Spot for Code LLMs | HackerNoon

Do Smaller, Full-Precision Models Outperform Quantized Code Models? | HackerNoon

Why 4-Bit Quantization Is the Sweet Spot for Code LLMs | HackerNoon

The V-Shaped Mystery of Inference Time in Low-Bit Code Models | HackerNoon

Fine-tuned GPT-3.5 Performance for Explanatory Feedback | HackerNoon

How LightCap Sees and Speaks: Mobile Magic in Just 188ms Per Image | HackerNoon

Windsurf Launches SWE-1 Family of Models for Software Engineering

Where Glitch Tokens Hide: Common Patterns in LLM Tokenizer Vocabularies | HackerNoon

ChatGPT: Everything you need to know about the AI chatbot

OpenAI's Hot New AI Has an Embarrassing Problem

#model-performance
#model-performance