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NewBeeNLP

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Latest from NewBeeNLP

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NewBeeNLP
NewBeeNLP
Jul 31, 2024 · Artificial Intelligence

How Continual Pre‑Training Boosts Llama‑3’s Chinese and Scientific Reasoning

This report presents a continual pre‑training approach that significantly enhances Llama‑3 (8B)’s Chinese language proficiency and scientific reasoning by using a carefully mixed corpus of existing and synthetic data, detailing the bilingual adaptation and synthetic‑enhancement stages, data‑mixing and curriculum strategies, and demonstrating strong results across multilingual and scientific benchmarks without sacrificing original capabilities.

LLMLlama-3benchmarking
0 likes · 9 min read
How Continual Pre‑Training Boosts Llama‑3’s Chinese and Scientific Reasoning
NewBeeNLP
NewBeeNLP
Jul 31, 2024 · Artificial Intelligence

Training 7B–13B LLMs: Practical Tips, Hyperparameters, and Scaling Challenges

The article shares hands‑on experience training 7‑ and 13‑billion‑parameter language models, covering essential hyper‑parameters, hardware requirements, data quality considerations, open dataset resources, and the systemic difficulties that arise when scaling to trillion‑parameter models.

LLM traininghyperparameterslarge language models
0 likes · 8 min read
Training 7B–13B LLMs: Practical Tips, Hyperparameters, and Scaling Challenges
NewBeeNLP
NewBeeNLP
Jul 26, 2024 · Industry Insights

What the Leaked Llama 3.1 405B Reveals About Meta’s Newest LLM

A leaked 405‑billion‑parameter Llama 3.1 model shows mixed benchmark results—outperforming GPT‑4o on some tasks while lagging on others—along with massive hardware requirements, extensive training data, and new safety considerations that could reshape AI deployment.

Llama 3.1Meta
0 likes · 11 min read
What the Leaked Llama 3.1 405B Reveals About Meta’s Newest LLM
NewBeeNLP
NewBeeNLP
Jul 25, 2024 · Artificial Intelligence

Llama 3.1 Unveiled: How the New Open‑Source Giant Matches GPT‑4o and Claude 3.5

Meta has officially released Llama 3.1, a 405‑billion‑parameter open‑source model that matches or surpasses GPT‑4o and Claude 3.5 on over 150 benchmarks, expands context to 128 K tokens, supports eight languages, and is accompanied by a detailed 100‑page paper describing its data, training stack, architecture, quantization, safety measures, and ecosystem support.

AI safetyLlama 3.1Meta
0 likes · 15 min read
Llama 3.1 Unveiled: How the New Open‑Source Giant Matches GPT‑4o and Claude 3.5
NewBeeNLP
NewBeeNLP
Jul 24, 2024 · Industry Insights

From Black Iron to Silver: The Evolution of Large Model Infrastructure (2019‑2024)

The article traces the evolution of large‑model training and inference infrastructure from the early “black‑iron” era (2019‑2021) through the “golden” boom (2022‑2023) to the emerging “silver” phase (2024‑), highlighting key research breakthroughs, open‑source frameworks, hardware trends, market dynamics, and practical challenges for engineers entering the field.

AI infrastructureIndustry trendsInference
0 likes · 22 min read
From Black Iron to Silver: The Evolution of Large Model Infrastructure (2019‑2024)
NewBeeNLP
NewBeeNLP
Jul 22, 2024 · Artificial Intelligence

How Meta Scales User Modeling for Ads: Inside the SUM Framework

This article examines Meta's SUM (Scaling User Modeling) system, detailing its upstream‑downstream architecture, the SOAP online asynchronous serving platform, production optimizations, and extensive offline and online experiments that demonstrate significant gains in ad personalization performance.

MetaRecommendation Systemsdeep learning
0 likes · 19 min read
How Meta Scales User Modeling for Ads: Inside the SUM Framework
NewBeeNLP
NewBeeNLP
Jul 16, 2024 · Artificial Intelligence

Can Item Language Models Bridge LLMs and Collaborative Filtering for Conversational Recommendation?

This paper identifies three challenges of applying large language models to recommendation systems and proposes an Item Language Model that combines an item encoder with a frozen LLM, demonstrating through extensive experiments that language‑item alignment and interaction knowledge significantly improve conversational recommendation performance.

Q-Formercollaborative filteringconversational recommendation
0 likes · 10 min read
Can Item Language Models Bridge LLMs and Collaborative Filtering for Conversational Recommendation?
NewBeeNLP
NewBeeNLP
Jul 10, 2024 · Artificial Intelligence

Can Large Language Models Master Co‑Temporal Reasoning? Introducing COTEMPQA

This article presents the COTEMPQA benchmark for evaluating large language models on co‑temporal reasoning, details its four scenario types, construction pipeline, experimental results across models, error analysis, and proposes the MR‑COT strategy that leverages mathematical reasoning to significantly improve performance.

LLM evaluationMR-COTbenchmark dataset
0 likes · 11 min read
Can Large Language Models Master Co‑Temporal Reasoning? Introducing COTEMPQA
NewBeeNLP
NewBeeNLP
Jul 8, 2024 · Artificial Intelligence

How LLMs Transform Recommendation Systems: The LEARN Framework Explained

This article reviews the Kuaishou paper on adapting large language models for recommendation, detailing the LEARN framework's dual‑tower architecture, embedding generation, loss functions, and experimental results that address cold‑start and long‑tail challenges in modern recommender systems.

InfoNCELLMLong Tail
0 likes · 8 min read
How LLMs Transform Recommendation Systems: The LEARN Framework Explained