model pruning | BestHub

DataFunSummit

Jun 10, 2025 · Artificial Intelligence

How Quwan’s Kaitian Model Tackles Emotional AI for Social Apps – Architecture, Training Tricks, and Safety

Quwan Technology presents its Kaitian social large model, designed for personalized, emotionally rich, multimodal AI interactions, detailing its scene‑specific goals, CPT+SFT+RLHF training pipeline, data desensitization, LoRA fine‑tuning, evaluation methods, pruning, latency trade‑offs, safety mechanisms, and future feedback loops.

AI safetyLoRARLHF

0 likes · 13 min read

How Quwan’s Kaitian Model Tackles Emotional AI for Social Apps – Architecture, Training Tricks, and Safety

JD Tech Talk

May 27, 2025 · Artificial Intelligence

Solving Real-World AI Challenges at JD Retail: Reward Model Ensembles, Query Expansion, and Model Pruning

This article recounts how JD Retail's young algorithm engineers tackled diverse AI problems—optimizing reward‑model ensembles for ad image generation, building large‑language‑model‑based query expansion, and pruning diffusion models with FFT and RDP—while sharing their technical approaches, code snippets, and growth reflections.

AIalgorithm engineeringlarge language models

0 likes · 14 min read

Solving Real-World AI Challenges at JD Retail: Reward Model Ensembles, Query Expansion, and Model Pruning

JD Retail Technology

May 7, 2025 · Artificial Intelligence

Solving Technical Challenges with Large AI Models at JD Retail: Reward Modeling, Query Expansion, and Model Pruning

JD Retail’s engineering team tackles hard AI problems by replacing a monolithic reward model with specialized small models for ad‑image generation, deploying an LLM‑driven query‑expansion pipeline that lifts conversion rates, and pruning text‑to‑image transformers using FFT and RDP to boost throughput 40% without loss, while building comprehensive evaluation tools and a semantic smart‑assistant.

AILarge ModelsReward Modeling

0 likes · 14 min read

Solving Technical Challenges with Large AI Models at JD Retail: Reward Modeling, Query Expansion, and Model Pruning

Kuaishou Tech

Oct 26, 2023 · Artificial Intelligence

SHARK: Efficient Embedding Compression for Large-Scale Recommendation Models

The paper introduces SHARK, a two‑component framework that uses a fast Taylor‑expanded permutation method to prune embedding tables and a frequency‑aware quantization scheme to apply mixed‑precision to embeddings, achieving up to 70% memory reduction and 30% QPS improvement in industrial short‑video and e‑commerce recommendation systems.

efficiencyembedding compressionlarge-scale AI

0 likes · 8 min read

SHARK: Efficient Embedding Compression for Large-Scale Recommendation Models

DataFunSummit

May 29, 2023 · Artificial Intelligence

Neuron‑level Shared Multi‑task Learning for Joint CTR and CVR Prediction

This article introduces a neuron‑level shared multi‑task learning framework that jointly estimates click‑through rate (CTR) and conversion rate (CVR), discusses the background and advantages of multi‑task learning, reviews classic shared‑bottom models, describes the proposed pruning‑based architecture, and presents experimental results demonstrating its effectiveness in large‑scale recommendation systems.

CVRRecommendation systemsctr

0 likes · 11 min read

Neuron‑level Shared Multi‑task Learning for Joint CTR and CVR Prediction