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DataFunTalk

Jun 3, 2025 · Artificial Intelligence

Meta‑Capability Alignment: Psychologically Inspired Training to Endow Large Language Models with Stable Reasoning

Researchers from NUS, Tsinghua and Salesforce AI Research introduce a meta‑capability alignment framework that integrates deductive, inductive and abductive reasoning via a psychology‑based triple, automatically generates and validates training data, and demonstrates over 10% accuracy gains on math, coding and scientific benchmarks for 7B and 32B models.

Artificial IntelligenceMeta‑Capability Alignmentlarge language models

0 likes · 8 min read

Meta‑Capability Alignment: Psychologically Inspired Training to Endow Large Language Models with Stable Reasoning

Youzan Coder

May 12, 2025 · Artificial Intelligence

How Large Language Models Empower Business Development Engineers: Data Analysis, Model Training, and Rapid Prototyping

This article demonstrates how large language models can augment business development engineers by providing data insight, automating algorithm training, and enabling low‑cost rapid product prototyping, thereby transforming traditional backend‑focused roles into full‑stack, AI‑enhanced innovators.

AIPythonRapid Prototyping

0 likes · 10 min read

How Large Language Models Empower Business Development Engineers: Data Analysis, Model Training, and Rapid Prototyping

DataFunTalk

Apr 6, 2025 · Artificial Intelligence

Meta Unveils Llama 4: New Multimodal AI Models with Mixture‑of‑Experts Architecture and 10 Million‑Token Context

Meta announced the Llama 4 series—Scout, Maverick and Behemoth—featuring multimodal capabilities, Mixture‑of‑Experts design, up to 10 million‑token context windows, and state‑of‑the‑art performance on STEM, multilingual and image benchmarks, with models now downloadable from llama.com and Hugging Face.

Llama 4Mixture of Expertslarge language model

0 likes · 14 min read

Meta Unveils Llama 4: New Multimodal AI Models with Mixture‑of‑Experts Architecture and 10 Million‑Token Context

Cognitive Technology Team

Mar 6, 2025 · Artificial Intelligence

From Traditional Machine Learning to Deep Learning: A Comprehensive Guide to Algorithms, Feature Engineering, and Model Training

This article provides a step‑by‑step tutorial that walks readers through the fundamentals of traditional machine‑learning algorithms, feature‑engineering techniques, model training pipelines, evaluation metrics, and then advances to deep‑learning concepts such as MLPs, activation functions, transformers, and modern recommendation‑system models.

PythonRecommendation systemsTransformer

0 likes · 63 min read

From Traditional Machine Learning to Deep Learning: A Comprehensive Guide to Algorithms, Feature Engineering, and Model Training

Tencent Technical Engineering

Feb 26, 2025 · Artificial Intelligence

Engineers' Perspectives on DeepSeek: Technical Innovations and Implications

Thirteen engineers praise DeepSeek’s open‑source, reinforcement‑learning‑driven architecture—using FP8 storage and SFT‑free training—to deliver GPT‑4‑level reasoning at one‑twentieth the cost, enabling single‑GPU deployment, lowering barriers for academia and startups, and prompting notable market reactions that could democratize advanced AI.

AI cost reductionDeepSeekFP8

0 likes · 9 min read

Engineers' Perspectives on DeepSeek: Technical Innovations and Implications

Cognitive Technology Team

Feb 24, 2025 · Artificial Intelligence

Fine-Tuning Large Language Models with LoRA: A Step-by-Step Guide and Code Example

This article demonstrates the before-and-after effects of fine‑tuning a large language model, explains the concept with analogies, details hardware setup, dataset preparation, LoRA configuration, training arguments, and provides complete Python code for a pure‑framework fine‑tuning workflow.

HuggingFaceLLM fine-tuningLoRA

0 likes · 24 min read

Fine-Tuning Large Language Models with LoRA: A Step-by-Step Guide and Code Example

Architect

Feb 18, 2025 · Artificial Intelligence

DeepSeek‑R1: Training Innovations and Architecture for High‑Performance Reasoning LLMs

The article explains how DeepSeek‑R1 advances large language model reasoning by releasing a lightweight distilled version, sharing a complete training pipeline—including pre‑training, supervised fine‑tuning, and reinforcement learning—introducing long‑chain reasoning data, a transitional inference model, and a comprehensive RL optimization that together yield strong mathematical and logical capabilities.

AIDeepSeeklarge language model

0 likes · 10 min read

DeepSeek‑R1: Training Innovations and Architecture for High‑Performance Reasoning LLMs

JD Tech Talk

Feb 13, 2025 · Artificial Intelligence

DeepSeek R1: Concept Overview, Training Principles, and Practical Implementations

This article introduces the DeepSeek family of models, explains the concepts of online search and deep reasoning, details the two‑phase training pipeline with data augmentation and reinforcement learning, and showcases practical experiments and deployment examples for the R1 and distilled variants.

DeepSeekLLMR1

0 likes · 10 min read

DeepSeek R1: Concept Overview, Training Principles, and Practical Implementations

DataFunSummit

Feb 10, 2025 · Artificial Intelligence

Intelligent Decision-Making Large Model ORLM: Research, Training Challenges, Commercialization, and Future Directions

This article presents the ORLM intelligent decision‑making large model, detailing how real‑world decision problems are formalized and solved, the training difficulties and data synthesis methods, the transition from academic research to commercial platforms, and future technical improvement plans.

AIDecision Modelingdata synthesis

0 likes · 10 min read

Intelligent Decision-Making Large Model ORLM: Research, Training Challenges, Commercialization, and Future Directions

Top Architect

Feb 9, 2025 · Artificial Intelligence

DeepSeek‑R1: Training Pipeline, Reinforcement‑Learning Techniques, and Experimental Results

The article reviews DeepSeek‑R1’s training methodology—including cold‑start data collection, multi‑stage RL fine‑tuning, SFT data generation, and model distillation—highlights its performance comparable to OpenAI‑o1‑1217, and discusses key contributions, reward design, successful experiments, and failed attempts.

AI researchDeepSeekLLM

0 likes · 12 min read

DeepSeek‑R1: Training Pipeline, Reinforcement‑Learning Techniques, and Experimental Results

Architect

Feb 6, 2025 · Artificial Intelligence

DeepSeek‑R1: Reinforcement‑Learning‑Driven Long‑Chain Reasoning for Large Language Models

The article reviews DeepSeek‑R1, detailing its reinforcement‑learning‑based training pipeline that uses minimal supervised data, cold‑start fine‑tuning, multi‑stage RL, rejection‑sampling SFT, and distillation to achieve reasoning performance comparable to OpenAI‑o1‑1217, while also discussing successful contributions and failed experiments.

AI researchDeepSeek-R1LLM reasoning

0 likes · 11 min read

DeepSeek‑R1: Reinforcement‑Learning‑Driven Long‑Chain Reasoning for Large Language Models

DataFunSummit

Jan 25, 2025 · Artificial Intelligence

AI-Driven Next-Generation Sales: Project Overview, Core Technologies, System Deployment, and Future Outlook

This article explores how AI transforms next‑generation sales by detailing project background and goals, core technologies such as efficient sample generation, model training and evaluation, system deployment impact, practical case studies, challenges, solutions, and future directions across multiple industries.

AISales AutomationSample Generation

0 likes · 25 min read

AI-Driven Next-Generation Sales: Project Overview, Core Technologies, System Deployment, and Future Outlook

Kuaishou Tech

Jan 24, 2025 · Artificial Intelligence

KwaiCoder-23BA4-v1: An Efficient Large Code Generation Model via Pruning, Knowledge Distillation, and Granular Upcycling

KwaiCoder-23BA4-v1 is a 23B wide MoE code‑completion model that achieves state‑of‑the‑art performance on HumanEval, BigCodeBench and Fill‑in‑Middle benchmarks by using high‑quality data, a cost‑effective training pipeline that combines model pruning, knowledge distillation and fine‑grained merging, and extensive ablation studies.

AICode Generationbenchmark

0 likes · 10 min read

KwaiCoder-23BA4-v1: An Efficient Large Code Generation Model via Pruning, Knowledge Distillation, and Granular Upcycling

DataFunSummit

Jan 24, 2025 · Artificial Intelligence

Challenges and Debugging Strategies for FP8 Training of Large Models

The article explains the performance benefits of using FP8 for large‑model training, outlines three main categories of FP8‑related issues such as loss spikes, divergence, and downstream metric gaps, and introduces a dedicated FP8 debug tool with metrics like MSE, cosine similarity, underflow, and overflow to help diagnose and resolve these problems.

AIFP8Nvidia

0 likes · 9 min read

Challenges and Debugging Strategies for FP8 Training of Large Models

Test Development Learning Exchange

Nov 26, 2024 · Artificial Intelligence

Comprehensive Python Tutorial for Data Preprocessing, Feature Engineering, Model Training, Evaluation, and Deployment

This tutorial walks through consolidating the first ten days of learning by covering data preprocessing, feature engineering, model training with linear regression, decision tree, and random forest, model evaluation using cross‑validation, and finally saving and loading the best model, all illustrated with complete Python code examples.

Pythondata preprocessingfeature engineering

0 likes · 9 min read

Comprehensive Python Tutorial for Data Preprocessing, Feature Engineering, Model Training, Evaluation, and Deployment

DataFunTalk

Nov 25, 2024 · Artificial Intelligence

2024 AI Development Report Summary by Fei‑Fei Li’s Team

The 2024 AI Development Report by Fei‑Fei Li’s team highlights rapid progress in model capabilities, rising training costs, dominant contributions from the US, China and Europe, emerging reliability challenges, and the broad economic, medical, and educational impacts of artificial intelligence.

2024AIeconomic impact

0 likes · 12 min read

2024 AI Development Report Summary by Fei‑Fei Li’s Team

Architecture and Beyond

Nov 2, 2024 · Artificial Intelligence

Step-by-Step Guide to Training a LoRA Model with Flux1_dev on ComfyUI

This tutorial walks programmers through preparing a GPU cloud environment, installing ComfyUI, downloading Flux1_dev models, integrating a custom LoRA, labeling generated images, and finally training the LoRA using ai‑toolkit, providing detailed commands, configuration tips, and practical cost estimates.

AI image generationComfyUIFlux

0 likes · 12 min read

Step-by-Step Guide to Training a LoRA Model with Flux1_dev on ComfyUI

Test Development Learning Exchange

Oct 29, 2024 · Artificial Intelligence

Data Preprocessing and Modeling with Pandas and Scikit‑learn

This guide walks through using Pandas for data cleaning, feature engineering, and preparation, then demonstrates building, evaluating, and persisting a machine‑learning model with Scikit‑learn's pipeline and RandomForestClassifier in Python.

Pythondata preprocessingmachine learning

0 likes · 5 min read

Data Preprocessing and Modeling with Pandas and Scikit‑learn

DataFunSummit

Oct 23, 2024 · Artificial Intelligence

Data Compliance Risks and Mitigation Measures Across the Generative AI Model Lifecycle

The article examines data compliance challenges and legal risks during the training, application, and optimization stages of generative AI models, and offers concrete mitigation strategies such as respecting robots.txt, obtaining user consent, handling cross‑border data, and implementing robust security and governance measures.

AI compliancedata securitygenerative AI

0 likes · 17 min read

Data Compliance Risks and Mitigation Measures Across the Generative AI Model Lifecycle

DataFunSummit

Aug 12, 2024 · Artificial Intelligence

Design and Application of Xiaohongshu Heterogeneous Training and Inference Engine

This article presents a comprehensive overview of Xiaohongshu's heterogeneous training and inference engine, covering the challenges of model engineering, the design of elastic heterogeneous engines, future HPC training frameworks, AI compilation techniques, and a forward‑looking outlook on scalability and performance.

AIAI CompilationHPC

0 likes · 19 min read

Design and Application of Xiaohongshu Heterogeneous Training and Inference Engine