LLM fine-tuning

Sohu Tech Products

Mar 19, 2025 · Artificial Intelligence

Easy DataSet: An Open‑Source Tool for Building Domain‑Specific Datasets and Fine‑Tuning Large Language Models

The article introduces Easy DataSet, an open‑source tool that streamlines the creation of domain‑specific datasets by aggregating public data sources, chunking Markdown documents, generating and managing QA pairs with configurable LLM endpoints, and exporting them in common formats, while outlining its architecture and future roadmap.

AILLM fine-tuningPrompt Engineering

0 likes · 30 min read

Easy DataSet: An Open‑Source Tool for Building Domain‑Specific Datasets and Fine‑Tuning Large Language Models

Architect

Mar 9, 2025 · Artificial Intelligence

Experiments with Reinforcement Learning Fine‑Tuning of a 0.5B Qwen Model on the KK Dataset

The author reports a series of reinforcement‑learning‑based fine‑tuning experiments on a 0.5‑billion‑parameter Qwen‑0.5VB instruct model using the KK dataset, detailing reward design adjustments, curriculum‑style data scaling, observed convergence issues, and hypotheses about why small models fail to develop long reasoning chains.

LLM fine-tuningcurriculum learningreinforcement learning

0 likes · 11 min read

Experiments with Reinforcement Learning Fine‑Tuning of a 0.5B Qwen Model on the KK Dataset

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

DevOps

May 29, 2024 · Artificial Intelligence

End-to-End Task-Oriented Dialogue Agent Construction Using Monte Carlo Simulation and LLM Fine-Tuning

This article presents an end‑to‑end approach for building task‑oriented dialogue agents by simulating user behavior with Monte Carlo methods, generating training data via LLMs, and efficiently fine‑tuning multiple large language models using LLaMA Factory, demonstrating significant improvements in intent recognition, slot filling, and contextual understanding.

Data GenerationLLM fine-tuningMonte Carlo simulation

0 likes · 17 min read

End-to-End Task-Oriented Dialogue Agent Construction Using Monte Carlo Simulation and LLM Fine-Tuning

360 Smart Cloud

Apr 15, 2024 · Artificial Intelligence

Fine‑Tuning Qwen‑14B Large Language Model: A Complete Guide

This article provides a comprehensive tutorial on fine‑tuning the Qwen‑14B large language model, covering the motivation, fine‑tuning concepts, step‑by‑step workflow, required code, DeepSpeed training parameters, testing scripts, and deployment using FastChat and the 360AI platform.

AI Model DeploymentDeepSpeedFastChat

0 likes · 9 min read

Fine‑Tuning Qwen‑14B Large Language Model: A Complete Guide

DataFunTalk

Dec 29, 2023 · Artificial Intelligence

Enterprise Knowledge Assistant: Leveraging Vector Databases and Large Language Models

This article explores the emerging enterprise knowledge assistant paradigm in the era of large models, detailing traditional knowledge management challenges, solution architecture using vector databases and LLMs, core technologies such as ETL pipelines, reranking, secure fine‑tuning, and future prospects for intelligent enterprise applications.

LLM fine-tuningSemantic Searchenterprise AI

0 likes · 11 min read

Enterprise Knowledge Assistant: Leveraging Vector Databases and Large Language Models

DaTaobao Tech

Oct 25, 2023 · Artificial Intelligence

Prompt Engineering, LLM Supervised Fine‑Tuning, and Mobile Tmall AI Assistant Application

The article explains prompt engineering techniques, supervised fine‑tuning of large language models, and their practical deployment in the Mobile Tmall AI shopping assistant, detailing ChatGPT’s generation steps, Transformer architecture, prompt clarity, delimiters, role‑play, few‑shot and chain‑of‑thought prompting, SFT versus pre‑training, LoRA adapters, data collection, Qwen‑14B training configuration, SDK‑based inference, and comprehensive evaluation.

AI AssistantLLM fine-tuningModel Deployment

0 likes · 14 min read

Prompt Engineering, LLM Supervised Fine‑Tuning, and Mobile Tmall AI Assistant Application