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Cognitive Technology Team
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
OPPO Kernel Craftsman
OPPO Kernel Craftsman
Mar 22, 2024 · Artificial Intelligence

InternLM Model Fine-Tuning Tutorial with XTuner: Chat Format and Practical Implementation Guide

This tutorial walks through fine‑tuning Shanghai AI Lab’s open‑source InternLM models with XTuner, explaining chat‑format conventions, loading and inference (including multimodal InternLM‑XComposer), dataset preparation, configuration sections, DeepSpeed acceleration, and memory‑efficient QLoRA details for 7‑B‑parameter chat models.

Chat FormatDeepSpeedFine-tuning
0 likes · 22 min read
InternLM Model Fine-Tuning Tutorial with XTuner: Chat Format and Practical Implementation Guide
Architect
Architect
Jul 1, 2023 · Artificial Intelligence

Comprehensive Guide to Text Generation Decoding Strategies with HuggingFace Transformers

This tutorial explores various text generation decoding methods—including greedy search, beam search, top‑k/top‑p sampling, sample‑and‑rank, and group beam search—explaining their principles, providing detailed Python code examples, and comparing their use in modern large language models.

HuggingFacebeam searchdecoding strategies
0 likes · 59 min read
Comprehensive Guide to Text Generation Decoding Strategies with HuggingFace Transformers
Tencent Cloud Developer
Tencent Cloud Developer
Jun 1, 2023 · Artificial Intelligence

A Comprehensive Guide to Decoding Strategies for Text Generation with HuggingFace Transformers

This guide thoroughly explains the major decoding strategies for neural text generation in HuggingFace Transformers—including greedy, beam, diverse beam, sampling, top‑k, top‑p, sample‑and‑rank, beam sampling, and group beam search—detailing their principles, Python implementations with LogitsProcessor components, workflow diagrams, comparative analysis, and references to original research.

HuggingFaceNatural Language Processingbeam search
0 likes · 60 min read
A Comprehensive Guide to Decoding Strategies for Text Generation with HuggingFace Transformers